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Management of crop residues for improving input use efficiency and agricultural sustainability.

1. Introduction

2. intensive agriculture and crop residues: present status, 3. environmental impacts from inefficient use of crop residues, 4. management of crop residues for improving input use efficiencies, 4.1. improving soil physical properties, 4.2. improving soil chemical properties, 4.3. improving soil microbial activity, 4.4. improving soil productivity, 5. crop residues improve the fertility and productivity of soil, 5.1. crop residue management for soil organic matter, 5.2. effects of crop residues on soil nutrient status and its availability, 5.3. effects of crop residues on soil productivity, 6. crop residue management decreases soil degradation, 6.1. erosion, 6.2. salinity, 6.3. buffering capacity, 6.4. decrease in soil aridity, 6.5. maintaining soil temperature, 7. crop residue management improves the resource use efficiency of rice–wheat systems, 7.1. residue management for soil resources, 7.2. residue management for water resources, 7.3. residue management for environmental resources, 8. conversion technologies for sustainable crop residue management, 8.1. thermochemical transformation, 8.1.1. gasification, 8.1.2. pyrolysis, 8.1.3. liquefaction, 8.2. biochemical transformation, 8.2.1. anaerobic digestion, 8.2.2. alcoholic fermentation, 8.2.3. photo-biological techniques, 8.3. conversion of crop residues into bioelectricity, 9. constraints of crop residue management in rice–wheat systems, 10. conclusions and future directions, author contributions, conflicts of interest, abbreviations.

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Sarkar, S.; Skalicky, M.; Hossain, A.; Brestic, M.; Saha, S.; Garai, S.; Ray, K.; Brahmachari, K. Management of Crop Residues for Improving Input Use Efficiency and Agricultural Sustainability. Sustainability 2020 , 12 , 9808. https://doi.org/10.3390/su12239808

Sarkar S, Skalicky M, Hossain A, Brestic M, Saha S, Garai S, Ray K, Brahmachari K. Management of Crop Residues for Improving Input Use Efficiency and Agricultural Sustainability. Sustainability . 2020; 12(23):9808. https://doi.org/10.3390/su12239808

Sarkar, Sukamal, Milan Skalicky, Akbar Hossain, Marian Brestic, Saikat Saha, Sourav Garai, Krishnendu Ray, and Koushik Brahmachari. 2020. "Management of Crop Residues for Improving Input Use Efficiency and Agricultural Sustainability" Sustainability 12, no. 23: 9808. https://doi.org/10.3390/su12239808

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Impact of crop residue management on crop production and soil chemistry after seven years of crop rotation in temperate climate, loamy soils

Marie-pierre hiel, sophie barbieux, jérôme pierreux, claire olivier, guillaume lobet, christian roisin, sarah garré, gilles colinet, bernard bodson, benjamin dumont.

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Corresponding author.

Received 2017 Oct 3; Accepted 2018 May 4; Collection date 2018.

This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.

Society is increasingly demanding a more sustainable management of agro-ecosystems in a context of climate change and an ever growing global population. The fate of crop residues is one of the important management aspects under debate, since it represents an unneglectable quantity of organic matter which can be kept in or removed from the agro-ecosystem. The topic of residue management is not new, but the need for global conclusion on the impact of crop residue management on the agro-ecosystem linked to local pedo-climatic conditions has become apparent with an increasing amount of studies showing a diversity of conclusions. This study specifically focusses on temperate climate and loamy soil using a seven-year data set. Between 2008 and 2016, we compared four contrasting residue management strategies differing in the amount of crop residues returned to the soil (incorporation vs. exportation of residues) and in the type of tillage (reduced tillage (10 cm depth) vs. conventional tillage (ploughing at 25 cm depth)) in a field experiment. We assessed the impact of the crop residue management on crop production (three crops—winter wheat, faba bean and maize—cultivated over six cropping seasons), soil organic carbon content, nitrate ( NO 3 − ), phosphorus (P) and potassium (K) soil content and uptake by the crops. The main differences came primarily from the tillage practice and less from the restitution or removal of residues. All years and crops combined, conventional tillage resulted in a yield advantage of 3.4% as compared to reduced tillage, which can be partly explained by a lower germination rate observed under reduced tillage, especially during drier years. On average, only small differences were observed for total organic carbon (TOC) content of the soil, but reduced tillage resulted in a very clear stratification of TOC and also of P and K content as compared to conventional tillage. We observed no effect of residue management on the NO 3 − content, since the effect of fertilization dominated the effect of residue management. To confirm the results and enhance early tendencies, we believe that the experiment should be followed up in the future to observe whether more consistent changes in the whole agro-ecosystem functioning are present on the long term when managing residues with contrasted strategies.

Keywords: Residue, Straw, Tillage, Crop production, Total organic carbon, Soil quality

Introduction

Once a crop is harvested, farmers have to decide what to do with the remaining crop residue (the above ground biomass that is cut but not harvested). Residues can be either exported and valorised as co-products (e.g., animal fodder, biogas production), or restored to the soil as such or after being burnt. Returning straw directly to the field has been promoted as a source of organic matter and a way to increase soil water holding capacity and its overall quality. As such, it is thought to help maintain, or even to some extent restore, soil fertility ( Lal et al., 2004 ). If the residues are returned to the soil, farmers have to choose how to manage them using either conventional tillage or alternatives such as reduced tillage. We define conventional tillage as a tillage based on mouldboard ploughing which is commonly used in temperate regions and reduced tillage as a tillage with reduced intensity and/or depth ( Hiel et al., 2016 ; the practical implementation of these techniques are specified in Table S1 ).

The precise impact of the restitution (or not) of residues and of the choice of tillage system to apply to the soil-plant system remains unclear and seems to be highly dependent on the pedo-climatic conditions (soil structure, moisture, macro fauna, etc.) ( Powlson et al., 2011 ). For instance, soil organic carbon (SOC) generally seems to slightly increase if residues are returned to the soil, particularly in the long term ( Chenu et al., 2014 ; Autret et al., 2016 ; Merante et al., 2017 ). However, the actual quantification of straw incorporation effect on soil organic carbon stocks shows conflicting results, as synthetized by Poeplau et al. (2015) , with studies reporting SOC losses, SOC stabilization or even non-significant or negligible impact. The effect of tillage on SOC content is less clear. While some studies show an increase of SOC with reduced or no-tillage ( Arrouays et al., 2002 ; Smith, 2007 ; Garcia-Franco et al., 2015 ), others report no effect ( Dick, 1983 ; Dolan et al., 2006 ; Dikgwatlhe et al., 2014 ).

As Hiel et al. (2016) show in their review, the impact of crop residue management on crop performance is also contradictory in the existing literature. The presence of residues seems to be detrimental to crop germination as they can form a physical obstacle for seedlings ( Arvidsson, Etana & Rydberg, 2014 ), can create a cold and humid micro-climate around the seed ( Soane et al., 2012 ) and provide a favourable habitat for slugs ( Christian & Miller, 1986 ) and plant pathogens ( Arvidsson, Etana & Rydberg, 2014 ). In general, the literature show that weather conditions are the main factor influencing crop yields ( Linden, Clapp & Dowdy, 2000 ; Dam et al., 2005 ; Soon & Lupwayi, 2012 ), and sometimes an interacting explanatory factor is the residue management ( Riley, 2014 ). Residue retention tends to induce lower yields under wet weather conditions (effect on diseases and pests) ( Riley, 2014 ) and higher yields in dry conditions (effect on water retention capacity) ( Linden, Clapp & Dowdy, 2000 ; Riley, 2014 ). There are also several studies reporting no effect on crop yields ( Dam et al., 2005 ; Soon & Lupwayi, 2012 ; Riley, 2014 ; Brennan et al., 2014 ). Some specific results show that it is important to have the information on the entire management type (i.e., residue in or out, type of tillage, tillage depth and timing, …) in order to be able to assess the impact of the management on crop performance. Van den Putte et al. (2010) showed for example that residue retention of winter cereals and maize, combined with reduced tillage reduces yields in Europe. On the other hand, Blanco-Canqui & Lal (2007) have shown that residue removal can impede crop yield.

The literature on the effect of residue management on nitrogen (N) or phosphorus (P) uptake by plant is equally dispersed with no (for N:  Brennan et al., 2014 ); positive (for N:  Malhi et al., 2011 ; for P: Noack et al., 2014 ) or negative (for N:  Soon & Lupwayi, 2012 ; for P:  Damon et al., 2014 ) effects reported by different authors. These differences are generally attributed to differences in soil texture and/or initial nutrient status or residue quality ( Kumar & Goh, 1999 ; Chen et al., 2014 ).

Interactions between crop residue management and the soil-water-plant system are complex and inherently depend on the pedo-climatic conditions. Local assessment and system approach are therefore necessary to come to relevant guidelines for residue management under specific pedo-climatic conditions. The objective of our study was therefore to determine the effects of contrasting crop residue management strategies on crop production and components of the soil fertility, over a period of several years. Regarding crop production, we studied how residues management strategy impacts on germination rate, biomass production and yield elaboration, along with N, P and K exportation. The soil fertility components that were dynamically followed were SOC, N, P and K contents and their repartition within the soil profile. The experiment was conducted in the loam belt under temperate climatic conditions, taking into account common crop rotations and local farming practices.

Materials and Methods

Site description.

The field experiment (50°33′49.6″N, 4°42′45.0″E) was established on 1.7 ha of the experimental farm of Gembloux Agro-Bio Tech, University of Liège, Belgium in 2008 and yield measurements started in 2010. The soil is a Cutanic Luvisol ( IUSS Working Group WRB, 2014 ). According to the Walloon soil map ( WalOnMap, 2018 ), the soil was silty with favourable natural drainage, containing 70–80% of silt, clay content of 18–22% of clay and 5–10% of sand. A characterization of the spatial variability of certain chemical parameters was carried out in 2011 (maps available in Fig. S1 ). Descriptive statistics is presented in Table 1 .

