research in agriculture livestock and fisheries

Advancing the Science of Climate Change (2010)

Chapter: 10 agriculture, fisheries, and food production, chapter ten agriculture, fisheries, and food production.

M eeting the food needs of a still-growing and more affluent global population—as well as the nearly one billion people who already go without adequate food—presents a key challenge for economic and human security (see Chapter 16 ). Many analysts estimate that food production will need to nearly double over the coming several decades (Borlaug, 2007; FAO, 2009). Recent trends of using food crops for fuel (e.g., corn ethanol) or displacing food crops with fuel crops, along with potential opportunities for reforesting land for carbon credits, may amplify the food security challenge by increasing competition for arable land (Fargione et al., 2008). Climate change increases the complexity of meeting these food needs because of its multiple impacts on agricultural crops, livestock, and fisheries. The potential ability of agricultural and fishery systems to limit climate change adds yet another dimension to be considered.

Questions that farmers, fishers, and other decision makers are asking or will be asking about agriculture, fisheries, and food production in the context of climate change include the following:

How will climate change affect yields?

How will climate change affect weeds and pests, and will I need more pesticides or different technology to maintain or increase yields?

Will enough water be available for my crops? Will the risk of flooding or drought increase?

Should I change to more heat-resistant or slower-growing crop varieties?

What new market opportunities should I take advantage of? How will competitors in other regions be affected?

What adjustments do I need to make to guarantee the sustainability of the fisheries under my management?

How will climate change affect my catch? Will I need new equipment and technology? Will regulations change?

How will climate change affect the availability of food in domestic and international markets? Will food become more expensive? Will food security increase or decrease?

How can changes in agricultural production and practices contribute to reduc-

tions in greenhouse gas emissions or dampen regional-scale impacts related to climate change?

The scientific knowledge summarized in this chapter illustrates how agriculture will be influenced by climate change, and it explores the less well understood impacts of climate change on fisheries. The chapter also indicates how agricultural management may provide opportunities to reduce net human greenhouse gas (GHG) emissions, and it offers insight into the science needed for adaptation in agriculture systems as well as food security issues. Finally, the chapter provides examples of a broad range of research that is needed to understand the impacts of climate change on food production systems and to develop strategies that assist in both limiting the magnitude of climate change through management practices and reducing vulnerability and increasing adaptive capacity in regions and populations in the United States and other parts of the world.

CROP PRODUCTION

Crop production will be influenced in multiple ways by climate change itself, as well as by our efforts to limit the magnitude of climate change and adapt to it. Over the past two decades, numerous experimental studies have been carried out on crop responses to increases in average temperature and atmospheric CO 2 concentrations (often referred to as carbon fertilization), and mathematical models depicting those relationships (singly or in combination) have been developed for individual crops. Fewer experiments and models have evaluated plant responses to climate-related increases in air pollutants such as ozone, or to changes in water or nutrient availability in combination with CO 2 and temperature changes. A recently published report of the U.S. Climate Change Science Program (CCSP, 2008e) summarized the results from experimental and modeling analyses for the United States. Results of experimental studies, for example, indicate that many crop plants, including wheat and soybeans, respond to elevated CO 2 with increased growth and seed yield, although not uniformly so. Likewise, elevated CO 2 also reduces the conductance of CO 2 and water vapor through pores in the leaves of some plants, with resulting improvements in water use efficiency and, potentially, improved growth under drought conditions (Leakey et al., 2009). On the other hand, studies carried out in the field under “free air CO 2 enrichment” environments indicate that growth response is often smaller than expected based on more controlled studies (e.g., Leakey et al., 2009; Long et al., 2006). The response of crop plants to carbon fertilization in field environments hence remains an important area of research (see Research Needs section at the end of the chapter).

Some heat-loving crop plants such as melons, sweet potatoes, and okra also respond positively to increasing temperatures and longer growing seasons; but many other crops, including grains and soybeans, are negatively affected, both in vegetative growth and seed production, by even small increases in temperature ( Figure 10.1 ). Many important grain crops tend to have lower yields when summer temperatures increase, primarily because heat accelerates the plant’s developmental cycle and reduces the duration of the grain-filling period (CCSP, 2008b; Rosenzweig and Hillel, 1998). In some crop plants, pollination, kernel set, and seed size, among other variables, are harmed by extreme heat (CCSP, 2008b; Wolfe et al., 2008). Studies also indicate that some crops such as fruit and nut trees are sensitive to changes in seasonality, reduced cold periods, and heat waves (Baldocchi and Wong, 2008; CCSP, 2008e; Luedeling et al., 2009).

Most assessments conclude that climate change will increase productivity of some crops in some regions, especially northern regions, while reducing production in others (CCSP, 2008b; Reilly et al., 2003), an expected result given the range of projected climate changes and diversity of food crops around the world. The Intergovernmental Panel on Climate Change (IPCC) suggests, with medium confidence, that moderate warming (1.8°F to 5.4°F [1°C to 3°C]) and associated increases in CO 2 and changes in precipitation would benefit crop and pasture lands in middle to high latitudes but decrease yields in seasonally dry and low-latitude areas (Easterling et al., 2007). This response to intermediate temperature increases would generate a situation of midlatitude “winners” in developed countries and low-latitude “losers” in developing coun-

FIGURE 10.1 Growth rates (green) and reproductive response (purple) versus temperature for corn (left) and soybean (right). The curves show that there is a temperature range (colored bars) within which the plants can optimally grow and reproduce, and that growth and reproduction are less efficient at temperatures above this range. The curves also show that, above a certain temperature, the plants cannot reproduce. SOURCE: USGCRP (2009a).

tries, thus magnifying rather than reducing existing inequities in food availability and security. The IPCC also concludes with medium to low confidence that, on the whole, global food production is likely to decrease with increases in average temperatures above 5.4°F (3°C).

Regional assessments of agricultural impacts in the United States (e.g., CCSP, 2008b, and references therein) suggest that over the next 30 years, the benefits of elevated CO 2 will mostly offset the negative effects of increasing temperature (see below for limits in modeling conducted to date). In northern regions of the country, many crops may respond positively to increases in temperature and atmospheric CO 2 concentrations. In the Midwest corn belt and more southern areas of the Great Plains, positive crop responses to elevated CO 2 may be offset by negative responses to increasing temperatures; rice, sorghum, and bean crops in the South would see negative growth impacts (CCSP, 2008b). In California, where half the nation’s fruit and vegetable crops are grown, climate change is projected to decrease yields of almonds, walnuts, avocados, and table grapes by up to 40 percent by 2050 (Lobell et al., 2007). As temperatures continue to rise, crops will increasingly experience temperatures above the optimum for growth and reproduction. Adaptation through altered crop types, planting dates, and other management options is expected to help the agricultural sector, especially in the developed world (Burke et al., 2009; Darwin et al., 1995). However, regional assessments for other areas of the world consistently conclude that climate change presents a serious risk to critical staple crops in sub-Saharan Africa, where adaptive capacity is expected to be less than in the industrialized world (Jones and Thornton, 2003; Parry et al., 2004). Parts of the world where agriculture depends on water resources from glacial melt, including the Andean highlands, the Ganges Plain, and portions of East Africa, are also at risk due to the worldwide reduction in snowpack and the retreat of glaciers (Bradley et al., 2006; Kehrwald et al., 2008; also see Chapter 8 ).

While models of crop responses to climate change have generally incorporated shifts in average temperature, length of growing season, and CO 2 fertilization, either singly or in combination, most have excluded expected changes in other factors that also have dramatic impacts on crop yields. These critical factors include changes in extreme events (such as heat waves, intense rainfall, or drought), pests and disease, and water supplies and energy use (for irrigation). Extreme events such as heavy downpours are already increasing in frequency and are projected to continue to increase (CCSP, 2008b; Rosenzweig et al., 2001). Intense rainfalls can delay planting, increase root diseases, damage fruit, and cause flooding and erosion, all of which reduce crop productivity. Drought frequency and intensity are likely (Christensen et al., 2007) to increase in several regions that already experience water stress, especially in developing

countries where investments have focused on disaster recovery more than adaptive capacity (e.g., Mirza, 2003).

Changes in water quantity and quality due to climate change are also expected to affect food availability, stability, access, and utilization. This will increase the vulnerability of many farmers and decrease food security, especially in the arid and semiarid tropics and in the large Asian and African deltas (Bates and Kundzewicz, 2008). As noted in Chapter 8 , freshwater demand globally will grow in coming decades, primarily due to population growth, increasing affluence, and the need for increased production of food and energy. Climate change is exacerbating these issues, and model simulations under various scenarios indicate that many regions face water resource challenges, especially in regions that depend on rainfall or irrigation from snowmelt (Hayhoe et al., 2007; Kapnick and Hall, 2009; Maurer and Duffy, 2005). As a result, many regions face critical decisions about modifying infrastructure and pricing policies as climate change progresses.

Many weeds, plant diseases, and insect pests benefit from warming (and from elevated CO 2 , in the case of most weed plants), sometimes more than crops; as temperatures continue to rise, many weeds, diseases, and pests will also expand their ranges (CCSP, 2008b; Garrett et al., 2006; Gregory et al., 2009; Lake and Wade, 2009; McDonald et al., 2009). In addition, under higher CO 2 concentrations, some herbicides appear to be less effective (CCSP, 2008b; Ziska, 2000; Ziska et al., 1999). In the United States, aggressive weeds such as kudzu, which has already invaded 2.5 million acres of the southeast, is expected to expand its range into agricultural areas to the north (Frumhoff, 2007). Worldwide, animal diseases and pests are already exhibiting range extensions from low to middle latitudes due to warming (CCSP, 2008b; Diffenbaugh et al., 2008). While these and other changes are expected to have negative impacts on crops, their impact on food production at regional or national scales has not been thoroughly evaluated.

Similar to crop production, commercial forestry will be affected by many aspects of climate change, including CO 2 fertilization, changes in length of growing season, changing precipitation patterns, and pests and diseases. Models project that global timber production could increase through a poleward shift in the locations where important forest species are grown, largely as a result of longer growing seasons. Enhanced growth due to carbon fertilization is also possible (Norby et al., 2005). However, experimental results and models typically do not account for limiting factors such as pests, weeds, nutrient availability, and drought; these limiting factors could potentially offset or even dominate the effects of longer growing seasons and carbon fertilization (Angert et al., 2005; Kirllenko and Sedjo, 2007; Norby et al., 2005).

LIVESTOCK PRODUCTION

Livestock respond to climate change directly through heat and humidity stresses, and they are also affected indirectly by changes in forage quantity and quality, water availability, and disease. Because heat stress reduces milk production, weight gain, and reproduction in livestock, production of pork, beef, and milk is projected to decline with warming temperatures, especially those above 5.4°F (3°C; Backlund et al., 2008) ( Figure 10.2 ). In addition, livestock losses due to heat waves are expected to increase, with the extreme heat exacerbated by rising minimum nighttime temperatures as well as increasing difficulties in providing adequate water (CCSP, 2008b).

Increasing temperatures may enhance production of forage in pastures and rangelands, except in already hot and dry locations. Longer growing seasons may also extend overall forage production, as long as precipitation and soil moisture are sufficient; however, uncertainty in climate model precipitation projections makes this difficult to determine. Although CO 2 enrichment stimulates production on many rangelands and pastures, it also reduces forage quality, shifts the dominant grass species toward those with lower food quality, and increases the prevalence of nonforage weeds (CCSP, 2008b; Eakin and Conley, 2002). In northern Sonora, Mexico, for example, buffelgrass, which was imported from Africa and improved in the United States, is increasingly planted as livestock pasture in arid conditions. However, the grass has become an

FIGURE 10.2 Percent change in milk yield from 20th-century (1850 to 1985) climate conditions to projected 2040 climate conditions made using two different models of future climate (bold versus italicized numbers) in different regions of the United States. The bold values are associated with the model that exhibits more rapid warming. SOURCE: CCSP (2008e).

aggressive invader, spreading across the Sonoran Desert landscape and into Arizona and overrunning important national parks and reserves (Arriaga et al., 2004). Overall, changes in forage are expected to lead to an overall decline in livestock productivity.

FISHERIES AND AQUACULTURE PRODUCTION

Over one billion people around the world rely on seafood as their primary source of protein, and roughly three billion people obtain at least 15 percent of their total protein intake from seafood (FAO, 2009). Global demand for seafood is growing at a rapid rate, fueled by increases in human population, affluence, and dietary shifts (York and Gossard, 2004). While demand for seafood is increasing, the catch of wild seafood has been declining slightly for 20 years (Watson and Pauly, 2001). Meeting the growth in demand has only been possible by rapid growth in marine aquaculture. The United States consumes nearly five billion pounds of seafood a year, ranking it third globally behind China and Japan. This large consumption, however, comes primarily from fish caught outside the nation’s boundary waters. Nearly 85 percent of U.S. consumption is imported, and that fraction is increasing (Becker, 2010). Therefore, consumption of food from the sea links the United States to nearly all the world’s ocean ecosystems.

Marine Fisheries

The impacts of climate change on marine-based food systems are far less well known than impacts on agriculture, but there is rapidly growing evidence that they could be severe (see Chapter 9 ). This is especially problematic given that a sizeable fraction of the world’s fisheries are already overexploited (Worm et al., 2009) and many are also subject to pollution from land or under stress from the decline of critical habitats like coral reefs and wetlands (Halpern et al., 2008; Sherman et al., 2009).

