Global Cropland Research Articles

Phasing out palm and soy oil biodiesel in the EU: What is the benefit?

AbstractThe Renewable Energy Directive (RED II) by the European Union (EU) provides an updated framework for the use of renewable energy in the EU transport sector until 2030, and bans the use of biofuels with a high risk of causing indirect land‐use change in high carbon stock areas (high ILUC‐risk criteria). The only biofuel feedstock affected by this criterion is palm oil. We employ the computable general equilibrium (CGE) model DART‐BIO for a scenario‐based policy analysis and evaluate a phase‐out of palm oil‐based biodiesel, and an additional phase‐out of soy oil‐based biodiesel in the EU. Our results show that the palm phase‐out has only a relatively small impact on global palm fruit production and total crop land use in tropical and subtropical regions, while the soy phase‐out leads to a comparable stronger decrease in global soy production, and a reduction in total cropland use in soy‐producing regions. Both policies lead to increased oilseed production in the EU. Therefore, farmer in Malaysia and Indonesia face a significantly reduced income. While European farmers profit the most, EU firms and households are confronted with higher expenditures. Finally, this study indicates that unilateral demand‐side regulations for a single good in a single sector is not sufficient for effective environmental protection. Enhanced binding sustainability criteria and certification schemes for the use of all vegetable oils in every sector and industry as well as improved protection schemes for sensible forest areas are necessary.

Identifying and quantifying local uncertainty and discrepancy in the comparison of global cropland extent through a synergistic approach

Spatiotemporally consistent information on global cropland extent is essential for resource management and scientific research. Multiple cropland datasets derived from remotely sensed products are currently available. However, significant discrepancies and uncertainties among them lead to acreage estimates that diverge considerably from official statistics, thus constraining their applicability. To this end, a new stratified optimization synergistic approach (SOSA) is here proposed to create hybrid cropland maps of China, circa 2000–2020, by fusing five existing land cover maps (i.e., CLCD, GLC_FCS30, Globeland30, GlobalCrop, and ESA_CCI), and sub-national statistics. Given the underlying challenges associated with cost-effective large-scale cropland mapping, this approach seeks to strike a balance between data value, veracity, and affordability. SOSA streamlines the commonly used protocol procedure for determining the optimal agreement level and the best product combination. Preliminary validation of the resulting cropland maps was performed, and the evaluation demonstrated that the synergy cropland map exhibited greater spatial accuracy and closer agreement with statistics compared to any individual input map. This hints that synergistic approaches can bolster cropland mapping performance and amplify consistency with statistical data. Our results are expected to serve as a valuable reference for data users, aid in the future improvement of cropland mapping to support forward-thinking applications, and enhance our understanding and modeling of agricultural systems worldwide.

Hot spots and hot moments of greenhouse gas emissions in agricultural peatlands

AbstractDrained agricultural peatlands occupy only 1% of agricultural land but are estimated to be responsible for approximately one third of global cropland greenhouse gas emissions. However, recent studies show that greenhouse gases fluxes from agricultural peatlands can vary by orders of magnitude over time. The relationship between these hot moments (individual fluxes with disproportionate impact on annual budgets) of greenhouse gas emissions and individual chamber locations (i.e. hot spots with disproportionate observations of hot moments) is poorly understood, but may help elucidate patterns and drivers of high greenhouse gas emissions from agricultural peatland soils. We used continuous chamber-based flux measurements across three land uses (corn, alfalfa, and pasture) to quantify the spatiotemporal patterns of soil greenhouse gas emissions from temperate agricultural peatlands in the Sacramento-San Joaquin Delta of California. We found that the location of hot spots of emissions varied over time and were not consistent across annual timescales. Hot moments of nitrous oxide (N2O) and carbon dioxide (CO2) fluxes were more evenly distributed across space than methane (CH4). In the corn system, hot moments of CH4 flux were often isolated to a single location but locations were not consistent across years. Spatiotemporal variability in soil moisture, soil oxygen, and temperature helped explain patterns in N2O fluxes in the annual corn agroecosystem but were less informative for perennial alfalfa N2O fluxes or CH4 fluxes across ecosystems, potentially due to insufficient spatiotemporal resolution of the associated drivers. Overall, our results do not support the concept of persistent hot spots of soil CO2, CH4, and N2O emissions in these drained agricultural peatlands. Hot moments of high flux events generally varied in space and time and thus required high sample densities. Our results highlight the importance of constraining hot moments and their controls to better quantify ecosystem greenhouse gas budgets.