Table 1. Descriptive statistics of soil fertility indicators within the experimental fields on the 0–30 cm soil layer.

number of sample

standard deviation

coefficient of variation

The climate is temperate (Cfb in Köppen–Geiger classification ( Peel, Finlayson & McMahon, 2007 ) with 819 mm average annual rain and 9.8 °C annual average temperature. Weather data were measured in a federal weather station located in Ernage (Belgium’s Royal Meteorological Institute), at 2.4 km from field site. An overview of monthly temperature and rainfall during the experimental period is shown in Figs. S2 and S3 .

Experimental design and treatments

The field was designed as a Latin square disposal with four replications. Each plot was 15 m wide and 40 m long. Crop residue management was defined as the combination of two practices: (i) the fate of the crop residue and (ii) the type of tillage. Firstly the residue fate can be restitution (IN) or exportation (OUT). It has to be noted that stubble and chaff are always left on the fields, even if the rest of the residue is exported. Secondly, we considered two tillage types (see Table 2 ): conventional (CT, 25 cm depth) or reduced (RT, 7–10 cm depth). The different combinations of these two aspects of residue management resulted in four treatments: CT-IN, CT-OUT, RT-IN, RT-OUT.

Table 2. Comparison of conventional and reduced tillage treatments.

The crop rotation during the experiment was: rapeseed ( Brassica napus ) in 2008–09, three consecutive years of winter wheat ( Triticum aestivum ) in 2009–10, 2010–11 and 2011–12, mustard ( Sinapis alba ) cover crop in 2012–13, faba bean ( Vicia Faba ) in 2013, winter wheat in 2014, oats ( Avena sativa ) and peas ( Pisum sativum ) mixed as cover crop in 2014–15, and finally maize ( Zea mays ) in 2015. Sowing densities were 300 kernels/m 2 for winter wheat, 50 kernels/m 2 for faba bean, and 13 kernels/m 2 for maize. Sowing process is detailed for each crop in Table S1 .

N fertilisation (liquid N, UAN at 39%) followed the regional standards depending on the type of crop. Rapeseed received two applications (at stem elongation stage: 31 and 32–50 on BBCH scale ( Meier et al., 2009 ) with a total of 160 kg of N/ha. Three applications were provided to winter wheat (at tillering, stem elongation and flag leaf stage (26, 30 and 37–39 on BBCH scale)) with a total of 180 kg of N/ha. Faba bean was not fertilised and maize crop was fertilised with 120 kg of N/ha before sowing. There was no external addition of P or K. Crop protection measures corresponded to the regional standards.

The detailed crop protocols (crop management, crop harvest, residue exportation, soil tillage, fertilization and crop protection treatments) are available in Table S1 .

Crop sampling and analyses

We monitored the germination rate and growth dynamics during the season with an adapted protocol for each crop type ( Table 3 ). The determination of the germination rate consisted in counting the number of seedlings on a definite area ( Table 3 ). To quantify above-ground biomass, plants were collected (according to crop protocol in Table 3 ) and their different parts (shoot and ears, pods or cobs) were separated, counted and oven-dried at 60 °C for 72 h. Grain yield was assessed with an experimental harvester adapted to each crop by one passage per plot (40 m long on a width dependent on the harvester, see specific crop protocol in Table 3 ). To quantify the amount of remaining crop residues on the field, residues (i.e., OUT plots: stubble and chaff, IN plots: all residue) were collected over a surface of 0.5 m wide and 2 m long immediately after harvest, dried, weighed. These samples were also used to quantify the NPK content of the remaining crop residues. Composite grain samples (maize and wheat grains; faba been seeds) of 1 kg were prepared from the harvest hopper (one sample per plot) for quantification of NPK grain content. Both grains and residues were crushed before analysis. N content was measured using the Kjeldahl method ( Bradstreet, 1965 ). Phosphate and potassium (K) levels in plants were measured using a modified protocol of Zasoski & Burau (1977) . Samples were first treated by a concentrated acid mix of HNO 3 and HClO 4 (1:1) (15 ml per g of sample). K content was measured by a flame atomic absorption spectrometric method (Spectrometer Varian 220). P was measured by colourimetry with molybdate and ammonium vanadate at 430 nm (Nanocolor UV/VIS; Macherey-Nagel, Duren, Germany). NPK content (kg/ha) were calculated by multiplying the nutrient content (%) by the biomass of the residue or grain (kg/ha).

Table 3. Details of crop specific measurement protocols.

Soil sampling and analyses

Twice a year around April and October, we took ten soil subsamples (with a gouge auger of 2 cm diameter) to form a composite sample per plot at 0–10 cm, 10–20 cm and 20–30 cm depth. The fall sampling was usually either made after spring crop harvest and before winter wheat sowing or after winter wheat harvest and cover crop sowing. The spring sampling was made when climatic conditions were again favourable for winter wheat growth or after spring crop sowing. TOC was determined on a 1g of dry soil (ground at 200 µm) by the Walkley-Black method ( Blakemore, 1972 ): oxidation with K 2 Cr 2 O 7 and H 2 SO 4 ; titration of the excess of K 2 Cr 2 O 7 with Mohr Salt ((NH 4 ) 2 Fe(SO 4 ) 2 ⋅ 6H 2 O). Available soil nutrients were measured by stirring a 10 g sample of soil (air-dried and sieved at 2 mm) for 30 min in 50 ml of solution (C 2 H 7 NO 2 0.5 M and EDTA 0.02 M at pH 4.65 ( Lakanen & Erviö, 1971 )). After filtration, for the cations measurement, atomic emission was used for K while P was determined by colourimetry (colour reaction of Murphy & Riley, 1962 , Nanocolor UV/VIS; Macherey-Nagel, Duren, Germany).

In addition to the two overall soil sampling campaigns per growing season, soil nitrate content was measure more frequently to catch the dynamic of uptake during the growing phase of the main crops. Composite humid soil samples based on eight subsamples (sampled with gouge auger of 2 cm diameter) were used per plot at three depths: 0–30 cm, 30–60 cm and 60–90 cm. We used KCl extraction and a colourimetry method of reduction of nitrate to nitrite (using Cadmium or Hydrazine) with a determination of nitrite ions by the modified Griess-Ilosvay reaction ( Bremner, 1965 ; Guiot, Goffart & Destain, 1992 ).

Statistical analyses

Statistical analyses were performed with R software ( R Core Team, 2015 ). The statistical analyses were systematically applied to assess the effect of crop residue management on crop and soil measurements, as follows. First, a 2-way ANOVA was performed, including the soil tillage and residue fate as fixed factors (with interaction) and the plot position (line and columns of the Latin square) as random factors. In case no interaction was highlighted between the fixed factors, we compared on the one hand, IN versus OUT treatments, and, on the other hand, RT versus CT treatments. These comparisons were then immediately made on the basis of the results of the 2-way ANOVA test. Contrarily, when an interaction between the fixed factors was significant, the four treatments (CT-IN, CT-OUT, RT-IN and RT-OUT) were intercompared and ranked using a post-hoc test (Student-Newman-Keuls—SNK). Analyses of variance (2-way ANOVA) and SNK tests were performed with the agricolae package ( Mendiburu & Simon, 2015 ). The conditions of application of the ANOVA test (normality of the distribution and homoscedasticity) were systematically checked on the residuals of the ANOVA, using respectively a Shapiro–Wilk test and a Bartlett test.

To study the evolution of soil parameters over the years, a linear mixed effects model was fitted using the lme4 package ( Bates et al., 2014 ). To evaluate possible difference between treatments on the entire profile or per depth, the model was used with soil tillage and residue fate and their interaction as fixed factors, while dates and plots were random effects. To estimate whether stratification occurred in the soil parameters per crop residue management treatment, the mixed effects model was used with the depths as a fixed factor and plots and dates as random effects. A student’s T -test was used to test for each treatment whether the soil factors of the last sampling year and the first sampling year were significantly different.

Management of crop residues

After seven crop rotations, the total amount of crop residue returned to the soil was on average 52% higher (i.e., +28.8 t/ha) for IN plots (55.7 t/ha) compared to OUT plots (26.8 t/ha) ( Table 4 and Table S2 for ANOVA summary). This was correlated with an increase in the amount of nutrients in the residues ( Tables S3 and S4 ) that were further restored to the soil.

Table 4. Descriptive statistics of crop residues dry weight remaining on the field (%, ±standard error) for the crops grown during the study period 2009–2015.

For each crop, treatments means with different letters are significantly different (ANOVA, p -value <0.05).

While the stock (expressed in [kg/ha]; Table S3 ) of nutrients returned were greater in IN plots, the OUT plots were characterised by greater content (expressed in [g/kg]; Table S5 ) of N (three years out of five) and P (two years out of five), due to a larger proportion of chaff in the remaining residues. The trend was the opposite for K (two years out of five) ( Tables S5 and S6 ).

The tillage treatment had no significant impact on the total amount of crop residues or on the stock of nutrient ( Table 4 and Tables S2 – S6 ).

Table 5 puts the emphasis on the different amounts of each nutrient (NPK) returned by the different crops through their residues: e.g., while maize and faba bean brought higher N than wheat, maize residues alone provided the highest quantity of K to the soil.

Table 5. Effect of residue treatment on the NPK stock of crop residues [kg/ha, ±standard deviation] restituted to the soil per type of crop residue source.

Wheat values came from the mean of three years of experiments in winter wheat. For each type of nutrient and crop, treatments with different letters are significantly different (ANOVA, p -value <0.05).

Soil results

Total organic carbon.

The initial TOC content, over the entire arable depth (0–30 cm), was 11.7 g/kg. After 8 years, the different treatments (tillage or residue management) did not have a significant effect on the overall TOC content evolution (lmer : on tillage F  = 1.62, p  = 0.22 ; on residue treatments F  = 0.58, p  = 0.46), although significant differences were observed during some specific years ( Fig. 1 ). The last measurement, in spring 2016, showed higher TOC under RT-IN compared to CT-OUT and RT-OUT.

