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Conservation agriculture (CA) is being promoted as a set of management practices that can sustain crop production while providing positive environmental benefits. However, its impact on crop productivity is hotly debated, and how this productivity will be affected by climate change remains uncertain. Here we compare the productivity of CA systems and their variants on the basis of no tillage versus conventional tillage systems for eight major crop species under current and future climate conditions using a probabilistic machine-learning approach at the global scale. We reveal large differences in the probability of yield gains with CA across crop types, agricultural management practices, climate zones and geographical regions. For most crops, CA performed better in continental, dry and temperate regions than in tropical ones. Under future climate conditions, the performance of CA is expected to mostly increase for maize over its tropical areas, improving the competitiveness of CA for this staple crop.The authors assess the productivity of conservation agriculture systems for eight major crops under current and future climate using a global-scale probabilistic machine-learning approach, revealing substantial differences in yield gain probabilities across crop type, management practice, climate zone and geography.

Environmental merits are a common motivation for many urban agriculture (UA) projects. One powerful way of quantifying environmental impacts is with life cycle assessment (LCA): a method that estimates the environmental impacts of producing, using, and disposing of a good. LCAs of UA have proliferated in recent years, evaluating a diverse range of UA systems and generating mixed conclusions about their environmental performance. To clarify the varied literature, we performed a systematic review of LCAs of UA to answer the following questions: What is the scope of available LCAs of UA (geographic, crop choice, system type)? What is the environmental performance and resource intensity of diverse forms of UA? How have these LCAs been done, and does the quality and consistency allow the evidence to support decision making? We searched for original, peer-reviewed LCAs of agricultural production at UA systems, and selected and evaluated 47 papers fitting our analysis criteria, covering 88 different farms and 259 production systems. Focusing on yield, water consumption, greenhouse gas emissions, and cumulative energy demand, using functional units based on mass of crops grown and land occupied, we found a wide range of results. We summarized baseline ranges, identified trends across UA profiles, and highlighted the most impactful parts of different systems. There were examples of all types of systems—across physical set up, crop type, and socio-economic orientation—achieving low and high impacts and yields, and performing better or worse than conventional agriculture. However, issues with the quality and consistency of the LCAs, the use of conventional agriculture data in UA settings, and the high variability in their results prevented us from drawing definitive conclusions about the environmental impacts and resource use of UA. We provided guidelines for improving LCAs of UA, and make a strong case that more research on this topic is necessary to improve our understanding of the environmental impacts and benefits of UA.

No tillage (NT) is often presented as a means to grow crops with positive environmental externalities, such as enhanced carbon sequestration, improved soil quality, reduced soil erosion, and increased biodiversity. However, whether NT systems are as productive as those relying on conventional tillage (CT) is a controversial issue, fraught by a high variability over time and space. Here, we expand existing datasets to include the results of the most recent field experiments, and we produce a global dataset comparing the crop yields obtained under CT and NT systems. In addition to crop yield, our dataset also reports information on crop growing season, management practices, soil characteristics and key climate parameters throughout the experimental year. The final dataset contains 4403 paired yield observations between 1980 and 2017 for eight major staple crops in 50 countries. This dataset can help to gain insight into the main drivers explaining the variability of the productivity of NT and the consequence of its adoption on crop yields. Measurement(s) yield trait • crop Technology Type(s) digital curation Factor Type(s) precipitation balance • temperature • crop species • soil texture • management of crop rotation • management of soil cover • management of field fertilization • management of weed and pest control • management of crop irrigation • geographic location Sample Characteristic - Organism Viridiplantae Sample Characteristic - Environment cultivated environment Sample Characteristic - Location global Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.13303118

Conservation agriculture (CA) has been promoted to mitigate climate change, reduce soil erosion, and provide a variety of ecosystem services. Yet, its impacts on crop yields remains controversial. To gain further insight, we mapped the probability of yield gain when switching from conventional tillage systems (CT) to CA worldwide. Relative yield changes were estimated with machine learning algorithms trained by 4403 paired yield observations on 8 crop species extracted from 413 publications. CA has better productive performance than no-till system (NT), and it stands a more than 50% chance to outperform CT in dryer regions of the world, especially with proper agricultural management practices. Residue retention has the largest positive impact on CA productivity comparing to other management practices. The variations in the productivity of CA and NT across geographical and climatical regions were illustrated on global maps. CA appears as a sustainable agricultural practice if targeted at specific climatic regions and crop species.