Year-to-year climate variability has long been known to cause large fluctuations in fish stocks, both directly and indirectly (McGowan et al., 1998; Stenseth et al., 2002), and this has always been a challenge for effective fisheries management (Walters and Parma, 1996). Similar sensitivity to longer time-scale variations in climate has been documented in a wide range of fish species from around the globe (Chavez et al., 2003; Steele, 1998), and this portends major changes in fish populations under future climate change scenarios. Successful management of fisheries will require an improved ability to forecast population fluctuations driven by climate change; this in turn demands significant new investments in research, including research on various management options (e.g., Mora et al., 2009). Fundamental shifts in management prac-

tices may be needed. For example, restoration planning for depleted Chinook salmon populations in the Pacific Northwest needs to account for the spatial shift in salmon habitat (Battin et al., 2007). An added complexity is that, because most of the fish catch comes from open oceans under international jurisdiction, any management regime will need to be negotiated and accepted by multiple nations to be effective.

Fished species tend to be relatively mobile, either as adults or young (larvae drifting in the plankton). As a result, their distributions can shift rapidly compared to those of land animals. In recent decades, geographical shifts toward the poles of tens to hundreds of kilometers have been documented for a wide range of marine species in different areas (Grebmeier et al., 2006; Lima et al., 2006; Mueter and Litzow, 2008; Sagarin et al., 1999; Zacherl et al., 2003). Model projections for anticipated changes by 2050 suggest a potentially dramatic rearrangement of marine life (Cheung et al., 2009). Although such projections are based upon relatively simple models and should be treated as hypotheses, they suggest that displacements of species ranges may be sufficiently large that the fish species harvested from any given port today may change dramatically in coming decades. Fishers in many Alaskan ports are already facing much longer commutes as distributions of target species have shifted (CCSP, 2009b).

Such projected shifts in fisheries distributions are likely to be most pronounced for U.S. fisheries in the North Pacific and North Atlantic, where temperature increases are likely to be greatest and will be coupled to major habitat changes driven by reduced sea ice (CCSP, 2009b). Abrupt warming in the late 1970s, which was associated with a regime shift in the Pacific Decadal Oscillation, greatly altered the marine ecosystem composition in the Gulf of Alaska (Anderson and Piatt, 1999). Rapid reductions in ice-dominated regions of the Bering Sea will very likely expand the habitat for subarctic piscivores such as arrowtooth flounder, cod, and pollock. Because there are presently only fisheries for cod and pollock, arrowtooth flounder may experience significant population increases with broad potential consequences to the ecosystem (CCSP, 2009b).

The effects of ocean acidification from increased absorption of CO 2 by the sea (see Chapters 6 and 9 ) may be even more important for some fisheries than other aspects of climate change, although the overall impact of ocean acidification remains uncertain (Fabry et al., 2008; Guinotte and Fabry, 2008). Many fished species (e.g., invertebrates such as oysters, clams, scallops, and sea urchins) produce shells as adults or larvae, and the production of shells could be compromised by increased acidification (Fabry et al., 2008; Gazeau et al., 2007; Hofmann et al . , 2008). Many other fished species rely on shelled plankton, such as pteropods and foraminifera, as their primary food source. Projected declines in these plankton species could have catastrophic impacts

on fished species higher in the food chain. Finally, acidification can disrupt a variety of physiological processes beyond the production of shells. Hence, the potential impacts of acidification—especially in combination with other climate changes on marine fish-eries—is potentially enormous, but the details remain highly uncertain (NRC, 2010f).

Aquaculture and Freshwater Fisheries

Today, approximately a third of seafood is grown in aquaculture, and that number rises to half if seafood raised for animal feed is included. As the fastest growing source of animal protein on the planet, aquaculture is widely touted as critical for meeting growing demands for food. Although aquaculture avoids some of the climate impacts associated with wild fish harvesting, others (e.g., ocean acidification) are equally challenging. Indeed, the current predominance of aquaculture facilities in estuaries and bays may exacerbate some of the impacts of ocean acidification (Miller et al., 2009). In addition, since different forms of aquaculture may require a variety of other natural resources such as water, feed, and energy to produce seafood, there may be much broader indirect impacts of climate change on this rapidly growing industry.

Freshwater fisheries face most of the same challenges from climate change as those in saltwater, as well as some that are unique. Forecasting the consequences of warming on fish population dynamics is complicated, because details of future climate at relatively small geographic scales (e.g., seasonal and daily variation, regional variation across watersheds) are critical to anticipating fish population responses (Littell et al., 2009). Yet, as noted in Chapter 6 , regional and local aspects of climate change are the hardest to project. Expected effects include elevated temperatures, reduced dissolved oxygen (Kalff, 2002), increased stratification of lakes (Gaedke et al., 1998; Kalff, 2002), and elevated pollutant toxicity (Ficke et al., 2007). Although the consequences of some of these changes are predictable when taken one at a time, the complex nature of interactions between their effects makes forecasting change for even a single species in a single region daunting (Littell et al., 2009). In addition to altering these physical and chemical characteristics of freshwater, climate change will also alter the quantity, timing, and variability of water flows (Mauget, 2003; Ye et al., 2003; Chapter 8 ). Climate-driven alterations of the flow regime will add to the decades or even centuries of alterations of stream and river flows through other human activities (e.g., urbanization, water withdrawals, dams; Poff et al., 2007). Finally, changes in lake levels that will result from changed patterns of precipitation, runoff, groundwater flows, and evaporation could adversely affect spawning grounds for some species, depending on bathymetry. While the full ramifications of these changes for freshwater fish require further analysis, there is evidence that coldwater fish such as salmon and trout will be especially

sensitive to them. For example, some projections suggest that half of the wild trout population of the Appalachians will be lost; in other areas of the nation, trout losses could range as high as 90 percent (Williams et al., 2007).

Globally, precipitation is expected to increase overall, and more of it is expected to occur in extreme events and as rain rather than snow, but anticipated regional changes in precipitation vary greatly and are highly uncertain (see Chapter 8 ). As a result, major alterations of stream and lake ecosystems are forecast in coming decades, but the details remain highly uncertain (Ficke et al., 2007). Although freshwater fish and invertebrates are typically as mobile as their marine counterparts, their ability to shift their range in response to climate change may be greatly compromised by the challenges of moving between watersheds. In contrast to the rapid changes in species ranges in the sea (Perry et al., 2005), freshwater fish and invertebrates may be much more constrained in their poleward range shifts in response to climate change, especially in east-west stream systems (Allan et al., 2005; McDowall, 1992).

In the United States, per capita consumption of fish and shellfish from the sea and estuaries is more than 15 times higher than consumption of freshwater fish (EPA, 2002); nevertheless, freshwater fish are important as recreation and as food for some U.S. populations. Globally, however, freshwater and diadromous fish (fish that migrate between fresh- and saltwater) account for about a quarter of total fish and shellfish consumption (Laurenti, 2007) and in many locations serve as the predominant source of protein (Bayley, 1981; van Zalinge et al., 2000). Given the large uncertainty in how climate change impacts on freshwater ecosystems will affect the fisheries they support, this important source of food and recreation is at considerable risk.

SCIENCE TO SUPPORT LIMITING CLIMATE CHANGE BY MODIFYING AGRICULTURAL AND FISHERY SYSTEMS

Food production systems are not only affected by climate change, but also contribute to it. Agricultural activities release significant amounts of CO 2 , methane (CH 4 ), and nitrous oxide (N 2 O) to the atmosphere (Cole et al., 1997; Paustian et al., 2004; Smith et al., 2007). CO 2 is released largely from decomposition of soil organic matter by microorganisms or burning of live and dead plant materials (Janzen, 2004; Smith, 2004); decomposition is enhanced by vegetation removal and tillage of soils. CH 4 is produced when decomposition occurs in oxygen-deprived conditions, such as wetlands and flooded rice systems, and from digestion by many kinds of livestock (Matson et al., 1998; Mosier et al., 1998). N 2 O is generated by microbial processes in soils and manures, and the flux of N 2 O into the atmosphere is typically enhanced by fertilizer use,

especially when applied in excess of plant needs (Robertson and Vitousek, 2009; Smith and Conen, 2004). The 2007 IPCC assessment concluded, with medium certainty, that agriculture accounts for about 10 to 12 percent of total global human-caused emissions of GHGs, including 60 percent of N 2 O and about 50 percent of CH 4 (Smith et al., 2007). The Environmental Protection Agency (EPA) estimates that about 32 percent of CH 4 emissions and 67 percent of N 2 O emissions in the United States are associated with agricultural activities (EPA, 2009b).

Typically, the projected future of global agriculture is based on intensification—increasing the output per unit area or time—which is typically achieved by increasing or improving inputs such as fertilizer, water, pesticides, and crop varieties, and thereby potentially reducing agricultural demands on other lands (e.g., Borlaug, 2007). Given this projected intensification, global N 2 O emissions are predicted to increase by about 50 percent by 2020 (relative to 1990) due to increasing use of fertilizers in agricultural systems (EPA, 2006; Mosier and Kroeze, 2000). If CH 4 emissions grow in direct proportion to increases in livestock numbers, then global livestock-related CH 4 production is expected to increase by 60 percent up to 2030 (Bruinsma, 2003); in the United States, the EPA (2006) forecasts that livestock-related CH 4 emissions will increase by 21 percent between 2005 and 2020. Projected changes in CH 4 emissions from rice production vary but are generally smaller than those associated with livestock (Bruinsma, 2003; EPA, 2006).

The active management of agricultural systems offers possibilities for limiting these fluxes and offsetting other GHG emissions. Many of these opportunities use current technologies and can be implemented immediately, permitting a reduction in emissions per unit of food (or protein) produced, and perhaps also a reduction in emissions per capita of food consumption. For example, changes in feeds and feeding practices can reduce CH 4 emissions from livestock, and using biogas digesters for manure management can substantially reduce CH 4 and N 2 O emissions while producing energy. Changes in management of fertilizers, and the development of new fertilizer application technologies that more closely match crop demand—sometimes called precision or smart farming—can also reduce N 2 O fluxes. It may also be possible to develop and adopt new rice cultivars that emit less CH 4 or otherwise manage the soil-root microbial ecosystem that drives emissions (Wang et al., 1997). Alternatively, organic agriculture or its fusion into other crop practices may reduce emissions and other environmental problems. To date, however, there has been little research on the willingness of farmers and the agricultural sector in general to adopt practices that would reduce emissions, or on the kinds of education, incentives, and institutions that would promote their use.

Beyond limiting the trace gases emitted in agricultural practice, there are opportunities for offsetting GHG emissions more broadly by managing agricultural landscapes to absorb and store carbon in soils and vegetation (Scherr and Sthapit, 2009). For example, minimizing soil tillage yields multiple benefits by increasing soil carbon storage, improving and maintaining soil structure and moisture, and reducing the need for inorganic fertilizers, as well as reducing labor, mechanization, and energy costs. Such practices may also have beneficial effects on biodiversity and other ecosystem services provided by surrounding lands and can be made economically attractive to farmers (Robertson and Swinton, 2005; Swinton et al., 2006). Incorporating biochar (charcoal from fast-growing trees or other biomass that is burned in a low-oxygen environment) has also been proposed as a potentially effective way of taking carbon out of the atmosphere; the resulting biochar can be added to soils for storage and improvement of soil quality (Lehmann and Joseph, 2009), although there has been some debate about the longevity of the carbon storage (Lehmann and Sohi, 2008; Wardle et al., 2008). Shifting agricultural production systems to perennial instead of annual crops, or intercropping annuals with perennial plants such as trees, shrubs, and palms, could also store carbon while producing food and fiber. Biofuel systems that depend on perennial species rather than food crops could be an integral part of such a system. Research is needed to develop these options and to test their efficacy. Most important, a landscape approach would be required in order to plan for carbon storage in conjunction with food and fiber production, conservation, and other land uses and the ecosystem services they provide.

Land clearing and deforestation have been major contributors to GHG emissions over the past several centuries, although as fossil fuel use has grown, land use contributions have become proportionally less important. Still, tropical deforestation alone accounted for about 20 percent of the carbon released to the atmosphere from human activities from 2000 to 2005 (Gullison et al., 2007) and 17 percent of all long-lived GHGs in 2004 (Barker et al., 2007). Reducing deforestation and restoring vegetation in degraded areas could thus both limit climate change and provide linked ecosystem and social benefits (see Chapter 9 ). It is not yet clear, however, how such programs would interact with other forces operating on agriculture to affect overall land uses and emissions. Finally, as with all proposed emissions-limiting land-management approaches, it is critical that attention be paid to consequences for all GHGs, not just a single target gas (Robertson et al., 2000), and to all aspects of the climate system, including reflectivity of the land surface (Gibbard et al., 2005; Jackson et al., 2008), as well as co-benefits in conservation, agricultural production, water resources, energy, and other sectors.