Figure 1. Total organic carbon (g/kg) present in the (A) 0–30 cm, (B) 0–10 cm, (C) 10–20 cm, (D) 20–30 cm soil layer.

Bottom ribbons represent the period covered by crops. Error bars depict the standard error between plots. Stars represent significant differences (ANOVA, p -value < 0.05) between crop residue management strategies per sampling date.

We observed a clear stratification of the TOC content between the different soil depths (0–10, 10–20 and 20–30 cm depths) in the reduced tillage treatments (lmer F  = 236.55, p  < 2.2 e −16 ; Fig. 1B – 1D ). More specifically, the TOC content increased over time in the 0–10 cm soil profile and, after 3.5 years, was systematically higher in RT than in CT (with the exception of autumn 2014). At that depth, higher TOC contents were observed in RT-IN than in RT-OUT. In the 10–20 cm soil layer, there were no differences between CT and RT. In the 20–30 cm soil layer, RT resulted in lower TOC content than CT from the third year of the trial onwards.

Soil nutrients

Averaged over the soil profile and over time, we did not observe differences in nitrate content between the different treatments, although some temporary differences were visible ( Fig. 2 ). The levels of nitrate within the 0–90 cm soil layer were highly variable over time and dominated by external inputs of fertilizer (black dots on Fig. 2 ). Residue fate had no impact on the NO 3 − stock in any of the soil layers under any crop.

Figure 2. Nitrates (N- NO 3 − ) (kg/ha) present in the (A) 0–30 cm, (B) 30–60 cm, (C) 60–90 cm depth layers of soil.

Bottom ribbons represent the period covered by crops. Error bars depict the standard error between plots. Vertical dot lines represent the date of crop fertilization. Black diamonds represent the quantity of nitrates added to the crop as mineral fertilization. Stars represent significant differences (ANOVA, p -value < 0.05) between crop residue management strategies per sampling date.

Phosphorus.

Over the course of the experiment, P content in soil significantly decreased in the 0–30 cm soil layer for all crop residue treatments, due to the absence of fertilisation during the course of the experiment ( Fig. S4 ). In addition, there was a stratification of P under RT (RT-OUT from autumn 2012 onwards (lmer F  = 84.58, p  < 2.2 e −16 ) and RT-IN from spring 2014 onwards (lmer F  = 34.06, p  = 3.6 e −12 )). In the top layer (0–10 cm), these treatments showed a higher decrease in P content than deeper in the profile. Our data do not suggest any significant impact of crop residue on P stocks in the soil (lmer F  = 0.05, p  = 0.83).

As for P, K content in soil decreased from the beginning of the trial, as no K fertilisation was applied ( Fig. S4 ). No particular effects of the different treatments were observed on the total amount of K (lmer F  = 0.06, p  = 0.81), except for the last 2 years of the experiment (2015–2016), where IN plots showed a higher K content. Also for K a stratification was visible (lmer F  = 97.91, p  < 2.2 e −16 ) with decreasing concentrations from top to bottom.

Crop results

Germination rate.

The presence or absence of extra residues did not affect germination rate, except for winter wheat in 2013–14 where we observed a lower germination rate in the RT-IN treatment. Tillage type, however, did result in differences in germination rate. Table 6 (and ANOVA summary in Table S7 ) shows that three crops (out of six) had a higher germination rate in CT as compared to RT (winter wheat (2010–11), faba bean (2013) and winter wheat (2013–14)).

Table 6. Descriptive statistics for germination rate [%, ±standard error] for the crops grown during the study period 2009–2015.

In Table 6 , we see that residue fate and tillage type had an interacting effect on the global relative germination rate which is the average of all germination rates of all crops considered for a specific treatment, normalised to the sowing density. The germination rate in RT-IN was significantly lower than RT-OUT, itself lower than CT-IN and CT-OUT.

Dynamic crop growth

When looking at the dynamics of crop growth, except for two sampling dates on ears’ growth and two sampling dates on shoot’s growth, no interactions were found between tillage and residue management treatment. Observations and results of the ANOVA and SNK analysis are reported in the supplementary material separately for ears and shoot growth ( Tables S8 – S11 ). The cumulated total produced biomass is presented in Fig. S5 (ANOVA summary in Table S12 ) under graphical representation. It was therefore decided to analyse the impacts of tillage and residue management individually.

Differences between residues management treatments (IN vs. OUT) were observed for crop development. Incorporation of residues (IN plots) negatively impacted the dynamic of ears and shoots during the first two crop seasons (winter wheat seasons 2009–10 and 2010–11). These differences appeared in both cases after the ear emergence stage. These were more pronounced for the shoot than the ear biomasses. In 2010, with weather conditions close to historical means, lower biomass under IN treatments decreased over the crop growth period, but no negative impact on yields were observed when residues were returned to the soil. In 2011, characterised by a spring drought, the exportation of residue (OUT) was favourable to the shoot development and to the early growth of ears. However, for this season, the final yield was not impacted by the residue management treatment (IN vs. OUT), as detailed in the next section. Finally, one could also notice that at the end of the 2014 crop season, while no differences were observed all along the season, a statistical difference was reported on the last sampling date in favour of IN plots. For all other sampling dates, no statistical differences were reported.

Reduced tillage mostly negatively impacted shoot crop development of winter wheat (2010–11), faba bean (2013) and maize (2015) ( Tables S8 and S9 ). This was probably due, in part, to the lower germination rate in RT plots for the winter wheat crop (2010–11)and faba bean (2013) (cfr ‘Crop yield and quality’). When computing the differences between tillage treatments ( Fig. 3 and Table S12 for ANOVA summary), it was observed that the gap between treatments tended to decrease for the different crops as they developed through the season. For faba bean no differences were finally observed between the final biomass ( Fig. 3 ), the pods ( Tables S10 and S11 ) and the seed yield ( Table 7 —cfr ‘Crop yield and quality’), while statistical differences between RT and CT remained for maize shoot, cobs and grain yield cultivated in 2015 ( Fig. 3 , Tables S10 and S11 , and Table 7 —cfr ‘Link between germination, development and yield’).

Figure 3. Relative crop biomass differences (%) between conventional tillage (CT) and reduced tillage (RT), relative to CT.

Error bars depict the standard error between plots. Stars represent significant differences (ANOVA, p -value < 0.05) between crop residue management strategies per sampling date.

Table 7. Descriptive statistics of yield dry weight [%, ±standard error] for the crops grown during the study period 2009–2015.

Crop yield and quality

Yearly grain yields were in general not influenced by residue fate or soil tillage ( Table 7 and Table S13 for ANOVA summary), except for a negative effect for reduced tillage for winter wheat cultivated in 2010–11 (−9%) and maize in 2015 (−4%) and residue incorporation for winter wheat in 2010–11 (−10%, year characterised by a spring drought, Figs. S2 and S3 ).

The cumulative grain yield since 2010 was significantly lower under reduced compared to conventional tillage (−3.4%, Table 7 ). No effect of residue fate was observed.

There was no significant effect of the treatments on NPK content of the harvested grain or seeds ( Table S14 ), except for marginally higher P content in winter wheat (in 2010–11) grains in IN plots ( P -value: 0.03) and a slightly lower K content under conventional tillage in 2 years out of 5 (Winter wheat in 2011–12, p -value: 0.01; Maize in 2015, p -value: 0.03).

Link between germination, development and yield

No correlation was seen between germination rate and shoot dry weight except for faba bean (2013) and a slight effect for winter wheat (2010–11) ( Fig. 4A ). Grain yield was only positively correlated to germination rate for winter wheat (2010–11) ( Fig. 4B ). Similarly, grain yield was slightly correlated to shoot dry weight for that year only ( Fig. 4C ).

Figure 4. (A) Total shoot dry weight (t/ha) versus germination rate (%). (B) Grain yield versus (t/ha) germination rate (%). (C) Grain yield (t/ha) versus total shoot dry weight (t/ha).

Ellipses encompass 95% of the distribution. Below each graph, colour-related r -squared values and p -values are given for that crop.

Integrated approach between crop production and soil chemistry

A principal component analysis was performed to study the relationship between soil and plant parameters ( Fig. 5 ). Plant and crop residue data are the sums (for residue biomass, shoot biomass, yield and NPK stocks) or means (for germination rate and harvest index (HI)) of the all crop data (the six crops grown between 2009 and 2015). We used soil data from the last spring measurement in 2016.

Figure 5. (A) Unconstrained ordination analysis. (B) Correlations between soil and plant variables.

(A) Each dot represents an individual plot (from 1 to 16). Red is reduced tillage and blue is conventional tillage. Filled dots are for plots with incorporation of crop residues and empty dots are plots with exportation of crop residues. (B) Residue data are represented by purple arrows, Soil parameters are in red arrows and crop parameters in green. ( NO 3 − , nitrates in the 0–90 cm soil layer; TOC, total organic carbon; K, potassium; P, phosphorus; N, nitrogen). TOC, K and P in soil are value of the 0–30 cm final measurement. Plant and crop residue data are the accumulation (Residue biomass, shoot biomass, yield, NPK stock) or mean (germination rate, harvest index (HI)) of the all crop data. Soil data are the data measured in spring 2016 (last measurement).

The two principal components allowed to explain respectively 35% and 25% of the variance. Treatments were easily differentiated by the two principal components ( Fig. 5A ). It appeared that crop productivity (yield, shoot and total biomass) and quality (K grain , P grain and N grain ) were favoured by conventional tillage, as illustrated by the discrimination of treatments along the second component ( Y -Axis) of the PCA. Similarly, it seemed that the soil parameters (TOC, K soil and to a lower extent P soil ) were more positively influenced by the crop residue retention ( Fig. 5B ), as illustrated by the discrimination of treatments along the first component ( X -Axis) of the PCA.