Oil palm is the most rapidly expanding tropical perennial crop. Its cultivation raises environmental concerns, notably related to the use of nitrogen (N) fertilisers and the associated pollution and greenhouse gas emissions. While numerous and diverse models exist to estimate N losses from agriculture, very few are currently available for tropical perennial crops. Moreover, there is a lack of critical analysis of their performance in the specific context of tropical perennial cropping systems. We assessed the capacity of 11 models and 29 sub-models to estimate N losses in a typical oil palm plantation over a 25-year growth cycle, through leaching and runoff, and emissions of NH3, N2, N2O, and NOx. Estimates of total N losses were very variable, ranging from 21 to 139 kg N ha−1 yr−1. On average, 31 % of the losses occurred during the first 3 years of the cycle. Nitrate leaching accounted for about 80 % of the losses. A comprehensive Morris sensitivity analysis showed the most influential variables to be soil clay content, rooting depth, and oil palm N uptake. We also compared model estimates with published field measurements. Many challenges remain in modelling processes related to the peculiarities of perennial tropical crop systems such as oil palm more accurately.

Crop yields are important items in the economic performance and the environmental impacts of second-generation biofuels. Since they strongly depend on crop management and pedoclimatic conditions, it is important to compare candidate feedstocks to select the most appropriate crops in a given context. Agro-ecosystem models offer a prime route to benchmark crops, but have been little tested from this perspective thus far. Here, we tested whether an agro-ecosystem model (CERES-EGC) was specific enough to capture the differences between miscanthus and switchgrass in northern Europe. The model was compared to field observations obtained in seven long-term trials in France and the UK, involving different fertilizer input rates and harvesting dates. At the calibration site (Estrées-Mons), the mean deviations between simulated and observed crop biomass yields for miscanthus varied between −0.3 t DM ha−1 and 4.2 t DM ha−1. For switchgrass, simulated yields were within 1.0 t DM ha−1 of the experimental data. Observed miscanthus yields were higher than switchgrass yields in most sites and for all treatments, with one exception. Overall, the model captured the differences between both crops adequately, with a mean deviation of 0.46 t DM ha−1, and could be used to guide feedstock selections over larger biomass supply areas.

Oil palm (Elaeis guineensis Jacq.) is currently cultivated on 19 million ha, and palm oil represents more than one‐third of the global vegetable oil market. Addition of nitrogen (N) via legume cover crop and fertilizers is a common practice in industrial oil palm plantations, however, there is a tendency for N loss, thus contributing significantly to environmental effects. To improve the sustainability of palm oil production, it is crucial to determine which management practices minimize N losses. Continuous field measurements would be cost‐prohibiting as a monitoring tool, and in the case of oil palm, available models do not account for all the potential nitrogen inputs and losses or management practices. In this context, we developed IN‐Palm, a model to help managers and scientists estimate N losses to the environment and identify best management practices. The main challenge was to build the model in a context of knowledge scarcity. Given these objectives and constraints, we developed an agri‐environmental indicator, using the INDIGO method and fuzzy decision trees. We validated the N leaching module of IN‐Palm against field data from Sumatra, Indonesia. IN‐Palm is implemented in an Excel file and uses 21 readily available input variables to compute 17 modules. It estimates annual emissions and scores for each N‐loss pathway and provides recommendations to reduce N losses. IN‐Palm predictions of N leaching were acceptable according to several statistics, with a tendency to underestimate nitrogen leaching. Thus, we highlighted necessary improvements to increase IN‐Palm precision before use in plantations.

Green manure (GM) enhances the ecological services in agricultural ecosystems, including soil health and carbon sequestration. However, its effect on regional methane (CH 4 ) emissions from paddy fields is unclear. Here we clarify the impacts of GM rotation by combining process-based modeling with microbial gene abundance information and coordinated distributed observations at 14 sites in southern China. We found that GM management, including application rate and rotation year, mainly affects CH 4 emissions in GM-rice systems by impacting soil biotic factors, which explain 78.4% of the variation (p < 0.001). The most influential factor is the ratio of soil CH 4 production to oxidation gene abundances (R 2 = 0.510; p < 0.001), which decreases with GM rotation year due to increased activity of methane-oxidizing soil microbes (p < 0.001), indicating that CH 4 emissions from GM-rice systems decrease with increased GM rotation year. By incorporating these microbial mechanisms as quantitative parameters in process-based model, we project that approximately 76% of the paddy rice areas in southern China, which have relatively low GM biomass and baseline CH 4 emissions, can achieve reductions in CH 4 emissions through nearly 15 years of GM crop rotation. This study indicates that CH 4 emissions from GM-rice rotations with appropriate GM application rate over the long term will not significantly increase, resolving the contradictions in previous research