SCIENCE TO SUPPORT ADAPTATION IN AGRICULTURAL SYSTEMS

The ability of farmers and the entire food production, processing, and distribution system to adapt to climate change will contribute to, and to some extent govern, the ultimate impacts of climate change on food production. Adaptation strategies may include changes in location as well as in-place changes such as shifts in planting dates and varieties; expansion of irrigated or managed areas; diversification of crops and other income sources; application of agricultural chemicals; changes in livestock care, infrastructure, and water and feed management; selling assets or borrowing credit (Moser et al., 2008; NRC, 2010a; Wolfe et al., 2008). At the broadest level, adaptation also includes investment in agricultural research and in institutions to reduce vulnerability. This is because the ability of farmers and others to adapt depends in important ways on available technology, financial resources and financial risk-management instruments, market opportunities, availability of alternative agricultural practices, and importantly, access to, trust in, and use of information such as seasonal forecasts (Cash, 2001; Cash et al., 2006a). It also depends on specific institutional arrangements, including property rights, social norms, trust, monitoring and sanctions, and agricultural extension institutions that can facilitate diversification (Agrawal and Perrin, 2008). Not all farmers have access to such strategies or support institutions, and smallholders—especially those with substantial debt, and the landless in poor countries—are most likely to suffer negative effects on their livelihoods and food security. Smallholder and subsistence farmers will suffer complex, localized impacts of climate change (Easterling et al., 2007).

Integrated assessment models, which combine climate models with crop models and models of the responses of farmers and markets, have been used to simulate the impacts of climate changes on productivity and also on factors such as farm income and crop management. Some modeling studies have included adaptations in these integrated assessments (McCarl, 2008; Reilly et al., 2003), for example by adjusting planting dates or varieties and by reallocating crops according to changes in profitability. For the United States, these studies usually project very small effects of climate change on the agricultural economy, and, in some regions, positive increases in productivity and profitability (assuming adaptation through cropping systems changes). As noted earlier with regard to climate-crop models, assessments have not yet included potential impacts of pests and pathogens or extreme events, nor have they included site- and crop-specific responses to climate change or variations. Moreover, even integrated assessment models that include adaptation do not include estimates of rates of technological change, costs of adaptation, or planned interventions (Antle, 2009). Thus, our understanding of the effects climate change will have on U.S. agriculture and on

international food supplies, distribution, trade, and food security remains quite limited and warrants further research.

As they have in the past, both autonomous adaptations by farmers and planned interventions by governments and other institutions to facilitate, enable, and inform farmers’ responses will be important in reducing potential damages from climate change and other related changes. Investments in crop development, especially in developing countries, have stagnated since the 1980s (Pardey and Beintema, 2002), although recent investments by foundations may fill some of the void. Private-sector expenditures play an important role, especially in developed countries, and some companies are engaging in efforts to develop varieties well suited for a changing climate (Burke et al., 2009; Wolfe et al., 2008).

Government investments in new or rehabilitated irrigation systems (of all sizes) and efficient water use and allocation technologies, transportation infrastructure, financial infrastructure such as availability of credit and insurance mechanisms (Barnett et al., 2008; Gine et al., 2008; World Bank, 2007), and access to fair markets are also important elements of adaptation (Burke et al., 2009). Likewise, investments in participatory research and information provision to farmers have been a keystone of past agricultural development strategies (e.g., through extension services in both developed and developing countries) and no doubt will remain so in the future. Finally, the provision of social safety nets (e.g., formal and informal sharing of risks and costs, labor exchange, crop insurance programs, food aid during emergencies, public works programs, or cash payments), which have long been a mainstay of agriculture in the developed world, will remain important (Agrawal, 2008; Agrawal and Perrin, 2008). These considerations need to be integrated into development planning.

It is important that agriculture be viewed as an integrated system. As noted above, the United States and the rest of the world will be simultaneously developing strategies to adapt agriculture to climate change, to utilize the potential of agricultural practices and other land uses to reduce the magnitude of climate change, and to increase agricultural production to meet rising global demands. With careful analysis and institutional design, these efforts may be able to complement one another while also enhancing our ability to improve global food security. However, without such integrated analysis, various practices and policies could easily work at cross purposes, moving the global food production system further from, rather than closer to, sustainability. For example, increased biofuel production would decrease reliance on fossil fuels but could increase demand for land and food resources (Fargione et al., 2008).

FOOD SECURITY

Food security is defined as a “situation that exists when all people, at all times, have physical, social, and economic access to sufficient, safe, and nutritious food that meets their dietary needs and food preferences for an active and healthy life” (Schmidhuber and Tubiello, 2007). The four dimensions of food security are availability (the overall ability of agricultural systems to meet food demand), stability (the ability to acquire food during income or food price shocks), access (the ability of individuals to have adequate resources to acquire food), and utilization (the ability of the entire food chain to deliver safe food). Climate change affects all four dimensions directly or indirectly; all can be affected at the same time by nonclimatic factors such as social norms, gender roles, formal and informal institutional arrangements, economic markets, and global to local agricultural policies. For example, utilization can be affected through the impact of warming on spoilage and foodborne disease, while access can be affected by changing prices in the fuels used to transport food. Most studies have focused on the first dimension—the direct impact of climate change on the total availability of different agricultural products. Models that account for the other three dimensions need to be developed to identify where people are most vulnerable to food insecurity (Lobell et al., 2008; see also Chapter 4 ).

Because the food system is globally interconnected, it is not possible to view U.S. food security, or that of any other country, in isolation. Where food is imported—as is the case for a high percentage of seafood consumed in the United States—prices and availability can be directly affected by climate change impacts in other countries. Climate change impacts anywhere in the world potentially affect the demand for agricultural exports and the ability of the United States and other countries to meet that demand. Food security in the developing world also affects political stability, and thereby U.S. national security (see Chapter 16 ). Food riots that occurred in many countries as prices soared in 2008 are a case in point (Davis and Belkin, 2008). Over the past 30 years, there has been dramatic improvement in access to food as real food prices have dropped and incomes have increased in many parts of the developing world (Schmidhuber and Tubiello, 2007). Studies that project the number of people at risk of hunger from climate change indicate that the outcome strongly depends on socioeconomic development, since affluence tends to reduce vulnerability by enlarging coping capacity (Schmidhuber and Tubiello, 2007). Clearly, international development strategies and climate change are inextricably intertwined and require coordinated examination.

RESEARCH NEEDS

Given the challenges noted in the previous section, it is clear that expanded research efforts will be needed to help farmers, development planners, and others engaged in the agricultural sector to understand and respond to projected impacts of climate change on agriculture. There may also be opportunities to limit the magnitude of future climate change though changes in agricultural practices; it will be important to link such strategies with adaptation strategies so they complement rather than undermine each other. Identifying which regions, human communities, fisheries, and crops and livestock in the United States and other parts of the world are most vulnerable to climate change, developing adaptation approaches to reduce this vulnerability, and developing and assessing options for reducing agricultural GHG emissions are critical tasks for the nation’s climate change research program. Focus is also needed on the developing world, where the negative effects of climate change on agricultural and fisheries production tend to coincide with people with low adaptation capacity. Some specific research areas are listed below.

Improve models of crop response to climate and other environmental changes. Crop plants and timber species respond to multiple and interacting effects—including temperature, moisture, extreme weather events, CO 2 , ozone, and other factors such as pests, diseases, and weeds—all of which are affected by climate change. Experimental studies that evaluate the sensitivity of crops to such factors, singly and in interaction, are needed, especially in ecosystem-scale experiments and in environments where temperature is already close to optimal for crops. Many assessments model crop response to climate-related variables while assuming no change in availability of water resources, especially irrigation. Projections about agricultural success in the future need to explicitly include such interactions. Of particular concern are assumptions about water availability that include consideration of needs by other sectors. The reliability of water resources for agriculture when there is competition from other uses needs to be evaluated in the context of coupled human-environment systems, ideally at regional scales. Improved understanding of the response of farmers and markets to production and prices and also to policies and institutions that affect land and resource uses is needed; incorporation of that information in models will aid in designing effective agricultural strategies for limiting and adapting to climate change.

Improve models of response of fisheries to climate change. Sustainable yields from fisheries require matching catch limits with the growth of the fishery. Climate variation already makes forecasting the growth of fish populations difficult, and future climate change will increase this critical uncertainty. Studies of connections between

climate and marine population dynamics are needed to enhance model frameworks for fisheries management. In addition, there is considerable uncertainty about differences in sensitivity among and within species to ocean acidification (NRC, 2010f). This inevitable consequence of increasing atmospheric CO 2 is poorly understood, yet global in scope. Most fisheries are subject to other stressors in addition to warming, acidification, and harvesting, and the interactions of these other stresses need to be analyzed and incorporated into models. Finally, these efforts need to be linked to the analysis of effective institutions and policies for managing fisheries.

Expand observing and monitoring systems. Satellite, aircraft, and ground-based measures of changes in crops yields, stress symptoms, weed invasions, soil moisture, ocean productivity, and other variables related to fisheries and crop production are possible but not yet carried out systematically or continuously. Monitoring of the environmental and social dynamics of food production systems on land and in the oceans is also needed to enable assessments of vulnerable systems or threats to food security. Monitoring systems will require metrics of vulnerability and sustainability to provide early warnings and develop adaptation strategies.

Assess food security and vulnerability in the context of climate change. Effective adaptation will require integration of knowledge and models about environmental as well as socioeconomic systems in order to project regional food supplies and demands, understand appropriate responses, to develop institutional approaches for adapting under climate variability and climate change, and to assess implications for food security (NRC, 2009k). Scenarios that evaluate implications of climate change and adaptation strategies for food security in different regions are needed, as are models that assess shifting demands for meat and seafood that will influence price and supply. Approaches, tools, and metrics are needed to assess the differential vulnerability of various human-environment systems so that investments can be designed to reduce potential harm (e.g., through interventions such as the development of new crop varieties and technologies, new infrastructure, social safety nets, or other adaptation measures). A concerted research effort is needed both for conducting assessments and to support the development and implementation of options for adaptation. Surprisingly, relatively little effort has been directed toward identification of geographic areas where damages to agriculture or fisheries could be caused by extreme events (hurricanes, drought, hypoxia); where there is or will be systematic loss of agricultural area due to sea level rise, erosion, and saltwater intrusion; or where there will be changes in average conditions (e.g., extent of sea ice cover, and warming of areas that are now too cold for agriculture) that could lead to broad-scale changes—positive or negative—in the type and manner of agricultural and fisheries production.

Evaluate trade-offs and synergies in managing agricultural lands. Improved integrated assessment approaches and other tools are needed to evaluate agricultural lands and their responses to climate change in the context of other land uses and ecosystem services. Planning approaches need to be developed for avoiding adaptation responses that place other systems (or other generations) at risk—for example, by converting important conservation lands to agriculture, allocating water resources away from environmental or urban needs, or overuse of pesticides and fertilizers. Integrated assessments would help to evaluate both trade-offs (e.g., conservation versus agriculture) and co-benefits (e.g., increasing soil carbon storage while also enhancing soil productivity and reducing erosion) of different actions that might be taken in the agricultural sector to limit the magnitude of climate change or adapt to its impacts.

Evaluate trade-offs and synergies in managing the sea. The oceans provide a wide range of services to humans, but conflicts over use of the oceans are often magnified because of the absence of marine spatial planning and relatively weak international marine regulatory systems. Efforts to limit the magnitude of climate change are causing society to consider the sea for new sources of energy (e.g., waves, tides, thermal gradients), while the opening of ice-free areas in the Arctic is encouraging exploration of offshore reserves of minerals and fossil fuels. Without analyses of the looming tradeoffs between these emerging uses and existing services, such as fisheries and recreation, conflicts will inevitably grow. New approaches for analyses of such trade-offs are needed as an integral component of marine spatial planning.

Develop and improve technologies, management strategies, and institutions to reduce GHG emissions from agriculture and fisheries and to enhance adaptation to climate change. Research on options for reducing emissions from the agricultural sector is needed, including new technologies, evaluation of effectiveness, costs and benefits, perceptions of farmers and others, and policies to promote implementation. Technologies such as crop breeding and new cropping systems could dramatically increase the sector’s adaptive capacity. Research on the role of entitlements and institutional barriers in influencing mitigation or adaptation responses; the effectiveness of governance structures; interactions of national and local policies; and national security implications of climate-agriculture interactions are also needed.

Climate change is occurring, is caused largely by human activities, and poses significant risks for—and in many cases is already affecting—a broad range of human and natural systems. The compelling case for these conclusions is provided in Advancing the Science of Climate Change , part of a congressionally requested suite of studies known as America's Climate Choices. While noting that there is always more to learn and that the scientific process is never closed, the book shows that hypotheses about climate change are supported by multiple lines of evidence and have stood firm in the face of serious debate and careful evaluation of alternative explanations.

As decision makers respond to these risks, the nation's scientific enterprise can contribute through research that improves understanding of the causes and consequences of climate change and also is useful to decision makers at the local, regional, national, and international levels. The book identifies decisions being made in 12 sectors, ranging from agriculture to transportation, to identify decisions being made in response to climate change.

Advancing the Science of Climate Change calls for a single federal entity or program to coordinate a national, multidisciplinary research effort aimed at improving both understanding and responses to climate change. Seven cross-cutting research themes are identified to support this scientific enterprise. In addition, leaders of federal climate research should redouble efforts to deploy a comprehensive climate observing system, improve climate models and other analytical tools, invest in human capital, and improve linkages between research and decisions by forming partnerships with action-oriented programs.