Assessing cropland disagreement in Tanzania using machine learning methods with Sentinel-2 and Planet Scope imagery

ABSTRACT East Africa faces major land use pressures arising from the harsh effects of climate change and fast-increasing human populations. Recent efforts to increase food production in this region have focused on cropland mapping and approaches to improve yield. Generating accurate cropland maps provides critical support to inform policy, investment, and logistical decisions that address food security, but cropland disagreement assessment studies are lacking mostly due to the unavailability of fine-scale data. Understanding the inconsistent nature of pre-existing global cropland mapping products necessitates disagreement assessment as the most valuable urgent tool to align agricultural resource allocation and policy realignment to tackle food security challenges. This study evaluates cropland disagreement in Kilombero and Ulanga districts in Morogoro, Tanzania based on land use land cover (LULC) maps generated using machine learning methods and high-resolution PlanetScope and Sentinel-2 data sets acquired from 1 January 2017 to 31 December 2018. We created time-variant median composites and performed a three-step assessment; (i) generating multi-class LULC maps in Google Earth Engine, (ii) generating croplands and non-cropland LULC classes from the multi-class images, and (iii) assessing cropland disagreement based on GIS conditional statements. Results show minimal disagreement in mapping cropland based on higher-resolution PlanetScope and Sentinel-2. Results based on the Random Forests and Support Vector Machines relatively outperform those from Classification and Regression Trees achieving 93%, 89%, and 83% with PlanetScope and 91%, 86%, and 76% with Sentinel-2, respectively. On average, 73% of the pixels were consistently classified while 27% were misclassified between cropland and non-cropland classes. Prior unsupervised land cover cluster analysis may substantiate the quality of sampling for accurate land cover classification. Our results indicate that in data-sparse regions where confirmatory ground truth data are lacking, remotely sensed cropland identification may be more successful using Support Vector Machines on newer high-resolution imagery, thereby improving crop forecasts.

Soil potassium depletion in global cereal croplands and its implications

Nitrogen (N), phosphorus (P) and potassium (K) are of the most essential nutrients for crop production, and inputs of their fertilizers have been a direct and effective way to replenish their removal from crop harvest. However, the global soil K status is scarcely documented. This study attempts to evaluate the soil K deficit in global cereal croplands and potential effects of better K fertilizer management. We hypothesized that historical records of cereal yield and K fertilizer use rate (K-FUR) can be used to understand how soil K deficit is related to the cumulative insufficient K replenishment. Evidently, the cereal production has been depleting soil K at a rate much higher than its replenishment, resulting in soil K deficit across 61 % of the global cereal harvest area with an average K-FUR (kg ha−1) of 7.1 which is much lower than the ∼27 required to balance N and P supply for an optimal grain yield. Replenishing K for all K-deficit areas based on current K-FUR classes could potentially add about 601 Mt. grains to the global food production: 386 Mt. from 153 M ha croplands with K-FUR class 10–20 kg ha−1; 90 Mt. from 131 M ha with K-FUR class 3–10 kg ha−1; and 126 Mt. from 158 M ha with K-FUR class <3 kg ha−1. Global soil K deficit is a long-standing and ubiquitous issue, and to replenish K for all K-deficit areas as proposed herein can be a feasible way for improving cereal yield by about 49 %. Increasing awareness of public and policy-makers about this threat and potential will help lead to a judicious allocation of limited potash resources and to a better return on any financial and humanitarian aids for eradicating poverty and hunger worldwide while also advancing global food security and agricultural sustainability.

Direct evidence for atmospheric carbon dioxide removal via enhanced weathering in cropland soil

Enhanced weathering of soil-applied crushed silicate rocks may remove substantial atmospheric carbon dioxide; however, field testing of this negative emission technology is lacking. Models have suggested that enhanced weathering could, in principle, remove billions of tons of CO2 each year across global croplands, but methodological limitations have hindered direct measurement of CO2 sequestration via crushed rock amendments in agriculture. Further questions remain concerning the efficacy of this technology in arid climates. Here we provide direct evidence of rapid CO2 removal via enhanced weathering in soil pore water samples from a corn (Zea mays L.) cropping system in California. From December through February, during an extreme drought in our study region, we demonstrate a 2.6 to 2.9-fold increase in in situ bicarbonate alkalinity in response to additions of metabasalt and olivine fines. We provide a field analysis of carbon removal via silicate rock amendments and suggest enhanced weathering can remove carbon dioxide even under moisture-limited conditions.

A redistribution of nitrogen fertiliser across global croplands can help achieve food security within environmental boundaries

A major societal challenge is to produce sufficient food for a growing global population while simultaneously reducing agricultural nitrogen pollution to within safe environmental boundaries. Here we use spatially-resolved, process-based simulations of cereal cropping systems (at 0.5° resolution) to show how redistribution of nitrogen fertiliser usage could meet this challenge on a global scale. Focusing on major cereals (maize, wheat and rice), we find that current production could be (i) maintained with a 32% reduction in total global fertiliser use, or (ii) increased by 15% with current nitrogen fertiliser levels. This would come with substantial reductions in environmental nitrogen losses, allowing cereal production to stay within environmental boundaries for nitrogen pollution. The more equal distribution of nitrogen fertiliser across global croplands would reduce reliance on current breadbasket areas, allow regions such as Sub-Saharan Africa to move towards self-sufficiency and alleviate nitrogen pollution in East Asia and other highly fertilised regions.