Effect of tillage and crop residue treatments on crop production

Overall, tillage influenced crop production more strongly than import or export of residues. The strongest effect was seen in terms of germination rate and was even stronger for residue incorporation treatments. RT resulted consistently in lower germination rates as also shown by Brennan et al. (2014) . Germination rate is strongly affected by seedbed soil moisture, soil structure and contact around the seed and soil temperature ( Guérif et al., 2001 ). It is acknowledged that crop residues can be a physical obstacle to crop emergence and a source of phytotoxicity for crop seedlings ( Morris et al., 2010 ). Moreover, the presence of crop residues around seeds can impede adequate seed-to-soil contact needed for good crop emergence by increasing the macroporosity which is known to decrease the degree of contact ( Brown et al., 1996 ). Nevertheless, the differences due to germination rate have the tendency to disappear at later growth stages if no climatic extremes occur, since the plants generally compensate a lower density with a better growth under favourable growth conditions as also observed by Dam et al. (2005) .

Several studies report higher crop production levels under CT under temperate climate, when compared to RT ( Brennan et al., 2014 ; Pittelkow et al., 2014 ). However, the difference between both systems remained small in the presented experiment, but confirmed, among else, by the PCA analysis. Our results went in the direction of the conclusion of Van den Putte et al. (2010) study of conservation agriculture in Europe showing a yield decline of 4.5% in RT systems as compared to conventional systems. When confronting the crop results to the meteorological conditions ( Fig. 3 , Table 7 and Figs. S2 and S3 ) we believe that the CT production systems might be less sensitive to inter-annual fluctuations of climatic conditions over different years compared to the RT systems. Also, Brennan et al. (2014) highlighted that the residue fate was less important than the tillage type for crop performance. Residue fate has a stronger effect on crop production under drier climates and water limited conditions ( Linden, Clapp & Dowdy, 2000 ; Pittelkow et al., 2014 ).

The differences observed for germination rates or during crop growth seem to have little influence on crop yield. A similar lack of correlation between shoot biomass and grain yield, as observed most strongly for faba bean and wheat (2014) in our study, has previously been observed for legume crops ( Araújo & Teixeira, 2008 ). For winter wheat, it is likely that the ability to produce more tillers at lower densities explains the recovery from lower germination rates, as reported in the literature ( Whaley et al., 2000 ; Gooding, Pinyosinwat & Ellis, 2002 ). Moreover, it is known that the flag leaf and ears are the main photosynthetic organs contributing to grain filling ( Sanchez-Bragado et al., 2014 ) which is an additional explanation why the entire shoot biomass was not correlated to yield, especially during years with climate conditions close to the historical means (i.e., winter 2009–10, 2011–12 and 2013–14). Winter wheat (2010–11) and maize (2015) were the only crops with observable differences between treatments at the end of crop development. We hypothesize that the spring drought during winter wheat development in 2010–11 impeded its ability to recover its potential yield as in the other years.

Except for the winter wheat 2010–11, our results did not show an increase in N grain stock. This observation is in agreement with Brennan et al. (2014) , but opposed to the results reported by Malhi et al. (2011) or Soon & Lupwayi (2012) . The absence of crop residue treatment effects on P grain content could be also due to the poor P content of crop residues. Regarding the small differences observed in grain K content, Zörb, Senbayram & Peiter (2014) mentioned that K content in grain is not correlated to K supply and grain have relatively low K contents.

Effect of tillage and crop residue treatments on soil chemistry

Over the seven years of this experiment, the effects of crop residue management on soil fertility parameters showed few statistical differences in the early time of the trial. However, the results were slowly magnifying, up to the point where differences became more systematically significant, with clear stratification occurring over the different soil layer of the ploughing depth. Furthermore, our results (PCA analysis) confirmed a clear link between soil TOC, K content, and to a lower extent P content, with residues management treatment (IN vs. OUT).

Even though the literature shows that residue incorporation could have a positive effect on the stock of SOC ( Chenu et al., 2014 ; Autret et al., 2016 ; Merante et al., 2017 ), we only observed small effects on the TOC content. It should be noted that we cultivated wheat for four out of seven years and therefore our residues contain a large proportion of straw, which has already been shown to be inefficient to increase TOC ( Lemke et al., 2010 ; Poeplau et al., 2015 ). Just like previous studies ( Angers et al., 1997 ; Dolan et al., 2006 ; Gadermaier et al., 2012 ; Dimassi Bassem, 2013 ; Riley, 2014 ; Dikgwatlhe et al., 2014 ), we have shown that reduced tillage provoked a stratification of TOC. The absence of differences between TOC(CT-IN) and TOC(CT-OUT) can be explained by a dilution effect (accumulation of organic matter in the top that is mixed through ploughing) as well as by a potentially faster degradation rate, as reported by Lal et al. (2004) .

The absence of any overall effect of residue treatment on the nitrate content was likely due to a combination of factors. Firstly, the proportionally high amount of mineral N applied as fertiliser—which respects the common practice in Belgium—reduced the effect of N returned by the residues. Secondly, the straw incorporation effect might have had a short-term impact on soil nitrate ( Van Den Bossche et al. ) rather than long term impact ( Brennan et al., 2014 ). Such a lack of impact was also reported by Stenberg et al. (1999) .

The absence of residue treatment effects on P content in soil can be explained by the low P content of crop residues. ( Damon et al., 2014 ) have shown that P availability is only increased for large amounts of residues with high P content. A P content threshold of 2–3 mg/g of residue is generally considered as the limit below which no impact should be expected. Under this value, immobilisation by microbial biomass occurs and P mineralization is hampered ( Damon et al., 2014 ). In our study, P content was 0.9 mg/g for wheat and maize residues and 1.6 mg/g for faba bean crop, which means that we were consistently and considerably below this theoretical threshold for P mineralization.

The decrease in K content was due to the lack of fertilisation during the trial (no dedicated K fertilisation or manure application was intentionally made between 2008 and 2016) but this decrease is slight. Compared to P content, K content of residues was much higher. Furthermore, as the mineralisation process is not involved, K is released in soil solution as soon as the plant cells were dead ( Schvartz, Decroux & Muller, 2005 ). These combined effects probably explain the slighter slope observed on the dynamics of K. The stratification observed in P and K content with reduced tillage was also reported by Riley (2014) .

When looking within the available choices among the soil and crop management techniques, crop production remains one of the most important drivers of farmer’s decisions. However, the impact of residue management and tillage treatment on crop production, and also on soil fertility, are known to be highly dependent on the local pedo-climatic conditions, and the literature usually focus on one of the aspects (soil or crop) and barely on multiple aspects at the same time.

This study aimed at analysing the impacts of crop residue management techniques on different soil and crop parameters together, as interacting components of the agro-ecosystem, and as a response to the local pedo-climatic conditions. We found out that, at the annual scale, crop production was generally not significantly impacted by the different residue management strategies. However, over the duration of the trial, while no effect of residue fate was reported (IN vs. OUT), the cumulative grain yield was found to be significantly lower (−3.4%) under reduced tillage (RT) compared to conventional tillage (CT).

In this seven-year experiment, small but gradually increasing differences between the different crop residue management strategies were observed. After a few years, the TOC content in the soil was higher only where the residues were incorporated and the tillage reduced. Overall, a stratification of organic matter and nutrients was observed under reduced tillage, i.e., for TOC, P and K. Crops grown on reduced tillage plots had a lower germination rate in some years, but in two years out of three crops overcame this germination gap through compensation mechanisms and finally yields were statistically equivalent.

Soil processes in general, and carbon dynamics in particular, are slow processes. Our study reflects a system currently in transition, which will likely continue to evolve over the next decade. Therefore, it will be of uttermost importance to continue the monitoring of this experimental site in order to understand the long-term impact of residue management on crop performance and soil quality and health.

Finally, when choosing among soil and crop management techniques, (among which the fate of residues and the intensity of tillage that were analysed in this study are few examples), other factors than the sole crop productivity should be included in the farmer’s decision process, such as fuel consumption, required working hours, greenhouse gas emissions ( Lognoul et al., 2017 ); analysis conducted on the same experiment), soil fauna ( Degrune, 2017 ), long-term soil quality and health.

Supplemental Information

Data were collected in 2011. Maps were obtained by interpolating using the kriging methods in ArcGIS.

Orange line is the 30 year mean, grey line is the observations for each year.

Orange line is the 30 year mean, grey bars show the observation for the year.

Bottom ribbons represent the period covered by crops. Error bars depict the standard error between plots. Stars represent significant differences (ANOVA, p -value <0.05) between crop residue management strategies per sampling date.

Error bars depict the standard error between plots.

Significance code: ‘***’ p -value < 0.001; ‘**’ p -value < 0.01; ‘*’ p -value < 0.05. (Df: degree of freedom, Mean Sq: mean square).

For each crop, treatments means with different letters are significantly different (ANOVA, p -value < 0.05). (WW: winter wheat, CT: conventional tillage, RT: reduced tillage, IN: incorporation of crop residue, OUT: exportation of crop residues).

Significance code: ‘***’ p -value < 0.001; ‘**’ p -value < 0.01; ‘*’ p -value < 0.05; ‘.’ p -value < 0.1. (Df: degree of freedom, Mean Sq: mean square).

For each crop, treatments means with different letters are significantly different (ANOVA, p  < 0.05). (WW: winter wheat, CT: conventional tillage, RT: reduced tillage, IN: incorporation of crop residue, OUT: exportation of crop residues).

Acknowledgments

A special thanks to the entire technical team of the experimental farm for their helping hands in the field. We also want to thank the CRA-W for their support. We also thank Yves Brostaux for the statistical advices.

Funding Statement

This work was funded by the AgricultureIsLife research platform—Gembloux Agro-Bio Tech—University of Liège. The CRA-W also supported part of the measurements. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Additional Information and Declarations

Competing interests.

The authors declare there are no competing interests.