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• Published: 05 July 2022

Potential impacts of climate change on agriculture and fisheries production in 72 tropical coastal communities

• Joshua E. Cinner   ORCID: orcid.org/0000-0003-2675-9317 1 ,
• Iain R. Caldwell   ORCID: orcid.org/0000-0001-8148-8762 1 ,
• Lauric Thiault   ORCID: orcid.org/0000-0002-5572-7632 2 , 3 ,
• John Ben 4 ,
• Julia L. Blanchard   ORCID: orcid.org/0000-0003-0532-4824 5 , 6 ,
• Marta Coll   ORCID: orcid.org/0000-0001-6235-5868 7 ,
• Amy Diedrich 8 , 9 ,
• Tyler D. Eddy   ORCID: orcid.org/0000-0002-2833-9407 10 ,
• Jason D. Everett   ORCID: orcid.org/0000-0002-6681-8054 11 , 12 , 13 ,
• Christian Folberth   ORCID: orcid.org/0000-0002-6738-5238 14 ,
• Didier Gascuel   ORCID: orcid.org/0000-0001-5447-6977 15 ,
• Jerome Guiet   ORCID: orcid.org/0000-0002-2146-5160 16 ,
• Georgina G. Gurney 1 ,
• Ryan F. Heneghan   ORCID: orcid.org/0000-0001-7626-1248 17 ,
• Jonas Jägermeyr 18 , 19 , 20 ,
• Narriman Jiddawi 21 ,
• Rachael Lahari 22 ,
• John Kuange 23 ,
• Wenfeng Liu   ORCID: orcid.org/0000-0002-8699-3677 24 ,
• Olivier Maury   ORCID: orcid.org/0000-0002-7999-9982 25 ,
• Christoph Müller   ORCID: orcid.org/0000-0002-9491-3550 20 ,
• Camilla Novaglio   ORCID: orcid.org/0000-0003-3681-1377 5 , 6 ,
• Juliano Palacios-Abrantes   ORCID: orcid.org/0000-0001-8969-5416 26 , 27 ,
• Colleen M. Petrik   ORCID: orcid.org/0000-0003-3253-0455 28 ,
• Ando Rabearisoa   ORCID: orcid.org/0000-0001-5371-7695 29 ,
• Derek P. Tittensor 30 , 31 ,
• Andrew Wamukota 32 &
• Richard Pollnac 33 , 34  

Nature Communications volume  13 , Article number:  3530 ( 2022 ) Cite this article

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• Climate change
• Climate-change ecology

Climate change is expected to profoundly affect key food production sectors, including fisheries and agriculture. However, the potential impacts of climate change on these sectors are rarely considered jointly, especially below national scales, which can mask substantial variability in how communities will be affected. Here, we combine socioeconomic surveys of 3,008 households and intersectoral multi-model simulation outputs to conduct a sub-national analysis of the potential impacts of climate change on fisheries and agriculture in 72 coastal communities across five Indo-Pacific countries (Indonesia, Madagascar, Papua New Guinea, Philippines, and Tanzania). Our study reveals three key findings: First, overall potential losses to fisheries are higher than potential losses to agriculture. Second, while most locations (> 2/3) will experience potential losses to both fisheries and agriculture simultaneously, climate change mitigation could reduce the proportion of places facing that double burden. Third, potential impacts are more likely in communities with lower socioeconomic status.

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Introduction

Climate change is expected to profoundly impact key food production sectors, with the tropics expected to suffer losses in both fisheries and agriculture. For example, by 2100 tropical areas could lose up to 200 suitable plant growing days per year due to climate change 1 . Likewise, fishable biomass in the ocean could drop by up to 40% in some tropical areas 2 , 3 . While understanding the magnitude of losses that climate change is expected to create in key food production sectors is crucial, it is the social dimensions of vulnerability that determine the degree to which societies are likely to be affected by these changes 4 , 5 , 6 , 7 , 8 . Vulnerability is the degree to which a system is susceptible to and unable to cope with the effects of change. It is comprised of exposure (the degree to which a system is stressed by environmental or social conditions), the social dimensions of sensitivity (the state of susceptibility to harm from perturbations), and adaptive capacity (people’s ability to anticipate, respond to, and recover from the consequences of these changes) 4 , 9 . Together, the exposure and sensitivity domains are referred to as “potential impacts”, which are the focus of this article.

Incorporating key social dimensions of vulnerability is particularly important because many coastal communities simultaneously rely on both agriculture and fisheries to varying degrees 10 , yet assessments of climate change impacts and the policy prescriptions that come from them often consider these sectors in isolation 1 , 5 , 11 , 12 , 13 , 14 . Recently, studies have begun to look at the simultaneous impacts of climate change on both fisheries and agriculture at the national level 15 , 16 , but this coarse scale does not capture whether people simultaneously engage with- and are likely to be affected by- changes in these sectors. Indeed, whether households engage in both fisheries and agriculture 10 will determine whether people have the knowledge, skills, and capital to substitute sectors if one declines, or alternatively, make them particularly susceptible to the potential double burden of a combined decline across sectors 15 . Thus, more localised analyses incorporating key social dimensions of vulnerability are required to better understand how combined impacts to fisheries and agriculture may affect coastal communities.

Here, we combine a measure of exposure based on model projections of losses to exploitable marine biomass (here dubbed fisheries catch potential) and agriculture from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) Fast Track phase 3 dataset with a measure of sensitivity based on survey data about material wealth and engagement in fisheries, agriculture, and other occupational sectors from >3,000 households across 72 tropical coastal communities in five countries (see Supplementary Data  file ). We answer the following questions: 1) What are the potential impacts of projected changes to fisheries catch potential and agriculture on coastal communities?, 2) How much will mitigation measures reduce these potential impacts?, and 3) Are lower socioeconomic status coastal communities facing more potential impacts from climate change than their wealthier counterparts? We show that: fisheries tend to be more impacted than agriculture although there is substantial within-country variability; climate change mitigation can reduce the number of locations experiencing a double burden (i.e. losses to both fisheries and agriculture); and communities with lower socioeconomic status will experience the most severe climate change impacts.

Our study has three key results. First, we find that overall possible impacts on fisheries catch potential is higher than possible impacts on agriculture, but there can be substantial within-country variability in both exposure and sensitivity (Fig.  1 ). Specifically, exposure under the high-emissions Shared Socioeconomic Pathway 8.5 scenario (which has tracked historic cumulative CO 2 emissions 17 , but has been recently critiqued for over-projecting CO 2 emissions and economic growth 18 ) indicates substantive losses by mid-century to fisheries catch potential [Fig.  1 ; 14.7% +/− 4.3% (SE) mean fisheries catch potential loss]. To put these projected losses in perspective, Sala et al 19 . found that strategically protecting 28% of the ocean could increase food provisioning by 5.9 million tonnes, which is just 6.9% of the 84.4 million tons of marine capture globally in 2018 20 . Thus, the mean expected fisheries catch potential losses are approximately double that which could be buffered by strategic conservation. Model run agreement about the directionality of change for projected impacts to fisheries catch potential was high (SSP5-8.5: 84.7 + /− 4.5% (SE); SSP1-2.6: 89.2 + /− 4.06% (SE)). Interestingly, crop models projected that agricultural productivity (based on rice, maize, and cassava- see methods) is expected to experience small average gains across the 72 sites (1.2% +/− 1.5% (SE) mean agricultural gain), with a large response range between sites and crops (Supplementary Fig.  1 ). However, the average gains are not significantly different from zero ( t  = −0.80, df = 5.0, p  = 0.46), and model run agreement about directionality of change was lower for agriculture (SSP5-8.5: 69.1 + /− 4.82% (SE); SSP1-2.6: 70.4 +/− 3.27% (SE)). These projected agricultural gains are driven exclusively by rice (Supplementary Fig.  1 ), which has particularly large model disagreement 14 , 21 . Excluding rice shows an average decline in agricultural production by mid-century, since maize and cassava show consistent median losses under both SSP1-2.6 and SSP5-8.5 climate scenarios (Supplementary Fig.  1 ). Significantly greater losses in fisheries catch potential compared to agriculture productivity are apparent not only for our study sites (i.e. 15.9 + /− 5.6% (SE) greater; t  = 2.81, df = 4.97, p  = 0.0379), but also for a random selection of 4746 (10% of) coastal locations in our study countries with populations >25 people per km 2 (Fig.  2 ). Among those random sites, fisheries catch potential losses are an average of 15.6 + /− 5.1% (SE) greater than agriculture productivity changes ( t  = 3.06, df = 5.00, p  = 0.0282). Differences between expected losses at our sites and the randomly selected sites are small for agriculture (Cohen’s D for SSP5-8.5 = -0.31, SSP1-2.6 = −0.35) and negligible for fisheries catch potential (Cohen’s D for SSP5-8.5 = -0.02, SSP1-2.6 = -0.03), indicating that our sites are not particularly biased towards high or low exposure for the study region. Not only is the level of exposure generally higher in fisheries compared to agriculture, but the sensitivity is on average nearly twice as high (Fig.  1a, b ; 0.077 + /− 0.007 mean fisheries sensitivity; 0.04 +/− 0.01 mean agricultural sensitivity; t  = 3.0, df = 2.26, p value =0.0815).

Potential impacts comprise the exposure (y-axis, measured in potential losses, with error bars showing 25th and 75th percentiles) and sensitivity (x-axis, measured as level of dependence by households). Model run agreement (shown as colour gradient) highlights the proportion of ( a ) crop model runs ( n  = 20), ( b ) fisheries model runs ( n  = 16), and ( c ) average of agriculture and fisheries model runs that agree about the direction of change per site. Point shapes indicate country of each community. Inset map in Supplementary Fig.  9 .

Black dots, histograms, and dotted lines (for mean exposures) represent our study sites ( n = 72). Grey dots, histograms, and dotted lines represent a random selection of 10% of coastal cells with population densities >25 people/km 2 from our study countries ( n = 4746).

Our analysis also reveals high within-country variability in potential impacts (i.e. both exposure and sensitivity), particularly for fisheries (Fig.  1 ) - a finding that may be masked in studies looking at national-level averages 15 , 16 . Looking only at the mean expected losses can obscure the more extreme fisheries catch potential losses projected for many communities (Figs.  1 , 2 ). For example, under SSP5-8.5, our Indonesian sites are projected to experience very close to the average fisheries catch potential losses among our study sites (15.9 + /− 2.1%SE), but individual sites range from 6.5-32% losses (Fig.  1b ). There is also substantial within-country variation in how communities are likely to experience climate change impacts, based on their sensitivity (Fig.  1a, b ). For example, in the Philippines, exposure to fisheries is consistently moderate (range 8.9-12.6% loss), but sensitivity ranges from our lowest (0.001) to our highest recorded scores (0.32). There is also within-country variability in model agreement, particularly for the agricultural models in Indonesia, where agricultural model agreement ranges from 50-85% and fisheries model agreement ranges from 56-100% for SSP5-8.5, and 50-80% and 50-94%, respectively, for SSP1-2.6.

The second key result from our integrated assessment reveals that some locations will bear a double burden of losses to fisheries and agriculture simultaneously, but mitigation efforts that reduce greenhouse gas emissions could curb these losses. Specifically, under SSP5-8.5, 64% of our study sites are expected to lose productivity in fisheries and agriculture simultaneously (Fig.  3a ), but this would reduce to 37% of sites under the low emissions scenario SSP1-2.6 (Fig.  3b ). Again, the effect of mitigation is consistent in the random selection of 4746 sites (Supplementary Fig.  2 ), with 70% of randomly selected sites expected to experience a double burden under SSP5 8.5, and 47% under SSP1 2.6. Many of the sites expected to experience the highest losses to both fisheries catch potential and agriculture have moderate to high sensitivity (Fig.  3a , Supplementary Fig.  3 ), which means the impacts of these changes could be profoundly felt by coastal communities.

a Under SSP5-8.5 agricultural losses (y-axis) plotted against fisheries losses (x-axis), with bubble size revealing the overall sensitivity and colour revealing the fisheries-agricultural relative sector dependency of each community’s sensitivity. b Potential benefits of mitigation shown by the potential losses for each community change going from the high emissions scenario (SSP5-8.5 in red) to a low emissions scenario (SSP1-2.6 in yellow).

Over a third of our sites (36% under SSP5-8.5) are expected to experience increases in agriculture (due to CO 2 fertilization effects that fuel potential increases particularly in rice yields) while experiencing losses in fisheries catch potential. For these sites, a question of critical concern is whether the potential gains in agriculture could help offset the losses in fisheries catch potential. The answer to this lies in part in the degree of substitutability between sectors. Our survey of 3,008 households reveals high variation among countries, and even within some countries in the degree of household occupational multiplicity incorporating both agriculture and fisheries sectors (Table  1 ). 31% of households in our study engaged in both fishing and agriculture, though this ranged from 10% of households in the Philippines to 77% of households in Papua New Guinea. This means that the degree to which agricultural gains might possibly offset some fisheries losses at the household scale is very context dependent. Our survey also revealed that 17% of households were involved in agriculture but not fisheries, ranging from 33% in Madagascar to 3% in our Papua New Guinean study communities. Alternatively, more than a third of households surveyed in Indonesia and Philippines were involved in fisheries but not agriculture (36% and 37% respectively), compared to a low value of 16% in Madagascar. In 12% of the Philippines communities surveyed (n = 3), not a single household was engaged in agriculture. Thus, for 32% of households across our sample, including some entire communities, potential agricultural gains will not offset potential fisheries losses. In these locations building adaptive capacity to buffer change will be critical 9 .