Performance Assessment of Irrigation Projects in Nepal by Integrating Landsat Images and Local Data

With growing global concern for food and water insecurity, an efficient method to monitor irrigation projects is essential, especially in the developing world where irrigation performance is often suboptimal. In Nepal, the irrigated area has not been objectively recorded, although their assessment has substantial implications for national policy, project’s annual budgets, and donor funding. Here, we present the application of Landsat images to measure irrigated areas in Nepal for the past 17 years to contribute to the assessment of the irrigation performance. Landsat 5 TM (2006–2011) and Landsat 8 OLI (2013–2022) images were used to develop a machine learning model, which classifies irrigated and non-irrigated areas in the study areas. The random forest classification achieved an overall accuracy of 82.2% and kappa statistics of 0.72. For the class of irrigation areas, the producer’s accuracy and consumer’s accuracy were 79% and 96%, respectively. Our regionally trained machine learning model outperforms the existing global cropland map, highlighting the need for such models for local irrigation project evaluations. We assess irrigation project performance and its drivers by combining long-term changes in satellite-derived irrigated areas with local data related to irrigation performance, such as annual budget, irrigation service fee, crop yield, precipitation, and main canal discharge.

English

Conservation agriculture (CA) aims to achieve productive and sustainable farming while preserving the natural resource base and environment. This article explores how CA aligns with Sustainable Development Goals (SDGs) of the United Nations and contributes to their achievement, particularly SDG 2: Zero Hunger, SDG 6: Clean Water and Sanitation, SDG 12: Responsible Consumption and Production, SDG 13: Climate Action, and SDG 15: Life on Land. CA has gained significant attention as a sustainable farming system on over 200 million hectares of global cropland, with an annual increase of 10 million hectares since 2008. The principles of minimum soil disturbance, diverse crop rotations and permanent soil cover help improve soil health, nutrients and water management, and contribute to biodiversity preservation. By encouraging sustainable agriculture and enhancing nutrition, SDG 2 seeks to end hunger. CA practices enhance soil health and nutrient availability, increasing crop productivity and reducing reliance on synthetic inputs like pesticides and fertilizers. The resilience of CA to climate change challenges further supports food production. For SDG 6, minimum tillage and soil cover components of CA help reduce soil erosion, resulting in cleaner water by preventing sedimentation in water bodies. Improved water management and conservation contribute to sustainable water use and sanitation. SDG 12 seeks responsible consumption and production patterns. CA promotes efficient resource use, reduces waste, and minimizes the negative environmental impact of agricultural inputs, leading to sustainable production systems with less reliance on external inputs. Regarding SDG 13, CA practices improve soil and water management, increase soil carbon absorption, and lower greenhouse gas emissions, all of which help agricultural systems become more resilient to climate change. Lastly, SDG 15 focuses on life on land preservation. CA's promotion of biodiversity through cover cropping and decreased chemical use provides habitats for organisms, supporting ecosystem services and sustainable land use. In conclusion, CA plays a significant role in achieving multiple SDGs by promoting sustainable agriculture, conserving resources, mitigating climate change, and preserving biodiversity. The adoption and expansion of CA practices are essential for a sustainable and resilient future

Effects of conservation tillage on soil enzyme activities of global cultivated land: A meta-analysis

The negative impacts of conventional agriculture and the imperative to adopt conservation tillage garnered significant attention. However, the effects of conservation tillage on soil enzyme activities still lack comprehensive cognition. Here, we collected 14,308 pairwise observations from 369 publications worldwide to systematically evaluate the effects of different conservation tillage practices (reduced tillage (T), reduced tillage with straw return (TS), reduced tillage with straw mulch return (TSO), no-tillage (NT), no-tillage with straw return (NTS), and no-tillage with straw mulch return (NTSO)) on the activities of 35 enzymes in soil. The results showed that: (1) the effect of conservation tillage on soil enzyme activity varied by enzyme type, except for peroxidase (−12.34%), which showed an overall significant positive effect (10.28–89.76%); (2) the NTS and TS demonstrated strong potential to improve soil enzyme activities by increasing a wide variety of soil enzyme activities (12–15) and efficacy (9.76–75.56%) than other conservation tillage (8.60–68.68%); (3) in addition, the effect of conservation tillage on soil enzyme activity was regulated by soil depth, crop type, years of conservation tillage, climate (mean annual precipitation and temperature), and soil physicochemical properties (e.g., pH, bulk density, electrical conductivity, organic matter, ammonium nitrogen, total phosphorus, available phosphorus, total potassium, available potassium, etc.). Overall, our quantitative analysis clearly suggests that conservation tillage is an effective measure for improving soil enzyme activity on global croplands, where combination of reduced tillage or no-till with straw return are considered to have great potential and promise. The results contribute to better comprehend the effects of conservation tillage on soil activity and provide a valuable insight for agricultural management.