Author Contributions

Marie-Pierre Hiel performed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the paper, approved the final draft.

Sophie Barbieux and Claire Olivier performed the experiments.

Jérôme Pierreux performed the experiments, authored or reviewed drafts of the paper.

Guillaume Lobet authored or reviewed drafts of the paper.

Christian Roisin contributed reagents/materials/analysis tools.

Sarah Garré authored or reviewed drafts of the paper, approved the final draft.

Gilles Colinet and Bernard Bodson conceived and designed the experiments, contributed reagents/materials/analysis tools, authored or reviewed drafts of the paper, approved the final draft.

Benjamin Dumont analyzed the data, authored or reviewed drafts of the paper, approved the final draft.

Data Availability

The following information was supplied regarding data availability:

Hiel, Marie-Pierre (2017): Crop residue management : crop ans soil raw data (2010-2016). figshare. Fileset. https://doi.org/10.6084/m9.figshare.5442289.v1 .

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Regenerative Agriculture: An agronomic perspective

Ken e giller, renske hijbeek, jens a andersson, james sumberg.

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Ken E Giller, Plant Production Systems, Wageningen University, PO Box 430, 6700AK Wageningen, The Netherlands. Email: [email protected]

Issue date 2021 Mar.

This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ( http://creativecommons.org/licenses/by-nc/4.0/ ) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages ( https://us.sagepub.com/en-us/nam/open-access-at-sage ).

Agriculture is in crisis. Soil health is collapsing. Biodiversity faces the sixth mass extinction. Crop yields are plateauing. Against this crisis narrative swells a clarion call for Regenerative Agriculture. But what is Regenerative Agriculture, and why is it gaining such prominence? Which problems does it solve, and how? Here we address these questions from an agronomic perspective. The term Regenerative Agriculture has actually been in use for some time, but there has been a resurgence of interest over the past 5 years. It is supported from what are often considered opposite poles of the debate on agriculture and food. Regenerative Agriculture has been promoted strongly by civil society and NGOs as well as by many of the major multi-national food companies. Many practices promoted as regenerative, including crop residue retention, cover cropping and reduced tillage are central to the canon of ‘good agricultural practices’, while others are contested and at best niche (e.g. permaculture, holistic grazing). Worryingly, these practices are generally promoted with little regard to context. Practices most often encouraged (such as no tillage, no pesticides or no external nutrient inputs) are unlikely to lead to the benefits claimed in all places. We argue that the resurgence of interest in Regenerative Agriculture represents a re-framing of what have been considered to be two contrasting approaches to agricultural futures, namely agroecology and sustainable intensification, under the same banner. This is more likely to confuse than to clarify the public debate. More importantly, it draws attention away from more fundamental challenges. We conclude by providing guidance for research agronomists who want to engage with Regenerative Agriculture.

Keywords: Sustainable intensification, agroecology, soil health, biodiversity, organic agriculture

Introduction

Claims that the global food system is ‘in crisis’ or ‘broken’ are increasingly common. 1 , 2 Such claims point to a wide variety of ills, from hunger, poverty and obesity; through industrial farming, over dependence on chemical fertilizer and pesticides, poor quality (if not unsafe) food, environmental degradation, biodiversity loss, exploitative labour relations and animal welfare; to corporate dominance and a lack of resilience. It is in this context, where every aspect of farming and food production, distribution and consumption is being questioned, that the current interest in ‘Regenerative Agriculture’ and ‘Regenerative Farming’ 3 has taken root.

While the use of the adjective regenerative is expanding among activists, civil society groups and corporations as they call for renewal, transformation and revitalization of the global food system ( Duncan et al., 2021 ), in this paper we explore the calls for Regenerative Agriculture from an agronomic perspective . By this we mean a perspective steeped in the use of plant, soil, ecological and system sciences to support the production of food, feed and fibre in a sustainable manner. Specifically, we address two questions: 1) What is the agronomic problem analysis that motivates the Regenerative Agriculture movement and what is the evidence base for this analysis? 2) What agronomic solutions are proposed, and how well are these supported by evidence?

Our avowedly agronomic perspective on Regenerative Agriculture means that some important aspects of the ‘food system in crisis’ narrative are beyond the scope of this paper, such as food inequalities and labour relations. However, in addition to agronomic science, our analysis is rooted in historical and political economy perspectives. These suggest that the food system is best viewed as an integral part of the much broader network of economic, social and political relations. It follows that many of the faults ascribed to the food system – including hunger, food poverty, poor labour relations, corporate dominance – will not be successfully addressed by action within the food system, but only through higher level political and economic change.

The paper proceeds as follows. The next section explores the origins of Regenerative Agriculture, and the various ways it has been defined. Following this, the two crises that are central to the rationale for Regenerative Agriculture – soils and biodiversity – are interrogated. The subsequent section looks at the practices most commonly associated with Regenerative Agriculture and assesses their potential to solve the aforementioned crises. The final discussion section presents a series of questions that may be useful for research agronomists as they engage with the Regenerative Agriculture agenda.

The origins of regenerative agriculture

The adjective ‘regenerative’ has been associated with the nouns ‘agriculture’ and ‘farming’ since the late 1970s ( Gabel, 1979 ), but the terms Regenerative Agriculture and Regenerative Farming came into wider circulation in the early 1980s when they were picked up by the US-based Rodale Institute. Through its research and publications (including the magazine Organic Gardening and Farming ), the Rodale Institute has, over decades, been at the forefront of the organic farming movement.

Robert Rodale (1983) defined Regenerative Agriculture as ‘one that, at increasing levels of productivity, increases our land and soil biological production base. It has a high level of built-in economic and biological stability. It has minimal to no impact on the environment beyond the farm or field boundaries. It produces foodstuffs free from biocides. It provides for the productive contribution of increasingly large numbers of people during a transition to minimal reliance on non-renewable resources’.

Richard Harwood, an agronomist who made his name in the international farming systems research movement ( Escobar et al., 2000 ), was Director of Rodale Research Centre when he published an ‘international overview’ of Regenerative Agriculture ( Harwood, 1983 ). The review goes to great pains to contextualize Regenerative Agriculture in relation to the historical evolution of different schools of organic and biodynamic farming, but it also highlights Rodale’s suggestion that Regenerative Agriculture was beyond organic because it included changes in ‘macro structure’ and ‘social relevancy’, and seeks to increase rather than decrease productive resources ( Rodale, 1983 ). Harwood summarizes the ‘Regenerative Agriculture Philosophy’ in 10 points (Box 1). He further states that this philosophy emphasizes: ‘1) the inter-relatedness of all parts of a farming system, including the farmer and his family; 2) the importance of the innumerable biological balances in the system; and 3) the need to maximise desired biological relationships in the system, and minimise use of materials and practices which disrupt those relationships’.

Box 1. Points summarizing the Regenerative Agriculture Philosophy as presented by Harwood (1983 : 31).

Agriculture should produce highly nutritional food, free from biocides, at high yields.

Agriculture should increase rather than decrease soil productivity, by increasing the depth, fertility and physical characteristics of the upper soil layers.

Nutrient-flow systems which fully integrate soil flora and fauna into the pattern of are more efficient and less destructive of the environment, and ensure better crop nutrition. Such systems accomplish a new upward flow of nutrients in the soil profile, reducing or eliminating adverse environmental impact. Such a process is, by definition, a soil genesis process.

Crop production should be based on biological interactions for stability, eliminating the need for synthetic biocides.

Substances which disrupt biological structuring of the farming system (such as present-day synthetic fertilizers) should not be used.

Regenerative agriculture requires, in its biological structuring, an intimate relationship between manager/participants of the system and the system itself.

Integrated systems which are largely self-reliant in nitrogen through biological nitrogen fixation should be utilized.

Animals in agriculture should be fed and housed in such a manner as to preclude the use of hormones and the prophylactic use of antibiotics which are then present in human food.

Agricultural production should generate increased levels of employment.

A Regenerative Agriculture requires national-level planning but a high degree of local and regional self-reliance to close nutrient-flow loops.

In what is probably the first journal article on Regenerative Agriculture, Francis et al. (1986) link it closely to organic and ‘low external input agriculture’, and highlight the importance of biological structuring, progressive biological sequencing and integrative farm structuring. They also associate it with a number of ‘specific technologies and systems’ including nitrogen fixation, nutrient cycling, integrated nutrient management, crop rotation, integrated pest management (IPM) and ‘weed cycling’. Figure 1 depicts the Regenerative Agriculture theory of change as articulated by Francis et al. (1986) .

Early theory of Regenerative Agriculture in developing countries. Source: Authors’ interpretation of Francis et al. (1986) .

A shifting timeline of attention

After an initial flurry of interest, Regenerative Agriculture left the scene for almost two decades before regaining momentum. To illustrate this, we look at the extent to which the terms Regenerative Agriculture and Regenerative Farming have been integrated into both the public and academic spheres. For the public sphere we draw from Google Books (Ngram Viewer) and the Nexis Uni database, which searches more than 17,000 news sources. As seen in Figure 2 , the occurrence of these terms in books first peaked in the mid-late 1980s, but by the mid-2000s they had virtually disappeared. The occurrence of Regenerative Agriculture then increased dramatically after 2015. It is important to note that over the period 1972–2018, Regenerative Agriculture appears in books much less frequently than other terms such as sustainable agriculture, organic agriculture, organic farming and agroecology.

The frequency of key terms in books (3-year rolling averages). Source: Google NGram Viewer, Corpus ‘English 2019’ which includes books predominantly in the English language published in any country.

Regenerative Agriculture and Regenerative Farming first appear in the Nexus Uni database of news stories in 1983 and 1986 respectively, both with reference to the Rodale Institute ( Figure 3a ), and neither term occurred in more than 15 news items each year until 2009. Their use increased dramatically after 2016, and since then the combined occurrence of these terms has doubled each year, reaching 6163 news items in 2020. To place this in perspective, in 2020 organic agriculture and organic farming appeared in 6,870 and 18,301 news items respectively.