Our third key result is that coastal communities with lower socioeconomic status are more likely to experience potential impacts than communities of higher socioeconomic status across the climate mitigation scenarios (SSP1-2.6 and SSP5-8.5; Fig.  4 ). Specifically, we examined the relationship between the average material style of life (a metric of wealth based on material assets; see methods) in a community and the relative potential impacts of simultaneous fisheries catch potential and agriculture losses (measured as the Euclidean distance of sensitivity and exposure from the origin). Importantly, socioeconomic status is related to both sensitivity and exposure (Supplementary Fig.  4 ). In other words, low socioeconomic status communities tend to have higher sensitivity to fisheries and agriculture than the wealthy, and are significantly more likely to be exposed to climate change impacts. Our findings regarding the relationship between socioeconomic status and sensitivity are consistent with a broad body of literature that shows how people tend to move away from natural resource-dependent occupations as they become wealthier 10 , 22 , 23 , 24 , 25 . One potential interpretation of our findings is that alternative livelihood programs (e.g. jobs outside the fisheries or agricultural sectors, such as the service industry) could reduce sensitivity in lower socioeconomic status communities. However, decades of research on livelihood diversification has highlighted a multitude of reasons why alternative livelihood projects frequently fail 26 , including that they do not provide high levels of non-economic satisfactions (e.g., social, psychological, and cultural) 27 , 28 , 29 , as well as cultural barriers to switching occupations (e.g. caste systems) 30 , and attachment to identity and place 31 . Alternative occupations need to provide some of the same satisfactions, including basic needs (safety, income), social and psychological needs (time away from home, community in which you live, etc.), and self-actualization (adventure, challenge, opportunity to be own boss, etc.). For example, fishing attracts individuals manifesting a personality configuration referred to as an externalizing disposition, which is characterized by a need for challenges, adventure, and risk. Fishing can be extremely satisfying for people with this personality complex, while many alternative occupations can lead to job dissatisfaction, which has negative social and psychological consequences 32 , 33 . Research has shown that recreational fishing captain or guide jobs produce some of the same satisfactions as fishing and have been successfully introduced as alternative occupations 33 . Despite these limited successes, alternative livelihood programs frequently fail and are not a viable substitute for mitigating climate change for the ~6 million coral reef fishers globally 34 .

Black lines are predictions from linear mixed-effects models (with country as random effect) and grey bands are standard errors. Statistical significance ( p ) and fit ( R 2 ) of the mixed-effects models are also shown: (m) = marginal R 2 , (c) = conditional R 2 . Point shape and colour indicate country.

Our study is an important first step in examining the potential simultaneous impacts to fisheries catch potential and agriculture in coastal communities, but has some limitations, some of which could be addressed in future studies. First, our measure of exposure was dynamic (i.e., it was projected into the future), while our measures of sensitivity and material wealth were static (i.e., from a single point in time) and did not consider potential changes over time. Although there are projections of how national-scale measures of wealth (e.g. gross domestic product; GDP) may change in the future, there are no reliable projections for household- or community-scale changes to material wealth or livelihoods. As an additional analysis, we examined observed changes in sensitivity and material wealth over 15 and 16 years, respectively, in two Papua New Guinean coastal communities (Fig.  5 ). We found that, over the observed time frame (2001-2016), which is approximately half that of the predicted time frame of exposure, sensitivity scores were extremely stable, particularly in Ahus (Fig.  5 ). Similarly, material wealth was also reasonably stable over time, but did reflect a shift in both communities toward more houses being built out of sturdier material (e.g., wood plank walls and floor, metal roofs). Importantly, while there were absolute changes to material wealth in both communities, the relative position stayed very similar. Although these data do not allow us to make inferences about what will happen into the future, they do highlight that, at least in decadal timeframes, these indicators are reasonably stable. One alternative approach may have been to assume that projected national-scale changes to GDP would apply evenly across each coastal community within a country (i.e., adjust the intercept of both material wealth and correlated sensitivity for each country relative to the projected changes in GDP). However, given the wide spread of material wealth and sensitivity scores within countries, we ultimately were less comfortable with the assumptions inherent in the approach (i.e., that national-scale changes would affect all communities in a country equally) than with the caveat that our metrics were static.

b shows how the communities change along the first two axes of a principal component analysis (i.e., PC1 and PC2), based on 16 household-scale material items, with black text and grey lines indicate the relative contribution of each material item to principal components.

Second, there are key limitations and assumptions to the models we used. For example, many tropical small-scale fisheries target seagrass 35 and coral reef habitats 34 , which are not represented in the global ensemble models. Additionally, the ensemble models were developed at relatively low spatial resolution (e.g. 1° cells), and are not designed to capture higher-resolution structures and processes. Our approach for dealing with this was to make transparent the degree of ensemble model run agreement about the direction of change, which relies on the assumption that we have greater confidence in projections that have higher model run agreement. Another limitation is that there may be discrepancies between the total consumer biomass (see method) in the absence of fishing that is outputed by the models used here and what would actually be harvested by fishers since total consumer biomass can include both target and non-target species as well as other taxa entirely. Despite these limitations, we assumed that total consumer biomass is directly related to potential fisheries yields 11 . Likewise, we included just three crops in the agricultural models (rice, maize, and cassava), which are key in the study region, with many study countries growing 2 or more of these crops. For example, in 2020 Indonesia was the 4th largest producer of rice in the world, the 5th largest producer of cassava, and the 8th largest producer of maize 36 . However, subsistence agriculture in Papua New Guinea is dominated by banana and yams, for which agricultural yield projections were not available. We used an unweighted average of projected changes in these three crops to represent a portfolio of small-scale agriculture, with a sensitivity test based on agricultural projections weighted by current yields/production area proportions of current yields (Supplementary Fig.  1 ). Finally, it is important to keep key model assumptions in mind when interpreting these data. For example, the agricultural models assumed no changes in farm management or climate change adaptation over time, while the fisheries models do not explicitly resolve predation impacts from higher trophic levels on phytoplankton.

Third, our sensitivity metric examined a somewhat narrow aspect of what makes people sensitive to climate change. Sensitivity is thought to contain dimensions of economic, demographic, psychological, and cultural dependency 37 . Our metric was based on people’s engagement in natural resource-based livelihoods, which primarily captures the economic dimensions (although livelihoods do provide cultural and psychological contributions to people 26 , 28 , 29 , 31 , 38 ).

Fourth, our study explicitly focused on the potential impacts of climate change in 72 Indo-Pacific coastal communities by examining their sensitivity and exposure, but our methodology did not enable us to incorporate adaptive capacity. Adaptive capacity is a latent trait that enables people to adapt to and take advantage of the opportunities created by change 39 , 40 , and is critically important in determining the fate of coastal communities under climate change. Adaptive capacity is thought to consist of dimensions of assets, flexibility, social organisation, learning, socio-cognitive, and agency 9 , 41 , 42 . Unfortunately, indicators of these dimensions of adaptive capacity were not collected in a standardised manner across all of the different projects comprising this study.

Fifth, we investigated the potential impacts of climate change on two key food production sectors, but there may be other climate change impacts which have much more profound impacts on people’s wellbeing. For example, sea level rise may destroy homes and other infrastructure 43 , while heat waves may result in direct mortality 44 . Last, we used shared socioeconomic pathway exploratory scenarios that bracket the full range of scenario variability (SSP5-8.5 and SSP1-2.6). At the time of publication, these were the only scenarios available for both fisheries and agriculture using the ISIMIP Fastrack Phase 3 dataset. Future publications may wish to explore additional scenarios.

Our study quantifies the potential impacts of climate change on key food production sectors in tropical coastal communities across a broad swath of the Indo-Pacific. We find that both exposure and sensitivity to fisheries is generally higher than to agriculture, but some places may experience losses from both sectors simultaneously. These losses may be compounded by other drivers of change, such as overfishing or soil erosion, which is already leading to declining yields 45 , 46 . Simultaneous losses to both fisheries catch potential and agriculture will limit people’s opportunity to adapt to changes through switching livelihoods between food production sectors 9 . This will especially be the case in lower socioeconomic status communities where dependence on natural resources is higher 10 . Together, our integration of model projections and socioeconomic surveys highlight the importance of assessing climate change impacts across sectors, but reveals important mismatches between the scale at which people will experience the impacts of climate change and the scale at which modelled projections about climate change impacts are currently available.

Sampling of coastal communities

Here, we integrated data from five different projects that had surveyed coastal communities across five countries 47 , 48 , 49 , 50 . Between 2009 and 2015, we conducted socioeconomic surveys in 72 sites from Indonesia ( n  = 25), Madagascar ( n  = 6), Papua New Guinea ( n  = 10), the Philippines ( n  = 25), and Tanzania (Zanzibar) ( n  = 6). Site selection was for broadly similar purposes- to evaluate the effects of various coastal resource management initiatives (collaborative management, integrated conservation and development projects, recreational fishing projects) on people’s livelihoods in rural and peri-urban villages. Within each project, sites were purposively selected to be representative of the broad range of socioeconomic conditions (e.g., population size, levels of development, integration to markets) experienced within the region. We did not survey strictly urban locations (i.e., major cities). Because our sampling was not strictly random, care should be taken when attempting to make inferences beyond our specific study sites.

We surveyed between 13 and 150 households per site, depending on the population of the communities and the available time to conduct interviews per site. All projects employed a comparable sampling design: households were either systematically (e.g., every third house), randomly sampled, or in the case of three villages, every household was surveyed (a census) (see Supplementary Data  file ). Respondents were generally the household head, but could have been other household members if the household head was not available during the study period (i.e. was away). In the Philippines, sampling protocol meant that each village had an even number of male and female respondents. Respondents gave verbal consent to be interviewed.

The following standard methodology was employed to assess material style of life, a metric of material assets-based wealth 48 , 51 . Interviewers recorded the presence or absence of 16 material items in the household (e.g., electricity, type of walls, type of ceiling, type of floor). We used a Principal Component Analysis on these items and kept the first axis (which explained 34.2% of the variance) as a material wealth score. Thus, each community received a mean material style of life score, based on the degree to which surveyed households had these material items, which we then scaled from 0 to 1. We also conducted an exploratory analysis of how material style of life has changed in two sites in Papua New Guinea (Muluk and Ahus villages) over fifteen and sixteen-year time span across four and five-time periods (2001, 2009, 2012, 2016, and 2002, 2009, 2012, 2016, 2018), respectively, that have been surveyed since 2001/2002 52 . These surveys were semi-panel data (i.e. the community was surveyed repeatedly, but we did not track individuals over each sampling interval) and sometimes occurred in different seasons. For illustrative purposes, we plotted how these villages changed over time along the first two principal components.

Sensitivity

We asked each respondent to list all livelihood activities that bring in food or income to the household and rank them in order of importance. Occupations were grouped into the following categories: farming, cash crop, fishing, mariculture, gleaning, fish trading, salaried employment, informal, tourism, and other. We considered fishing, mariculture, gleaning, fish trading together as the ‘fisheries’ sector, farming and cash crop as the ‘agriculture’ sector and all other categories into an ‘off-sector’.

We then developed three distinct metrics of sensitivity based on the level of dependence on agriculture, fisheries, and both sectors together. Each metric incorporates the proportion of households engaged in a given sector (e.g., fisheries), whether these households also engage in occupations outside of this sector (agriculture and salaried/formal employment; referred to as ‘linkages’ between sectors), and the directionality of these linkages (e.g., whether respondents ranked fisheries as more important than other agriculture and salaried/formal employment) (Eqs.  1 – 3 )

where \({{{{{{\rm{S}}}}}}}_{{{{{{\rm{A}}}}}}}\) , \({{{{{{\rm{S}}}}}}}_{{{{{{\rm{F}}}}}}}\) and \({{{{{{\rm{S}}}}}}}_{{{{{{\rm{AF}}}}}}}\) are a community’s sensitivity in the context of agriculture, fisheries and both sectors, respectively. A, F and AF are the number of households relying on agriculture-related occupations within that community, fishery-related and agriculture- and fisheries-related occupations within the community, respectively. NA, NF and NAF are the number of households relying on non-agriculture-related, non-fisheries-related, and non-agriculture-or-fisheries-related occupations within the community, respectively. N is the number of households within the community. \({{{{{{\rm{r}}}}}}}_{{{{{{\rm{a}}}}}}}\) , \({{{{{{\rm{r}}}}}}}_{{{{{{\rm{f}}}}}}}\) and \({{{{{{\rm{r}}}}}}}_{{{{{{\rm{af}}}}}}}\) are the number of times agriculture-related, fisheries-related and agriculture-and-fisheries-related occupations were ranked higher than their counterpart, respectively. \({{{{{{\rm{r}}}}}}}_{{{{{{\rm{na}}}}}}}\) , \({{{{{{\rm{r}}}}}}}_{{{{{{\rm{nf}}}}}}}\) and \({{{{{{\rm{r}}}}}}}_{{{{{{\rm{naf}}}}}}}\) are the number of times non-agriculture, non-fisheries, and non-agriculture-and-fisheries-related occupations were ranked higher than their counterparts. As with the material style of life, we also conducted an exploratory analysis of how joint agriculture-fisheries sensitivity has changed over time in a subset of sites (Muluk and Ahus villages in Papua New Guinea) that have been sampled since 2001/2002 52 . Although our survey methodology has the potential for bias (e.g. people might provide different rankings based on the season, or there might be gendered differences in how people rank the importance of different occupations 53 ), our time-series analysis suggest that seasonal and potential respondent variation do not dramatically alter our community-scale sensitivity metric.