Assessing the productive performance of conservation agriculture (CA) has become a major issue due to growing concerns about global food security and sustainability. Numerous experiments have been conducted to assess the performance of CA under various local conditions, and meta-analysis has become a standard approach in agricultural sector for analysing and summarizing the experimental data. Meta-analysis provides valuable synthetic information based on mean effect size estimation. However, summarizing large amounts of information by way of a single mean effect value is not always satisfactory, especially when considering agricultural practices. Indeed, their impacts on crop yields are often non-linear, and vary widely depending on a number of factors, including soil properties and local climate conditions. To address this issue, here we present a machine learning approach to produce data-driven global maps describing the spatial distribution of the productivity of CA versus conventional tillage (CT). Our objective is to evaluate and compare several machine-learning models for their ability in estimating the productivity of CA systems, and to analyse uncertainty in the model outputs. We consider different usages, including classification, point regression and quantile regression. Our approach covers the comparison of 12 different machine learning algorithms, model training, tuning with cross-validation, testing, and global projection of results. The performances of these algorithms are compared based on a recent global dataset including more than 4,000 pairs of crop yield data for CA vs. CT. We show that random forest has the best performance in classification and regression, while quantile regression forest performs better than quantile neural networks in quantile regression. The best algorithms are used to map crop productivity of CA vs. CT at the global scale, and results reveal that the performance of CA vs. CT is characterized by a strong spatial variability, and that the probability of yield gain with CA is highly dependent on geographical locations. This result demonstrates that our approach is much more informative than simply presenting average effect sizes produced by standard meta-analyses, and paves the way for such probabilistic, spatially-explicit approaches in many other fields of research.

PURPOSE: Recent life cycle assessment studies for vegetable products have identified the agricultural stage as one of the most important contributors to the environmental impacts for these products, while vegetable production systems are characterized by specific but also widely diverse production conditions. In this context, a review aiming at comparing the potential impacts of vegetable products and analyzing the relevance of the methods and data used for the inventory of the farm stage appeared necessary. METHODS: Ten papers published in peer-reviewed scientific journals or ISO-compliant reports were selected. First, a presentation of the selected papers was done to compare the goal and scope and the life cycle inventory data to the related sections in the ILCD Handbook. Second, a quantitative review of input flows and life cycle impact assessment (LCIA) results (global warming, eutrophication, and acidification) was based on a cropping system typology and on a classification per product group. Third, an in-depth analysis of the methods used to estimate field emissions of reactive nitrogen was proposed. RESULTS AND DISCUSSION: The heated greenhouse system types showed the greatest global warming potential. The giant bean group showed the greatest acidification and eutrophication potentials per kilogram of product, while the tomato group showed the greatest acidification and eutrophication potentials per unit of area. Main sources of variations for impacts across systems were yields and inputs variations and system expansion rules. Overall, the ability to compare the environmental impact for these diverse vegetable products from cradle-to-harvest was hampered by (1) weaknesses regarding transparency of goal and scope, (2) a lack of representativeness and completeness of data used for the field stage, and (3) heterogeneous and inadequate methods for estimating field emissions. In particular, methods to estimate reactive nitrogen emissions were applied beyond their validity domain. CONCLUSIONS AND RECOMMENDATIONS: This first attempt at comparing the potential impacts of vegetable products pinpointed several gaps in terms of data and methods to reach representative LCIA results for the field production stage. To better account for the specificities of vegetable cropping systems and improve the overall quality of their LCA studies, our key recommendations were (1) to include systematically phosphorus, water, and pesticide fluxes and characterize associated impacts, such as eutrophication, toxicity, and water deprivation; (2) to better address space and time representativeness for field stage inventory data through better sampling procedures and reporting transparency; and (3) to use best available methods and when possible more mechanistic tools for estimating Nr emissions.