(a) Occurrence of Regenerative Agriculture or Regenerative Farming in news items and (b) Academic peer-reviewed publications on Regenerative Agriculture or Regenerative Farming. Sources: (a) Nexis Uni database, (b) Web of Science.

Turning to the more academic literature, in the first 30 years following the publication of Francis et al. (1986) , only seven other papers are identified by Web of Science having the terms Regenerative Agriculture or Regenerative Farming in their title or abstract ( Figure 3b ). The year 2016 marked a clear turning point in academic interest, and by 2020 a total of 52 academic papers had been published, and together these have been cited some 250 times.

Thus, while the terms Regenerative Agriculture and Regenerative Farming have been in use since the early 1980s, to date they have not been as widely used as other related terms such as sustainable agriculture or organic agriculture. Since 2016 their occurrence in books, news stories and on the internet has increased dramatically, which reflects the fact that they have now been adopted by a wide range of NGOs (e.g. The Nature Conservancy, 4 the World Wildlife Fund, 5 GreenPeace, 6 Friends of the Earth 7 ), multi-national companies (e.g. Danone, 8 General Mills, 9 Kellogg’s, 10 Patagonia, 11 the World Council for Sustainable Business Development 12 ) and charitable foundations (e.g. IKEA Foundation 13 ). In relation to this newfound popularity, Diana Martin, the Director of Communications of the Rodale Institute, cautioned ‘It’s [Regenerative Agriculture] the new buzzword. There is a danger of it getting greenwashed’. 14

While the academic literature referring to Regenerative Agriculture is growing, the published corpus remains very limited, and only a fraction of this corpus addresses what might be considered agronomic questions. It is likely that additional funding for agronomic research will accompany the public commitments to Regenerative Agriculture being made by NGOs, corporations and foundations. Navigating the rhetoric and potential for greenwash will be a major challenge for research agronomists who seek to work in this area.

Evolving definitions

Within the recent resurgence of interest in Regenerative Agriculture, there is a lack of consensus around any particular definition ( Merfield, 2019 ; Soloviev and Landua, 2016 ). Early (and continuing) efforts have struggled to draw a clear distinction between regenerative, organic and other ‘alternative’ agricultures (for example, Whyte, 1987 : 244): indeed the Rodale Institute continues to refer to ‘regenerative organic agriculture’ ( Rodale Institute, 2014 ).

Since the 1980s, both more broad and more narrow definitions of Regenerative Agriculture have been proposed, with most highlighting or developing one or more of the elements originally identified by Rodale (1983) . For example, some authors have emphasized the idea that regenerative systems are ‘semi-closed’, i.e. ‘those designed to minimize external inputs or external impacts of agronomy outside the farm’ ( Pearson, 2007 ) or ‘those in which inputs of energy, in the form of fertilisers and fuels, are minimised because these key agricultural elements are recycled as far as possible’ ( Rhodes, 2012 ). Regenerative Agriculture as ‘a system of principles and practices’ is central to some definitions, but not all. For Burgess et al. (2019) Regenerative Agriculture ‘generates agricultural products, sequesters carbon, and enhances biodiversity at the farm scale’, and for Terra Genesis International it ‘increases biodiversity, enriches soils, improves watersheds, and enhances ecosystem services’. 15

This raises the question whether Regenerative Agriculture is an end, or a means to an end. As noted by Burgess et al. (2019) a number of definitions of Regenerative Agriculture focus on the notion of ‘enhancement’, e.g. of soil organic matter (SOM) and soil biodiversity (California State University, 2017 16 ); of biodiversity, soils, watersheds, and ecosystem services (Terra Genesis, 2017 17 ); of biodiversity and the quantity of biomass ( Rhodes, 2017 ); and of soil health ( Sherwood and Uphoff, 2000 ). Carbon Underground argues that Regenerative Agriculture should be defined around the outcome, claiming that ‘Consensus is mounting for a single, standardized definition for food grown in a regenerative manner that restores and maintains natural systems, like water and carbon cycles, to enable land to continue to produce food in a manner that is healthier for people and the long-term health of the planet and its climate’. 18 Finally, the Rodale Institute comes back to the idea of a ‘holistic systems approach’, but now with an explicit nod to both innovation and wellbeing, suggesting that ‘regenerative organic agriculture […] encourages continual on-farm innovation for environmental, social, economic and spiritual wellbeing’ ( Rodale Institute, 2014 ). A specific certification scheme, Regenerative Organic Certified was established in 2017 in the USA under the auspices of the Regenerative Organic Alliance within which the Rodale Institute is a key player. 19 Certification is based on three pillars of Soil Health, Animal Welfare and Social Fairness – each of which, it is suggested, can be verified using existing certification standards. A perceived need to move beyond the standards of the USDA Organic Certification scheme has driven the establishment of this new standard. 20

In a review of peer-reviewed articles, the most commonly occurring themes associated with Regenerative Agriculture are improvements to soil health, the broader environment, human health and economic prosperity ( Schreefel et al., 2020 ). The authors go on to define Regenerative Agriculture as ‘an approach to farming that uses soil conservation as the entry point to regenerate and contribute to multiple provisioning, regulating and supporting ecosystem services, with the objective that this will enhance not only the environmental, but also the social and economic dimensions of sustainable food production’.

While for some organizations Regenerative Agriculture is unequivocally a form of organic agriculture, others are open to the judicious use of agrochemicals. Nevertheless, from an agronomic perspective the two challenges most frequently linked to Regenerative Agriculture are:

Restoration of soil health, including, the capture of carbon (C) to mitigate climate change

Reversal of biodiversity loss

Figure 4 shows what we understand to be the most common current articulation of the Regenerative Agriculture theory of change. For the purposes of this agronomically oriented paper, the critical question is: How far and in what contexts do the proposed regenerative practices restore soil health and/or reverse biodiversity loss? Given the diversity of understandings of Regenerative Agriculture, and the different contexts within which it is promoted, it should not be surprising that a wide variety of agronomic practices are promoted under the Regenerative Agriculture rubric. We return to these practices later, but first take a closer look at the two crises that Regenerative Agriculture aims to address.

Regenerative Agriculture: Authors’ interpretation of the commonly used theory of change in 2021. Our analysis focuses on the lower blue box: ‘agronomic considerations’.

The crises addressed by Regenerative Agriculture

In this section we briefly review the purported crises of (1) soil health (including C sequestration) and (2) biodiversity, which are central to most articulations of Regenerative Agriculture. In each case we discuss how the crisis is framed and the strength of the evidence to support this framing.

A crisis of soil health

Soil health receives particularly strong attention in narratives surrounding Regenerative Agriculture ( Schreefel et al., 2020 ; Sherwood and Uphoff, 2000 ). Indeed, the idea that soil, and soil life in particular, is under threat underpins most, if not all, calls for Regenerative Agriculture. Nonetheless, the term soil health is inherently problematic ( Powlson, 2020 ). Just like soil quality, soil health is a container concept, which requires disaggregation to be meaningful. While it can be understood as something positive to strive for, underlying soil functions need meaningful indicators which can be measured and monitored over long periods of time. Moreover, agronomic practices which benefit one aspect of soil health (such as soil life) often have negative effects on other functions (such as nitrate leaching, primary production or GHG emissions, ten Berge et al., 2019 ); there is usually not one direction in soil health, but multiple trade-offs.

Many websites and testimonials concerning Regenerative Agriculture highlight the importance of soil biodiversity, and in particular the macro- and micro-organisms which are responsible for the biological cycling of nutrients. Reports of declining soil biodiversity under intensive agriculture and the simplification of soil food webs ( de Vries et al., 2013 ; Tsiafouli et al., 2014 ) have led to widespread alarm concerning soil health. For example, a recent report of an advisory body to the Dutch government was entitled ‘De Bodem Bereikt’ 21  – literally, ‘The bottom has been reached’ – a double entendre based on the word ‘bodem’ that means both bottom and soil. The report argues that soil quality has declined to a critical point – at least partly due to loss of soil biodiversity. Whilst studies clearly reveal differences in soil food webs between cultivated fields, grasslands and (semi-) natural vegetation, the links with soil function are largely established through correlation – there is little evidence for any direct causal link between soil biodiversity and any loss in function (see Kuyper and Giller, 2011 ).

The mantra to ‘feed the soil, not the crop’ has long been central to organic agriculture while the importance of building soil organic matter was highlighted by the proponents of organic or biodynamic agriculture, and in more conventional agricultural discourses in the USA (e.g. USDA, 1938 , 1957 , 1987 ) and elsewhere. Soil takes centuries to form and significant soil loss through erosion is unsustainable. The Dust Bowl in the 1930s in the USA was a foundational experience for both the scientific and public appreciation of soil. It is commonly claimed that a quarter or more of the earth’s soils are degraded, although the precise numbers are contested ( Gibbs and Salmon, 2015 ). Commonly quoted estimates of soil loss through erosion are made using run-off plots which tend to overestimate the rates of loss as they do not account for deposition and transfer of soil across the landscape. Nonetheless, Evans et al. (2020) suggest that the rates of soil loss exceed those of soil formation by an order of magnitude, suggesting a lifespan less than 200 years for a third of the soils for which data were available.

A related long-term trend that draws attention to soils, is the reduction in the global soil C pool and its contribution to global warming. Recent modelling estimates the historic soil C loss due to human land use to be around 116 Pg C ( Sanderman et al., 2017 , 2018 ), comparable to roughly one-fifth of cumulative GHG emissions from industry. Most of these losses are due to changes in land use. Conversion from natural vegetation, especially forests, almost always results in a decrease in SOM content ( Poeplau and Don, 2015 ) due to non-permanent vegetation, export of biomass and consequently, reduced amounts of organic matter inputs. The loss of soil C through land use conversion is however a different matter than the losses or gains which can be made by altering management practices on existing agricultural land. We discuss the impacts of changing management practices below.