To evaluate the exposure of communities to the impact of future climates on their agriculture and fisheries sectors, we used projections of production potential from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) Fast Track phase 3 experiment dataset of global simulations. Production potential of agriculture and fisheries for each of the 72 community sites and 4746 randomly selected sites from our study countries with coastal populations >25 people/km 2 were projected to the mid-century (2046–2056) under two emission scenarios (SSP1-2.6, and SSP5-8.5) and compared with values from a reference historical period (1983–2013).

For fisheries exposure (E F ), we considered relative change in simulated total consumer biomass (all modelled vertebrates and invertebrates with a trophic level >1). For each site, the twenty nearest ocean grid cells were determined using the Haversine formula (Supplementary Fig.  5 ). We selected twenty grid cells after a sensitivity analysis to determine changes in model agreement based on different numbers of cells used (1, 3, 5, 10, 20, 50, 100; Supplementary Figs.  6 – 7 ), which we balanced off with the degree to which larger numbers of cells would reduce the inter-site variability (Supplementary Fig.  8 ). We also report 25th and 75th percentiles for the change in marine animal biomass across the model ensemble. Projections of the change in total consumer biomass for the 72 sites were extracted from simulations conducted by the Fisheries and marine ecosystem Model Intercomparison Project (FishMIP 3 , 54 ). FishMIP simulations were conducted under historical, SSP1-2.6 (low emissions) and SSP5-8.5 (high emissions) scenarios forced by two Earth System Models from the most recent generation of the Coupled Model Intercomparison project (CMIP6); 55 GFDL-ESM4 56 and IPSL-CM6A-LR 57 . The historical scenario spanned 1950–2014, and the SSP scenarios spanned 2015–2100. Nine FishMIP models provided simulations: APECOSM 58 , 59 , BOATS 60 , 61 , DBEM 2 , 62 , DBPM 63 , EcoOcean 64 , 65 , EcoTroph 66 , 67 , FEISTY 68 , Macroecological 69 , and ZooMSS 11 . Simulations using only IPSL-CM6A-LR were available for APECOSM and DBPM, while the remaining 7 FishMIP models used both Earth System Model forcings. This resulted in 16 potential model runs for our examination of model agreement, albeit with some of these runs being the same model forced with two different ESMs. Thus, the range of model agreement could range from 8 (half model runs indicating one direction of change, and half indicating the other) to 16 (all models agree in direction of change). Model outputs were saved with a standardised 1° spatial grid, at either a monthly or annual temporal resolution.

For agriculture exposure (E A ), we used crop model projections from the Global Gridded Crop model Intercomparison Project (GGCMI) Phase 3 14 , which also represents the agriculture sector in ISIMIP. We used a window of 11×11 cells centred on the site and removed non-land cells (Supplementary Fig.  5 ). The crop models use climate inputs from 5 CMIP6 ESMs (GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, and UKESM1-0-LL), downscaled and bias-adjusted by ISIMIP and use the same simulation time periods. We considered relative yield change in three rain-fed and locally relevant crops: rice, maize, and cassava, using outputs from 4 global crop models (EPIC-IIASA, LPJmL, pDSSAT, and PEPIC), run at 0.5° resolution. These 4 models with 5 forcings generate 20 potential model runs for our examination of model agreement. Yield simulations for cassava were only available from the LPJmL crop model. All crop model simulations assumed no adaptation in growing season and fertilizer input remained at current levels. Details on model inputs, climate data, and simulation protocol are provided in ref. 14 . At each site, and for each crop, we calculated the average change (%) between projected vs. historical yield within 11×11 cell window. We then averaged changes in rice, maize and cassava to obtain a single metric of agriculture exposure (E A ).

We also obtained a composite metric of exposure (E AF ) by calculating each community’s average change in both agriculture and fisheries:

Potential Impact

We calculated relative potential impact as the Euclidian distance from the origin (0) of sensitivity and exposure.

Sensitivity test

To determine whether our sites displayed a particular exposure bias, we compared the distributions of our sites and 4746 sites that were randomly selected from 47,460 grid cells within 1 km of the coast of the 5 countries we studied which had population densities >25 people/km 2 , based on the SEDAC gridded populating density of the world dataset ( https://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-density-rev11/data-download ).

We used Cohen’s D to determine the size of the difference between our sites and the randomly selected sites.

Validating ensemble models

We attempted a two-stage validation of the ensemble model projections. First, we reviewed the literature on downscaling of ensemble models to examine whether downscaling validation had been done for the ecoregions containing our study sites.

While no fisheries ensemble model downscaling had been done specific to our study regions, most of the models of the ensemble have been independently evaluated against separate datasets aggregated at scales down to Large Marine Ecosystems (LMEs) or Exclusive Economic Zones (EEZs) (see 11 ). For example, the DBEM was created with the objective of understanding the effects of climate change on exploited marine fish and invertebrate species 2 , 70 . This model roughly predicts species’ habitat suitability; and simulates spatial population dynamics of fish stocks to output biomass and maximum catch potential (MCP), a proxy of maximum sustainable yield 2 , 62 , 71 . Compared with spatially-explicit catch data from the Sea Around Us Project (SAUP; www.seaaroundus.org ) 70 there were strong similarities in the responses to warming extremes for several EEZs in our current paper (Indonesia and Philippines) and weaker for the EEZs of Madagascar, Papua New Guinea, and Tanzania. At the LME level, DBEM MCP simulations explained about 79% of the variation in the SAUP catch data across LMEs 72 . The four LMEs analyzed in this paper (Agulhas Current; Bay of Bengal; Indonesian Sea; and Sulu-Celebes Sea) fall within the 95% confidence interval of the linear regression relationship 62 . Another example, BOATS, is a dynamic biomass size-spectrum model parameterised to reproduce historical peak catch at the LME scale and observed catch to biomass ratios estimated from the RAM legacy stock assessment database (in 8 LMEs with sufficient data). It explained about 59% of the variability of SAUP peak catch observation at the LME level with the Agulhas Current, Bay of Bengal, and Indonesian Sea catches reproduced within +/-50% of observations 61 . The EcoOcean model validation found that all four LMEs included in this study fit very close to the 1:1 line for overserved and predicted catches in 2000 64 , 65 . DBPM, FEISTY, and APECOSM have also been independently validated by comparing observed and predicted catches. While the models of this ensemble have used different climate forcings when evaluated independently, when taken together the ensemble multi-model mean reproduces global historical trends in relative biomass, that are consistent with the long term trends and year-on-year variation in relative biomass change (R 2 of 0.96) and maximum yield estimated from stock assessment models (R 2 of 0.44) with and without fishing respectively 11 .

Crop yield estimates simulated by GGCMI crop models have been evaluated against FAOSTAT national yield statistics 14 , 73 , 74 . These studies show that the models, and especially the multi-model mean, capture large parts of the observed inter-annual yield variability across most main producer countries, even though some important management factors that affect observed yield variability (e.g., changes in planting dates, harvest dates, cultivar choices, etc.) are not considered in the models. While GCM-based crop model results are difficult to validate against observations, Jägermeyr et al 14 . show that the CMIP6-based crop model ensemble reproduces the variability of observed yield anomalies much better than CMIP5-based GGCMI simulations. In an earlier crop model ensemble of GGCMI, Müller et al. 74 show that most crop models and the ensemble mean are capable of reproducing the weather-induced yield variability in countries with intensely managed agriculture. In countries where management introduces strong variability to observed data, which cannot be considered by models for lack of management data time series, the weather-induced signal is often low 75 , but crop models can reproduce large shares of the weather-induced variability, building trust in their capacity to project climate change impacts 74 .

We then attempted to validate the models in our study regions. For the crop models, we examined production-weighted agricultural projections weighted by current yields/production area (Supplementary Fig.  1 ). We used an observational yield map (SPAM2005) and multiplied it with fractional yield time series simulated by the models to calculate changes in crop production over time, which integrates results in line with observational spatial patterns. The weighted estimates were not significantly different to the unweighted ones (t = 0.17, df = 5, p = 0.87). For the fisheries models, our study regions were data-poor and lacked adequate stock assessment data to extend the observed global agreement of the sensitivity of fish biomass to climate during our reference period (1983-2013). Instead, we provide the degree of model run agreement about the direction of change in the ensemble models to ensure transparency about the uncertainty in this downscaled application.

To account for the fact that communities were from five different countries we used linear mixed-effects models (with country as a random effect) for all analyses. All averages reported (i.e. exposure, sensitivity, and model agreement) are estimates from these models. In both our comparison of fisheries and agriculture exposure and test of differences between production-weighted and unweighted agriculture exposure we wanted to maintain the paired nature of the data while also accounting for country. To accomplish this we used the differences between the exposure metrics as the response variable (e.g. fisheries exposure minus agriculture exposure), testing whether these differences are different from zero. We also used linear mixed-effects models to quantify relationships between the material style of life and potential impacts under different mitigation scenarios (SSP1-2.6 and 8.5), estimating standard errors from 1000 bootstrap replications. To further explore whether these relationships between the material style of life and potential impacts were driven by exposure or sensitivity, we conducted an additional analysis to quantify relationships between the material style of life and: 1) joint fisheries and agricultural sensitivity; 2) joint fisheries and agricultural exposure under different mitigation scenarios. We present both the conditional R 2 (i.e., variance explained by both fixed and random effects) and the marginal R 2 (i.e., variance explained by only the fixed effects) to help readers compare among the material style of life relationships.

Reporting summary

Further information on research design is available in the  Nature Research Reporting Summary linked to this article.

Data availability

The de-identified exposure, sensitivity, and material style of life data generated in this study for each community can be accessed through Zenodo 76 [ https://doi.org/10.5281/zenodo.6496413 ]. All outputs from the FishMIP model ensemble are available via ISIMIP [ https://www.isimip.org/gettingstarted/data-access/ ]. Raw social survey data are not available because our verbal informed consent made it clear that only aggregated data would be published. The sample sizes and proportions of each community included in the social surveys can be found in the Supplementary Data  file . Base layer map data in Fig.  1c and Supplementary Figures  5 , 8 , and 9 is from Natural Earth, which is freely available through their website (naturalearthdata.com). The SEDAC gridded populating density of the world dataset used to identify a subset of random locations can be found at the following: https://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-density-rev11/data-download .

Code availability

Code used to analyse and visualize results is available through Zenodo 76 [ https://doi.org/10.5281/zenodo.6496413 ].

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Acknowledgements

J.E.C. is supported by the Australian Research Council (CE140100020, FT160100047, DP110101540, and DP0877905). This work was undertaken as part of the Consultative Group for International Agricultural Research (CGIAR) Research Program on Fish Agri-Food Systems (FISH) led by WorldFish. T.D.E acknowledges support from the Natural Sciences and Engineering Research Council of Canada (RGPIN-2021-04319). M.C. and J.S. acknowledge support from the Spanish project ProOceans (RETOS-PID2020-118097RB-I00) and the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S) to the Institute of Marine Science (ICM-CSIC). G.G.G. acknowledges support from an Australian Research Council Discovery Early Career Research Award (DE210101918). C.M.P. acknowledges support from NOAA grants NA20OAR4310441 and NA20OAR4310442. M.C. acknowledges the financial support of Ministerio de Ciencia e Innovación, Proyectos de I+D+I (RETOS-PID2020-118097RB-I00, ProOceans) and the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S).

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Contributions

J.E.C. conceived of the study and hosted a workshop with G.G.G., A.D., and R.P. to operationalise the concept. J.E.C., G.G.G., R.P., J.K., N.J., A.R., R.L., A.W., and A.D. provided socioeconomic data. J.J., C.M., C.F., W.L. contributed crop model simulations. J.B., M.C., J.S., T.E., J.E., D.G., J.G., R.F.H., C.N., J.P.A., C.P., and D.T. contributed fisheries model simulations. L.T., J.J., R.F.H., T.E., and I.R.C. analysed the data and all authors contributed to the writing of the manuscript.