A crisis of biodiversity

Those who promote Regenerative Agriculture frame the crisis of biodiversity around the widespread use of monocultures along with strong dependence on external inputs and a lack of ‘biological cycling’ ( Francis et al., 1986 ). No doubt, large areas of genetically uniform crops can be susceptible to rapid spread of pests and diseases and add little value to the quality of rural landscapes.

If we consider biodiversity more broadly, there is little doubt that the earth has entered a sixth mass extinction ( Ceballos et al., 2020 ). The increase in the human population, the clearance of native habitats and the expansion of agriculture over the past century are clearly root causes. How best to arrest this loss of biodiversity is less clear. Optimistic projections suggest that the world’s population will peak at around 9.8 billion in 2060 ( Vollset et al., 2020 ), whereas the United Nations Population Programme projects a population of 11.4 billion by the end of the century. In either case, the increase in population will without doubt require the production of additional nutritious food. Moderating consumption patterns and changing diets can reduce the extent of this demand, as can reducing food loss and waste, but conservative estimates suggest that overall, global food production must increase by at least 25% ( Hunter et al., 2017 ).

In simple terms, there are two ways to meet this future food demand. The first is to increase production from the existing area of agricultural land: here, what is commonly termed a ‘land sparing’ strategy, involves closing yield gaps by increasing land productivity. The second is to increase the area of land under cultivation. But converting land use to agriculture has direct impacts in terms of habitat loss, as well as multiple indirect effects through altering biogeochemical and hydrological cycles ( Baudron and Giller, 2014 ). In many areas an expansion of agricultural lands to increase food production will mean that inherently less productive soils are brought under cultivation, requiring disproportionate land use conversion. Against this backdrop, calls for, and commitments to Zero (Net) Deforestation are changing to calls for Zero (Net) Land Conversion. 22 Both aim specifically to protect areas of high conservation value for biodiversity, with the latter focused on the use of degraded lands for any future expansion of agriculture, while restoring ecosystems with high value for biodiversity conservation.

Another major concern for impacts on biodiversity relates to the effects of the chemicals used for plant protection, and in particular insecticides. Despite increasingly stringent controls since Rachel Carson published ‘Silent Spring’ in 1962, concerns remain. Attention has been focused on impacts on non-target organisms, with considerable alarm at the loss of bees and other pollinators ( Hall and Martins, 2020 ). A recent report that attracted considerable attention in the media indicated a 75% decline in flying insect biomass in Germany in only 27 years ( Hallmann et al., 2017 ). A global meta-analysis painted a more complex picture, suggesting (still alarming) average declines of ∼9% per decade in terrestrial insect abundance, but ∼11% per decade increases in freshwater insect abundance, and strong regional differences ( van Klink et al., 2020 ). Echoing the concerns about DDT raised by Carson, declines in populations of insectivorous birds were found to be associated with higher concentrations of neonicotinoids in the environment ( Hallmann et al., 2014 ). Further, neonicotinoids have been implicated in a new pesticide treadmill, where pesticide resistance and reduced populations of natural enemies lead to increased dependence on chemical control ( Bakker et al., 2020b ). With respect to weed control, the introduction of glyphosate was widely lauded as it was seen as environmentally benign compared with alternative herbicides. However, its widespread use combined with ‘Round-up Ready’ varieties of maize, oilseed rape and soybean, and reduced tillage, has led to the proliferation of herbicide-resistant weeds ( Mortensen et al., 2012 ). With increasing concerns over human toxicity, glyphosate use has become highly controversial, leading to an earlier re-assessment of its license in the EU. 23

Regenerative Agriculture practices

The practices.

McGuire (2018) , Burgess et al. (2019) and Merfield (2019) provide lists of practices associated with different variants of Regenerative Agriculture which we order in Table 1 around agronomic principles. It should be noted, that to qualify as Regenerative Organic Agriculture, no chemical fertilizers or synthetic pesticides can be used and ‘soil-less’ cultivation methods are prohibited.

Agronomic principles and practices considered to be part of Regenerative Agriculture and their potential impacts on restoration of soil health and reversal of biodiversity loss.

Based on McGuire (2018) , Burgess et al. (2019) and Merfield (2019) .

Many practices associated with Regenerative Agriculture, such as crop rotations, cover crops, livestock integration, are (or in some contexts were) generally considered to be ‘Good Agricultural Practice’ and remain integral to conventional farming. Some are more problematic: conservation agriculture, for example, can be practiced within an organic framework or as GMO-based, herbicide and fertilizer intensive ( Giller et al., 2015 ). Others, such as permaculture, have rather limited applicability for the production of many agricultural commodities. Still others, such as holistic grazing are highly contentious in terms of the claims made for their broad applicability and ecological benefits in terms of soil C accumulation and reduction of greenhouse gas emissions ( Briske et al., 2014 ; Garnett et al., 2017 ). The potential of perennial grains has aroused substantial interest in relation to Regenerative Agriculture. Deep rooting perennial grasses such as intermediate wheatgrass ( Thinopyrum intermedium ), cereals (e.g. sorghum) or legumes (e.g. pigeonpea) have the advantage of supplying multiple products such as fodder as well as grain, and provide continuous soil cover that can arrest soil erosion and reduce nitrate leaching ( Glover et al., 2010 ). On the down side, perennial grains tend to yield less than annual varieties and share constraints with monocultures in terms of pest and disease build up. They may also encounter difficulties with weed control. Snapp et al. (2019) provide a nuanced analysis of the potential of perennial grains.

Regenerative Agriculture practices, the soil crisis and climate change

A majority of the Regenerative Agriculture practices focus on soil management, with a particular emphasis on increasing soil C, under the premise that it will increase crop yields and mitigate climate change. SOM is an important indicator of soil fertility ( Reeves, 1997 ) as it serves many functions within the soil, for example in the supply of nutrients, soil structure, water holding capacity, and supporting soil life ( Johnston et al., 2009 ; Watts and Dexter, 1997 ).

The amount of C stored in soil is largely a function of the amount of organic matter added to the soil and soil texture: clay soils can store much more C than sandy soils ( Chivenge et al., 2007 ). Soil tillage has only a minor effect ( Giller et al., 2009 ). The degree to which the amount of C stored in the soil can be increased depends on the starting conditions. A continuously cultivated, degraded clay soil, heavily depleted of soil C, can store much more extra C than a degraded sandy soil. A fertile soil may already be close to what is called its C ‘saturation potential’ ( Six et al., 2002 ). Thus under continuous cultivation, soil C can only be increased marginally by changing management practices, such as the use of animal manure, cultivation of green manures or return of crop residues ( Poulton et al., 2018 ). The greatest opportunities to increase soil C are found in low yielding regions, where increasing crop yields increase the available biomass stock and inputs of organic matter to the soil ( van der Esch et al., 2017 ). But even if SOM increases due to improved management, the rate of annual increase in soil C is temporary. As a new equilibrium is reached the rate of C accumulation attenuates ( Baveye et al., 2018 ) and this new equilibrium is reached at a lower level under cultivation than under natural vegetation cover. Limiting the conversion of forest and natural grasslands to agriculture is therefore essential to protect soil C stocks. Among the practices associated with Regenerative Agriculture, agroforestry in its many shapes and forms perhaps has the greatest potential to contribute to climate change mitigation through C capture both above- and below-ground ( Feliciano et al., 2018 ; Rosenstock et al., 2019 ).

A synthesis of 14 meta-analyses across the globe indicates that crop yields mainly benefit from increased SOM due to the nutrients, in particular N, which it supplies ( Hijbeek et al., 2018 ). Nevertheless, the global N budget over the last 50 years, suggests that half of the N taken up by cereals came from mineral fertilizers ( Ladha et al., 2016 ), indicating that global food production would collapse without external nutrients. If a field is used for crop production without any external source of nutrients, as espoused by some proponents of Regenerative Agriculture, this will degrade the soil resource base and lead to a decline in yields. Symbiotic nitrogen fixation through legumes can provide a truly renewable source of some N, but to sustain production in the long term, external sources of other nutrients are required to compensate for the nutrient offtake through harvested crops.

As with the external nutrient supply, other technical options can mimic, supplement or substitute for some of the contributions that SOM makes to soil fertility. Irrigation and tillage, for example, can have positive effects on soil water availability and soil structure respectively ( van Noordwijk et al., 1997 ). This is one of the reasons why increasing SOM does not always directly benefit soil fertility or crop yields ( Hijbeek, 2017 ). Additional SOM only increases crop yields in the short term if it alleviates an immediate constraint to crop growth. In the longer term it would be expected that increased SOM leads to crop yields that are more resilient to abiotic stresses due to improved soil physical structure, but evidence on this is scarce.

With current trends in greenhouse gas emissions, most IPCC scenarios include net negative emission technologies to limit global warming to a maximum of 1.5°C above pre-industrial levels ( Rogelj et al., 2018 ). These technologies include carbon capture and storage, but also reforestation and soil C sequestration ( Rogelj et al., 2018 ). In this light, Regenerative Agriculture is said to hold a promise of ‘zero carbon farming’ or even offsetting GHG emissions from other sectors ( Hawken, 2017 ). The most recent offering from the Rodale Institute ‘confidently declares that global adoption of regenerative practices across both grasslands and arable acreage could sequester more than 100% of current anthropogenic emissions of CO 2 ’ ( Moyer et al., 2020 ). The confidence in this claim was rapidly dented by other protagonists of Regenerative Agriculture, who concluded the figure was probably closer to 10–15%. 24

A recent study in China investigated potential soil C sequestration across a range of different cropping systems. The results show that – for a wide range of crop rotations and management practices – soil C sequestration compensated on average for 10% of the total GHG emissions (N 2 O, CH 4 , CO 2 ), with a maximum of 30% ( Gao et al., 2018 ). Although there were many examples of soil C increasing in response to increased crop yields, the climate change benefits (expressed as CO 2 -equivalent) were considerably outweighed by the greenhouse gas emissions associated with the practices themselves, especially N fertilizer and irrigation. In the UK, Powlson et al. (2011) reported similar outcomes using data from the Broadbalk experiment: associated GHG emissions of crop management (tillage, fertilizers, irrigation, crop protection, etc.) were four-fold greater than the carbon sequestered. Of course, in Regenerative Agriculture the use of some of these GHG emitting crop management practices and external nutrient inputs, such as mineral fertilizers are abandoned. But while organic fertilizers such as manure can increase SOM and have additional yield benefits beyond nutrient supply, they are also more prone to nutrient losses. A recent global meta-analysis showed that manure application significantly increased N 2 O emissions by an average 32.7% (95% confidence interval: 5.1–58.2%) compared with mineral fertilizers ( Zhou et al., 2017 ), thereby offsetting the mitigation gains of soil C sequestration.

The exclusion of external inputs is even more problematic, considering that nutrients are needed to build SOM and sequester soil C ( Kirkby et al., 2011 ; Richardson et al., 2014 ). This phenomenon can be explained by stoichiometric arguments and has been coined ‘the nitrogen dilemma’ of soil C sequestration ( van Groenigen et al., 2017 ). As shown by Rice and MacCarthy (1991) , the elemental composition of SOM (ratios of C, H, O, N and S) has a narrow range. If C is added to a soil in which there is no surplus N, P or S, there will be no increase in SOM and the carbon will be lost to the atmosphere as CO 2 . Besides the associated energy requirements to build SOM, this also raises the question whether those nutrients are most useful to human society when stored in the soil, or when available for plant growth ( Janzen, 2006 ).

Regenerative Agriculture practices and the biodiversity crisis

Although reversing loss of biodiversity is a central tenet of Regenerative Agriculture, it receives surprisingly little attention in discussions of recommended practices. The principle ‘foster plant diversity’ is of course central, and is one means to address the principle to ‘avoid pesticides’. Yet little attention is paid to approaches such as integrated pest and disease management (IPM). The principles of IPM – to minimize chemical use and maximize the efficiency when used – are well established. Genetic resistance is key, and regular crop scouting is used to trigger responsive spraying when a particular threshold of the pest and disease is observed, rather than preventative spraying at particular times in the cropping calendar. Recommended practices such as rotations and (multi-species) cover crops fit within IPM, as do approaches such as intercropping and strip cropping which are largely ignored in discussions of Regenerative Agriculture. IPM is knowledge intensive, requires regular crop monitoring and the skill to identify early signs of outbreaks of multiple pests and diseases. The reasons for the lack of uptake of IPM approaches are complex, but include the perceived risk of crop damage ( Bakker et al., 2020a ). Alongside IPM, integrated weed control (IWM) combines the use of mechanical weeding through tillage and cover cropping with a much more strategic use of herbicides ( Mortensen et al., 2012 ). IWM is promoted as an environmentally friendly approach that can harness diversity to manage deleterious effects of weeds ( Adeux et al., 2019 ), but again, is highly knowledge intensive.

Whether it is possible to continue intensive forms of agriculture which will meet global demands for agricultural produce without the use of chemicals for plant protection is the subject of much debate. There is a danger that bans on the use of some products could lead to wider use of even more toxic ones, at least for a period before environmental controls catch up. Few could disagree with the aspiration to limit the use of chemicals in agriculture: in addition to biodiversity concerns, the misuse of pesticides in developing countries has serious negative effects on human health ( Boedeker et al., 2020 ; Jepson et al., 2014 ).

Finally, much of the discussion of Regenerative Agriculture, pesticides and biodiversity concerns biodiversity on-farm, rather than biodiversity across landscapes, or enhancing yields to spare land for biodiversity conservation and prevent the need for further land conversion to agriculture. This is a theme we return to when considering the broader implications of Regenerative Agriculture below.

Agriculture all over the world faces serious challenges, as governments, corporations, research agronomists, farmers and consumers seek to negotiate a critical but dynamic balance between human welfare (or the ‘right to food’), productivity, profitability, and environmental sustainability. However, given the high degree of diversity of agro-ecosystems, farm systems and policy contexts, the nature of these challenges can vary dramatically over time and space. This fact undermines any proposition that it is possible to identify one meaningful and widely relevant problem definition, or specific agronomic practices which could alleviate pressures on the food system everywhere.

Neither the ‘soil crisis’ nor the ‘biodiversity crisis’, both of which are central to the rationale for Regenerative Agriculture, is universal; and across those contexts where one, the other or both can be observed, their root causes and manifestations are not necessarily the same. This tension between, on the one hand, a compelling, high-level narrative that identifies a problem, its causes and how it should be addressed, and on the other, the complexity of divergent local realities, arises with all universalist schemes to ‘fix’ agriculture and the ‘failing’ food system. In this sense, Regenerative Agriculture, while using new language, is no different than sustainable agriculture, sustainable intensification, climate-smart agriculture, organic farming, agroecology and so on.

To date the discussion around Regenerative Agriculture has taken little account of the wide variety of initial starting points defined by the variation in local contexts and farming systems and the scales at which they operate. For example, the problems caused by over-use of fertilizer or manure in parts of North America, Europe and China may well allow for reductions in input use and result in significant environmental benefits, without necessarily compromising crop yields or farmer incomes. In contrast, in many developing countries, and especially in Africa, crop productivity, and thus the food security and/or incomes of farming households, is tightly constrained by nutrient availability (i.e. because of highly weathered soils, and the limited availability of fertilizer, manure and compostable organic matter) (e.g. Rufino et al., 2011 ). Under such circumstances continued cultivation inevitably leads to soil degradation, and the use of external inputs, including fertilizer, is essential to increase crop yields, sustain soils and build soil C ( Vanlauwe et al., 2014 , 2015 ).

Although not all interpretations of Regenerative Agriculture preclude the use of agrochemicals, all argue to reduce and minimize their use. In writings on Regenerative Agriculture, surprising little attention is paid to alternative methods of pest and disease control, although this appears to be one of the major challenges that farmers will face in order to reduce or phase out chemical control methods. Some interpretations of Regenerative Agriculture are uncompromisingly anti-GMO, despite the potential genetic engineering has to confer plant resistance and reduce the need for chemical sprays ( Giller et al., 2017 ; Lotz et al., 2020 ). Further, all types of agrochemicals are lumped into the same basket, whereas the concerns for both human and environmental health associated with pesticides and fertilizers are vastly different.

As academic and other research agronomists now seek to engage constructively with the individuals, organizations and corporations championing Regenerative Agriculture, we argue that for any given context there are five questions that must be addressed:

What is the problem to which Regenerative Agriculture is meant to be the solution?

What is to be regenerated?

What agronomic mechanism will enable or facilitate this regeneration?

Can this mechanism be integrated into an agronomic practice that is likely to be economically and socially viable in the specific context?

What political, social and/or economic forces will drive use of the new agronomic practice?

These questions are meant to stimulate critical reflection on the agronomic aspects of the mechanisms and dynamics of regeneration, given that it is the conceptual core of Regenerative Agriculture. Without reflection along these lines, Regenerative Agriculture will continue to struggle to differentiate itself from other forms of ‘alternative’ agriculture, while the practices with which it is associated will (continue to) vary little if at all from those in the established canon of ‘Good Agricultural Practices’. The questions will also help to separate the philosophical baggage and some of the extraordinary claims that are linked to Regenerative Agriculture, from the areas and problems where agronomic research might make a significant contribution.

The growing enthusiasm for Regenerative Agriculture highlights the need for agronomists to be more explicit about the fact that many of the categories and dichotomies that frame public, and to some degree the scientific debates about agriculture, have little if any analytical purchase. These include e.g. alternative/conventional; family/industrial; regenerative/degenerative; and sustainable/unsustainable. Regardless of their currency in public discourse, these categories are far too broad and undefinable to have any place in guiding agronomic research (although the politics behind their use and abuse in discourse remains of considerable interest).

It is clear from many farmer’s testimonials on the Internet that their moves towards Regenerative Agriculture are underpinned by a philosophy that seeks to protect and enhance the environment. The core argument is most often around soil health, and in particular soil biological health, which is seen as being under threat and is attributed somewhat mythical properties. In much of the promotional material available in the public domain, exaggerated claims are made for the potency and functioning of soil microorganisms in particular. By contrast, for many campaigning NGOs, the locking up or sequestration of carbon in the soil is paramount, with a vision of an agriculture free of external inputs or GMOs, that mimics nature and contributes to solving the climate crisis. Not surprisingly the claimed potential of Regenerative Agriculture has attracted considerable critique – as McGuire (2018) aptly captures in his blog entitled ‘ Regenerative Agriculture: Solid Principles, Extraordinary Claims ’. It seems unlikely that Regenerative Agriculture can deliver all of the positive environmental benefits as well as the increase in global food production that is required. Reflective engagement by research agronomists is now critically important.

Acknowledgement

We thank David Powlson and Matthew Kessler for their critical reviews of an earlier version of this manuscript. All errors or omissions remain our responsibility.

https://www.weforum.org/agenda/2018/01/our-food-system-is-broken-three-ways-to-fix-it/ .

https://www.theguardian.com/environment/2018/nov/28/global-food-system-is-broken-say-worlds-science-academies .

Hereon we use the term Regenerative Agriculture to encompass Regenerative Farming.

https://www.nature.org/en-us/what-we-do/our-insights/perspectives/the-next-agriculture-revolution-is-under-our-feet/ .

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https://civileats.com/2020/10/01/does-overselling-regenerative-ags-climate-benefits-undercut-its-potential/ .

Declaration of conflicting interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The NWO-WOTRO Strategic Partnership NL-CGIAR and the CGIAR Research Program on Maize through the CIMMYT grant ‘Rural livelihood-oriented research methodologies for social impact analyses of Sustainable Intensification interventions’.

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