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Cinner, J.E., Caldwell, I.R., Thiault, L. et al. Potential impacts of climate change on agriculture and fisheries production in 72 tropical coastal communities. Nat Commun 13 , 3530 (2022). https://doi.org/10.1038/s41467-022-30991-4

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Coordinated development of high-quality agricultural transformation and technological innovation: a case study of main grain-producing areas, China

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The high-quality development of agriculture is closely related to technological innovation, but the evolutionary characteristics of the relationship between agricultural transformation and technological innovation have received little study. This study takes 13 main grain-producing areas of China as the research object. Data collection was from 2004 to 2019. Based on the coupling coordination and responsiveness models, we analyze the spatio-temporal agriculture comprehensive level and the associated response degree of agricultural transformation to technological innovation. The results showed that (1) the comprehensive development of technological innovation showed a growth trend, while the agricultural transformation showed a U-shaped growth trend; (2) the coordinated development of these two systems has been significantly improved, but there are differences in the development speed of each province; (3) the coordinated gravity center moved southward in the spatial pattern, eventually presenting the characteristics of “higher level in the east and lower level in the west, while the higher level in the south and lower level in the north”; (4) the influence of technological innovation on agricultural transformation gradually changed from inhibition to positive promotion. In the end, this paper puts forward suggestions on the high-quality development of agriculture from the relationship of technological innovation and agricultural transformation.

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The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

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The study is financially supported by Research on the High-Quality Development System of Manufacturing Industry in Chengdu-Chongqing Economic Circle under the New Development Pattern of Double Circulation (SC21ZDCY003).

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All authors contributed to the study’s conception and design. Investigation, methodology, and data collection were performed by Fumin Deng. Data collection, model building, and original draft writing were performed by Siyuan Jia. Methodology and model building were completed by Meng Ye. Zhi Li performed methodology, writing-review, and editing. In addition, all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Deng, F., Jia, S., Ye, M. et al. Coordinated development of high-quality agricultural transformation and technological innovation: a case study of main grain-producing areas, China. Environ Sci Pollut Res 29 , 35150–35164 (2022). https://doi.org/10.1007/s11356-021-18020-1

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Modern Development Of Agricultural Insurance In The Khabarovsk Territory

The article is devoted to certain aspects of agricultural development in the Khabarovsk territory and related issues of agricultural insurance. The analysis is based on data from the state statistics service, both on indicators of agricultural development in the Khabarovsk territory and on indicators of agricultural insurance in Russia. Due to the fact that the Khabarovsk region has difficult climatic conditions, most of the territory is covered with forests, and the temperature background changes sharply and often, the development of agriculture in this territory is quite labor-intensive and not always profitable. The products of regional agricultural producers are seriously competing with cheaper vegetables and fruits from border China. The development of crop and livestock production in the Khabarovsk territory is also hindered by limited financial support measures due to the deficit of regional and local budgets. The regional government understands the danger of dependence of the Khabarovsk region on external food imports, as well as the need to support the development of livestock and crop production in order to reduce the share of food dependence from outside. One of the mechanisms for supporting the agricultural sector is agricultural subsidies and insurance. The development and modernization of the domestic regional segment of food production, primarily related to the development of crop and livestock production, is one of the ways to improve the quality of life of the region's population and create additional jobs. Keywords: Agricultural insurance food security economic development

Introduction

The authors of the study set a goal: to consider the reasons for the weak development of agricultural insurance in the Khabarovsk territory. The problem statement was designated as: analysis of the problem of agricultural insurance development through the evaluation of its statistical indicators. In this regard, the research questions were: the study of modern legislative regulation of agricultural insurance in Russia; the importance of subsidizing the agricultural sector and the assessment of the components of the modern agricultural insurance market in Russia.

The purpose of the study is to determine whether voluntary agricultural insurance has a positive impact on the development of the agricultural sector in the region and on food security. The following research methods were used: analysis and synthesis, statistical methods.

The Russian agricultural insurance industry is experiencing the following difficulties:

•inability to form their own reserves, which is due to the low solvency of agricultural producers;

•the timing of insurance contracts coincides with the beginning of the sowing campaign, during which all financial resources of organizations are directed, first of all, to the formation of working capital that provides current activities (purchase of seeds, fertilizers, plant protection products, fuel and lubricants for machinery) (Buneeva & Tsydypova. 2019);

•presence of unscrupulous insurers on the market;

•high cost of insurance for agricultural producers;

•imperfection of the method of calculating the insurance cost and the amount of loss (death) of crops (animals) ( Vinokhodova, 2017 ).

The Russian government has already introduced support measures at the legislative level for both agricultural producers and insurance companies that insure agricultural production. The activities of the Russian Government include issues related to food security, the development of domestic enterprises in the field of crop production, animal husbandry, fishing, etc. Import-substitution, which includes it implements the direction of support for domestic food producers, in order to ensure stable food security in the country, is implemented in events in the regions of the Russian Federation. In close connection with the development of agriculture in the country the sphere of agricultural insurance is being modernized.

Problem statement: analysis of statistical indicators in the field of agricultural insurance

The authors of the study had the goal: to consider the reasons for the weak development of agricultural insurance in the Khabarovsk territory. Using statistical research methods and theoretical methods such as analysis and synthesis, the authors compared the values of official statistical indicators in the field of agricultural insurance. First of all, the authors calculated the ratio of statistical indicators of agricultural insurance in the Far East to the all-Russian values, as well as in the Khabarovsk territory to the all-Russian values.

Legislative regulation of agricultural insurance in Russia

Federal law No. 260-FZ of July 25, 2011 "On state support in the field of agricultural insurance and on amendments to the Federal Law "on the development of agriculture", which establishes state support for insurers in the framework of the execution of agricultural insurance contracts, as well as regulates the formation of reserves in this insurance industry: funds received by the Association of insurers from the investment of the compensation Fund and the exercise of the right to claim are directed to the replenishment of the compensation Fund; while not more than twenty-five percent of the funds received by the Association of insurers from investing the Fund for compensation payments can be sent by the Association of insurers in coordination with the Bank of Russia, authorized body and the Federal body of Executive power performing functions of elaborating state policy and normative-legal regulation in sphere of insurance activity, the financing of target programs of the Association of insurers on development of system of agricultural insurance, carried out with state support.

Modern agricultural insurance market in Russia

The insurance market in Russia has undergone many changes over the past 20 years, and agricultural production itself is associated with many risks. The trend observed over the past 7-10 years in the field of agricultural activity confirms that significant losses to agricultural producers from floods, droughts, fires, etc. 2017 and 2018 are characterized by the fact that the volume of premiums received by insurance companies under agricultural insurance contracts has decreased significantly in comparison with 2013 and 2014, i.e. with the period when the state indicated its line of support for the development of the agricultural sector and its component - agricultural insurance (table 1 ).

At the same time, if we consider the values in table 2 that reflect agricultural activity in the Khabarovsk territory, then in the period 2017-2018 there is a decrease in the index of production. The index of production is "lagging" from the costs of agricultural production, including the costs associated with insurance. The data in table 3 according to Rosstat generally reflect that the profitability of sold goods, products (works, services) of organizations for agriculture, forestry, hunting, fishing and fish farming in the Khabarovsk territory is not only negative, but also lower than in the far Eastern Federal district ( The Regions Of Russia, Socio-economic indicators, Stat. sat. Rosstat, M., 2019 ).

The same indicator for Russia is generally positive. Of course, the Khabarovsk region, particularly its Northern areas are characterized by very severe weather conditions, it is impossible to relate fully with the climate in the Central part of Russia or the southern areas (in the Primorye territory and the Amur region climatic conditions are softer). The territory of the Khabarovsk region and more not only in size but also more forested, and poor transport accessibility especially in Northern areas of the region, poorly developed infrastructure in many areas, all these conditions slow down the development of agriculture.

Agriculture of the Khabarovsk region needs not only subsidies and subsidies, but also the involvement of specialists in its development: specialists in the field of livestock and crop production, agronomists, engineers of greenhouses, etc., in the region, many specialists are not trained in higher and secondary vocational education programs – there are no employment opportunities and attractive conditions for it. At the same time, the region is very dependent on imported milk, meat, and many vegetables and fruits at lower prices, mainly from China. In other words, the products that are produced in the Khabarovsk region are not always competitive, or even profitable, compared to similar agricultural products from China, Turkey, or from neighboring regions-the Primorye territory and the Amur region.

Research questions: significance of subsidizing agricultural insurance contracts for producers of the Khabarovsk territory

Food security in the Khabarovsk territory is low, despite the fact that the share of unprofitable agricultural enterprises is 51%, this figure is higher than in the whole of the far Eastern Federal district, and in Russia as a whole. State support measures are needed for the development of agricultural enterprises in the Khabarovsk territory, so that the region can independently cover its needs for basic food products by 75-80 percent. Data provided on the website of the Ministry of agriculture, trade, food and processing industry of the Khabarovsk territory: production of the main types of livestock products in farms of all categories is still low, which reflects statistical data ( Official data of the Federal state statistics service, 2020a ; Official data of the Federal state statistics service, 2020b ; The Regions Of Russia. Socio-economic indicators, 2019 ).

Importance of agricultural subsidies in the industry

Subsidies that are provided in order to reduce the possible loss of income in the production of crop and livestock products in cases of loss (death) of crops and farm animals (Official website of the Ministry of agriculture, trade, food and processing industry of the Khabarovsk territory, 2020). Conclusion agricultural producers of the region contracts of agricultural insurance, the relevant law defined the rules for granting and distribution of subsidies from the Federal budget to budgets of subjects of the Russian Federation for compensation of part of expenses of agricultural producers for payment of insurance premiums for agricultural insurance contracts are applied in the Khabarovsk territory. Unfortunately, there is no information on concluded agricultural insurance contracts in the Khabarovsk territory, the share of subsidies allocated, and how these measures affected the profitability of producers (Official website of the Ministry of agriculture, trade, food and processing industry of the Khabarovsk territory, 2020).

The entry into force of agricultural insurance contracts and payment by the agricultural producer of the territory of 50 percent of the accrued insurance premium under these contracts; ensuring the average monthly salary of one employee is not lower than the minimum wage established by Federal law – are the main reasons for the payment of subsidies to producers. Subsidies are provided for reimbursement of part of the costs of agricultural producers of the region to pay insurance premiums under agricultural insurance contracts as a result of insurance events. The granting of subsidies is declarative (Official website of the Ministry of agriculture, trade, food and processing industry of the Khabarovsk territory, 2020).

Indicators of agricultural development in the Khabarovsk territory in 2018-2019

According to the government of the Khabarovsk territory, the following values were achieved in the agricultural sector in the period 2018-2019. In January-November 2019, in the Khabarovsk region, the production of agricultural products in farms of all categories at current prices amounted to 14,017. 1 million rubles (83.7 % compared to the level of January – November 2018) (Official website of the Ministry of agriculture, trade, food and processing industry of the Khabarovsk territory, 2020; Decree Of the government of the Khabarovsk territory of March 20, 2012 N 66-PR "Procedure and conditions for providing subsidies from the regional budget for reimbursement of part of the costs of agricultural producers of the Khabarovsk territory for the payment of insurance premiums under agricultural insurance contracts", 2020).

In January-November 2019, the region produced 1,779 tons of protected ground vegetables, including: cucumbers – 756 tons, tomatoes – 483 tons, and other-540 tons (Official website of the Ministry of agriculture, trade, food and processing industry of the Khabarovsk territory, 2020; Decree Of the government of the Khabarovsk territory of March 20, 2012 N 66-PR "Procedure and conditions for providing subsidies from the regional budget for reimbursement of part of the costs of agricultural producers of the Khabarovsk territory for the payment of insurance premiums under agricultural insurance contracts", 2020).

In 2019, the following investment projects are being implemented in the agricultural sector of the Khabarovsk territory (Official website of the Ministry of agriculture, trade, food and processing industry of the Khabarovsk territory, 2020; Decree Of the government of the Khabarovsk territory of March 20, 2012 N 66-PR "Procedure and conditions for providing subsidies from the regional budget for reimbursement of part of the costs of agricultural producers of the Khabarovsk territory for the payment of insurance premiums under agricultural insurance contracts", 2020):

•Construction of a 10.3 ha greenhouse complex of JGC evergreen LLC;

•Reconstruction of the greenhouse complex of LLC "agro-industrial complex Vostok" with an area of 6.0 ha;

•Construction of a greenhouse complex for year-round production of green crops K(f)X Butkov V. B. with an area of 0.75 ha;

•Construction of a pig breeding complex for the production of up to 70,000 heads per year in the Khabarovsk territory of scifagro-DV LLC»;

•Construction and reconstruction of a highly efficient agro-industrial enterprise for 700 head of dairy cattle. Construction of a grain dryer, reconstruction of a granary for the purpose of deep processing of grain LLC "Vector" s. Kiinskoe, district. Lazo;

•Creation of seed production for growing and processing soy seeds in the Khabarovsk territory, LLC " Sporos»;

•Construction of a livestock complex for 2,000 head of dairy cattle, a dairy plant with a capacity of up to 21.0 thousand tons of dairy products per year, LLC " green agro-Khabarovsk";

•Construction of a pig breeding complex with a capacity of 945 tons of meat per year, LLC "green Star-2" in the Lazo district;

•Development of specialized beef cattle breeding with a capacity of 130 tons of meat per year. Cultivation of agricultural crops necessary for feeding cattle, LLC " SHP "Kolos" [9, 11].

There is no information about the current level of implementation of these projects. But they have state support.

Agricultural enterprises in modern conditions of development of the Khabarovsk region without the state financial support cannot exist that is associated with many factors: the remoteness of farmland, not well-developed transport infrastructure, both within the region and in the far Eastern Federal district (in regions still not developed a unified system of coordination of transit, because in the chain of decision-making involved enterprises of different ownership forms and departmental affiliation, authorities, and between them there is no single established mechanism of interaction); quite high energy tariffs (even despite all measures of state support to reduce them in the far East); lack of developed agricultural machinery in the region (all agricultural machinery is imported either from the countries of the Asia-Pacific region or from the Western regions of Russia); lack of non-state financial investments, including through the mechanism of agricultural insurance.

Conclusions

Currently, mandatory and property insurance is actively developed in Russia and in the Khabarovsk territory, the share of voluntary agricultural insurance is small, food security is weak in the region, the quality of life of the population is lower than the average in Russia, strong negative migration processes (the outflow of population from the region has not stopped since the 1990s, the last ten years have only slowed its pace), the lack of specialists for priority economic areas, including agriculture and the insurance sector.

Insurance activity in the Khabarovsk region is mainly represented by large Russian companies

Insurance activities in the Khabarovsk region are mainly represented by branches of large Russian companies, with registration in Moscow and Saint Petersburg. Regional insurance companies do not compete with them, they leave the market, so the main financial flows in insurance (namely, in collecting insurance premiums) fall on the Western region of Russia.

The Main financial centers, including in the insurance sector, are located in Moscow

Almost all of the share collected from insurance premiums is reallocated in Moscow, due to the fact that the legal addresses of most major insurance companies and their main offices are located in Moscow. Comparing statistical reports, it can be seen that for Moscow, the share of collected insurance premiums is 89% of the national value, and the share of payments for all insurance contracts, including both mandatory and optional, personal and property-29% of the national value; regarding agricultural insurance, due to the fact that its share in the far Eastern Federal district is too small, there is no information about it in open statistical reports (Review of insurers ' activities in 2018. Official data of the Federal state statistics service, 2020; Russian Insurance market:…, 2020 ; Statistical indicators and information…, 2020 ; Central Bank Of The Russian Federation, 2020 ).

The Khabarovsk region has a weak food self-sufficiency: the share of food produced within the territory is small, and food imports from neighboring regions are high

Thus, for a more detailed study of the features of agricultural insurance in the Khabarovsk region, additional scientific research and statistical measurements in this area are necessary; it is necessary to conduct an additional analysis of the food security of the region.

Agricultural insurance in the Khabarovsk territory is almost not developed, and additional research is needed on the mechanisms for developing This type of insurance in the region

In general, problems of development of the Khabarovsk territory: the outflow of population, poor transportation infrastructure in the region and in the whole of Far Eastern Federal district, the lack of the Far Eastern Federal district in Khabarovsk region of uniform system of coordination of transit, lack of agricultural machinery in the region, the lack of specialists in the sphere of agricultural insurance and agriculture in General, low levels of private investment in agriculture. These and other aspects negatively affect the development of agriculture in the Khabarovsk territory and its food security. State programs for subsidizing agricultural insurance contracts exist, but they do not allow solving all the problems in this industry, i.e. a comprehensive system solution is needed to solve the existing difficulties in the development of agriculture in the Khabarovsk region and its component – agricultural insurance. In addition, the increase in agricultural production in the territory of the region will create additional jobs, increase trade with neighboring regions. The development of agricultural insurance will help reduce risks in the agricultural sector, increase its investment, and therefore become one of the factors for its successful development.

Acknowledgement

This work was supported by Far-East Institute of management, branch of the Russian Presidential Academy of National Economy and Public Administration (hereinafter RANEPA), Department of mathematical methods and information technologies, Khabarovsk 680000, Russia.

• Buneeva, M. A., & Tsydypova, A. V. (2019). Problems of development of agricultural insurance of agricultural crops in the Republic of Buryatia. Topical issues of the development of the agricultural sector of the Baikal region: materials of the scientific and practical conference dedicated to the Day of Russian science: Ulan-Ude: Buryat state agricultural Academy named after V. R. Filippov, 180-183.
• Central Bank Of The Russian Federation. (2020). http://www.cbr.ru/ (accessed: 16.03.2020).
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• Procedure and conditions for providing subsidies from the regional budget for reimbursement of part of the costs of agricultural producers of the Khabarovsk territory for the payment of insurance premiums under agricultural insurance contracts. (2020). Decree of the government of the Khabarovsk territory, 66-PR (2012, 20 March 20). Resolutions of the government of the Khabarovsk territory, 320-PR (2013, 7 October), 347-PR (2014, 25 September). https://minsh.khabkrai.ru/Deyatelnost/Selskoe-hozyajstvo/9 (accessed: 11.05.2020).
• Review of insurers activities in 2018. (2020). Official data of the Federal state statistics service. Retrieved from https://www.gks.ru/storage/mediabank/strah-org_n.htm (accessed: 14.03.2020).
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https://doi.org/10.15405/epsbs.2021.03.29

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Digital economy, cybersecurity, entrepreneurship, business models, organizational behavior, entrepreneurial behavior, behavioral finance, personnel competencies

Cite this article as:

Puinko, L. Y., & Tcvetova, G. V. (2021). Modern Development Of Agricultural Insurance In The Khabarovsk Territory. In N. Lomakin (Ed.), Finance, Entrepreneurship and Technologies in Digital Economy, vol 103. European Proceedings of Social and Behavioural Sciences (pp. 218-226). European Publisher. https://doi.org/10.15405/epsbs.2021.03.29

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Rapid evaluation of the Australian Pesticides and Veterinary Medicines Authority’s structure and governance

The Australian Pesticides and Veterinary Medicines Authority (APVMA) is the independent statutory authority responsible for the regulation of agricultural chemicals and veterinary medicines in Australia. 

The purpose of the Rapid Evaluation

The Final Report on Future Structure and Governance Arrangements for the Australian Pesticides and Veterinary Medicines Authority (APVMA) (Rapid Evaluation) was commissioned by the government following an independent review by the law firm Clayton Utz. 

Mr Ken Matthews AO was engaged to complete an independent rapid evaluation of the Clayton Utz findings and to advise on future structure and governance arrangements for the APVMA. The Rapid Evaluation makes 33 recommendations aimed at improving governance, organisational capacity, regulatory performance, and cultural shortcomings of the APVMA.

• Final Report on Future Structure and Governance Arrangements for the Australian Pesticides and Veterinary Medicines Authority (APVMA) [572 KB]
• Final Report on Future Structure and Governance Arrangements for the Australian Pesticides and Veterinary Medicines Authority (APVMA) [217 KB]

If you have difficulty accessing this file, visit web accessibility for assistance.

Preliminary government response

The preliminary government response to the Rapid Evaluation is available below. You can also download the response in PDF.

The preliminary response indicates whether the government supports, supports in principle, partially supports, or does not support each of the 33 recommendations. 

A detailed response will be issued by the government in mid-2024 and will address each recommendation in greater detail, as well as addressing issues identified in earlier reports regarding the APVMA.

Preliminary government response to the Rapid Evaluation (PDF 278 KB)

Preliminary government response to Final Report on Future Structure and Governance Arrangements for the Australian Pesticides and Veterinary Medicines Authority (APVMA)

The Final Report on Future Structure and Governance Arrangements for the Australian Pesticides and Veterinary Medicines Authority (APVMA) (Rapid Evaluation) was commissioned by the government following an independent review by the law firm Clayton Utz. Clayton Utz identified very serious matters that suggested a high risk of systemic administration and governance problems, including suggestions of potential industry capture, that may have contributed to the APVMA not performing its full regulatory responsibilities. Both reports note the relocation of the APVMA to Armidale disrupted and exacerbated issues with the organisational culture, staff capability, performance of the APVMA while also presenting new recruitment challenges. 

Ken Matthews AO was engaged to complete an independent rapid evaluation of the Clayton Utz findings and to advise on future structure and governance arrangements for the APVMA. The Rapid Evaluation makes 33 recommendations aimed at improving governance, organisational capacity, regulatory performance, and organisational culture shortcomings of the APVMA. 

The government’s commitment to an independent, capable, and high-performing APVMA

An effective APVMA supports Australia’s thriving agricultural industry and makes a vital contribution to the protection of Australia’s people, our environment and economy through access to less harmful and more targeted chemistries and tailoring pesticide use. 

The government is committed to ensuring the APVMA is a capable, high performing agency operating independently of government supported by robust governance, a sustainable funding base, fit for purpose performance indicators and stakeholder engagement processes and a healthy workplace culture.

Taking immediate action on key recommendations

The government is releasing the Rapid Evaluation and taking immediate action on key recommendations. The government supports the APVMA as an independent statutory authority and does not support abolishing either the APVMA or its Board (Recommendations 1-7). 

The government rejects the recommendation to relocate the APVMA back to Canberra although the government does support the recommendation to repeal the Government Policy Order (GPO) dictating the location of the APVMA (Recommendation 11). This will allow the APVMA Board and CEO to make decisions on staff and office locations that best suit the authority’s operational needs.  Repealing the GPO will bring the APVMA into line with other Commonwealth agencies and APS standards by enabling the agency to make decisions on staff and office locations that best suit their operational needs and will assist in recruitment efforts. 

These immediate actions enshrine APVMA’s independence and provide certainty to stakeholders and assurance to staff on the APVMA’s future. 

Action already underway in response to findings

The APVMA has already taken steps to address the findings raised in recent reviews. The APVMA is making progress in line with a Ministerial Direction issued to expedite eight long-running chemical reconsiderations and a Ministerial Statement of Expectations requiring improvements to workplace culture, governance, transparency, accountability and engagement. 

With new interim leadership, there is clear strategic direction outlined in the APVMA Strategy 2030, relationships between the Board and CEO are clarified with the updated Board Charter and improved engagement with their employees. The government commends the APVMA for the work it has done so far in addressing the findings of recent reviews and improve its operations. We can already see progress in rebuilding the APVMA.

Further consideration and engagement will occur 

The Rapid Evaluation makes a number of recommendations – for example, concerning improvements to the quality and integrity of scientific decision making, the development of more balanced performance indicators, and adjustments to the APVMA’s funding base – that are inherently more complex and require further consideration and engagement. 

This preliminary response indicates whether the government supports, supports in principle, partially supports, or does not support each of the 33 recommendations. 

A detailed response will be issued by the government in mid-2024 and will address each recommendation in greater detail, as well as addressing issues identified in earlier reports regarding the APVMA. The detailed response will comprehensively outline the government’s reform agenda for the APVMA, including reforms already underway, additional policy analysis and research, and future consultation processes with stakeholders to develop policy positions for consideration by the government. The purpose of the detailed response is to demonstrate the government’s commitment that the high performance and operational stability of the APVMA is restored and sustained into the future.

Recommendations of the Final Report on Future Structure and Governance Arrangements for the Australian Pesticides and Veterinary Medicines Authority (APVMA)

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Petunia varieties bring color to spring gardens.

Spring is my favorite season of the year as I enjoy the return of warmer days and the colorful blooms in gardens and landscapes. Among the many flowering plants that grace the spring landscape, petunias stand out as one of my favorites.

As nature bursts into life, now is the perfect time to visit your local nurseries and garden centers to acquire bright and colorful petunias.

One standout variety that captures the essence of spring is the Cascadias Pitaya petunia.

Sporting a vigorous and well-branched habit, this delightful plant cascades gracefully over the edges of baskets and pots, creating a stunning visual display. Its trumpet-shaped flowers are a mesmerizing dark pink with intricate dark purple veins and throats -- truly a sight to behold. Flower petal edges are a vibrant lime green, further enhancing its appeal.

For those seeking a touch of celestial magic in their garden, I recommend the Night Sky petunia. Its deep purple petals are speckled with variable white dots reminiscent of stars against a night sky.

Equally captivating is the Easy Wave Rose Fusion petunia, with its pink rose-colored flowers adorned with striking dark pink veins. This variety exudes elegance and charm, making it a standout addition to any garden or container arrangement.

Beyond their aesthetic appeal, petunias are prized for their resilience and adaptability. They thrive in both cool and hot temperature extremes, making them perfect for brightening up outdoor spaces throughout the spring, summer and fall months.

With proper care and attention, petunias will reward you with a profusion of vibrant blooms, bringing color to your outdoor oasis.

To ensure optimal growth and bloom production, it is essential to provide petunias with the right care.

In containers or hanging baskets, plant them in a well-drained potting mix and feed them with a liquid fertilizer every two to three weeks during the summer months. If planting in the ground, ensure the soil is well-drained.

Make sure you put your petunias in a location that receives at least 6 to 8 hours of sunlight each day. They thrive in full sun, although they can tolerate partial shade, especially in hot climates.

Deadhead spent flowers regularly to encourage continuous blooming and maintain the plant’s appearance.

If my plants begin to look open or a bit tired, I give them an all-over trim. I use a sharp pair of scissors or pruning shears to trim back up to one-third of the volume of the plant.

After any trim, use a water-soluble fertilizer to provide instant energy to help kickstart new growth, branching and flowering. The plants take about a week to recover from the trim, but the result is fuller plants with more blooms going forward. Repeat the trim as needed throughout the growing season.

Whether adorning hanging baskets, containers or garden beds, petunias are sure to elevate your outdoor space and inspire admiration from all who see them. So, embrace the beauty of spring and let some of these exquisite petunias add a touch of magic to your garden landscape.

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Dr. Eddie Miles Louis Smith

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