Towards a better understanding of pathways of multiple co-occurring erosion processes on global cropland

Soil erosion is a complex process involving multiple natural and anthropic agents, causing the deterioration of multiple components comprising soil health. Here, we provide an estimate of the spatial patterns of cropland susceptibility to erosion by sheet and rill, gully, wind, tillage, and root crops harvesting and report the co-occurrence of these processes using a multi-model approach. In addition, to give a global overview of potential future changes, we identify the locations where these multiple concurrent soil erosion processes may be expected to intersect with projected dry/wet climate changes by 2070. Of a modelled 1.48 billion hectares (B ha) of global cropland, our results indicate that 0.56 B ha (∼36% of the total area) are highly susceptible (classes 4 and 5) to a single erosion process, 0.27 B ha (∼18% of the total area) to two processes and 0.02 B ha (1.4% of the total area) to three or more processes. An estimated 0.82 B ha of croplands are susceptible to possible increases in water (0.68 B ha) and wind (0.14 B ha) erosion. We contend that the presented set of estimates represents a basis for enhancing our foundational knowledge on the geography of soil erosion at the global scale. The generated insight on multiple erosion processes can be a useful starting point for decision-makers working with ex-post and ex-ante policy evaluation of the UN Sustainable Development Goal 15 (Life on Land) activities. Scientifically, this work provides the hitherto most comprehensive assessment of soil erosion risks at the global scale, based on state-of-the-art models.

Changes in crop failures and their predictions with agroclimatic conditions: Analysis based on earth observations and machine learning over global croplands

In this study, we aim to characterize synchronized global crop failures using remote sensing-based products, analyze their predictability and relationships with agroclimatic conditions using machine learning, and identify trends of the most influential agroclimatic indices revealed by machine learning over global croplands. We found that global synchronous crop failures showed strong interannual variability during 1982 to 2016. The most extreme global synchronous crop failure events occurred over 40% of global croplands in the years 2002 (rice and wheat) and 2012 (maize and soy), which had drier and warmer conditions compared to the normal years. Crop failure events can be accurately predicted using machine learning with agroclimatic indices. Of the four crops for both temperate and tropic regions, soy crop failure is most accurately predicted, with an Area Under the Curve (AUC) score of 0.8991 for the temperate region and 0.7892 for the tropics. The AUC score of maize failure in the temperate region is 0.8760, followed by wheat failure (0.8627) and rice failure (0.8025). In the tropics, the remaining crops performed similarly, with AUC scores of 0.7298 (maize), 0.7313 (rice), and 0.7337 (wheat). The machine learning model revealed that growing degree days, last spring frost, first fall frost, growing season precipitation, and optimal field conditions (represented by soil moisture) are the most influential agroclimatic indices, showing various nonlinear relationships with crop failure probabilities. The most influential agroclimatic indices present significant trends on more than 25% of global croplands, showing increasing growing degree days, earlier last spring frost, later first fall frost, while growing season precipitation and optimal field conditions are increasing. Our findings may inform food security predictions, selections of weather index for crop insurance, and climate adaptations.

Cropland abandonment in China: Patterns, drivers, and implications for food security

Grasping cropland abandonment patterns in China can help protect croplands, thereby ensuring the perpetuation of nationwide food security. However, the pattern of long-term cropland abandonment at the national scale remains limited. Here, we investigate the spatiotemporal patterns and drivers of cropland abandonment across China during 1990–2019 by integrating continuous long-term series of land cover, natural factors, and socioeconomic data. We find that the mean abandoned cropland area is 2.34 × 104 km2 per year. The spatial pattern of cropland and natural factors are prime drivers of cropland abandonment, but there is a substantial variation across agricultural regions. Further analysis suggests that annual food loss due to cropland abandonment is about 7.94 billion kilograms, which could feed 19.85 million people. These findings allow for a better understanding of the spatial heterogeneity and driving forces of cropland abandonment patterns, and inspire to govern global cropland abandonment and formulate relevant policies.

Building a community-based open harmonised reference data repository for global crop mapping.

Reference data is key to produce reliable crop type and cropland maps. Although research projects, national and international programs as well as local initiatives constantly gather crop related reference data, finding, collecting, and harmonizing data from different sources is a challenging task. Furthermore, ethical, legal, and consent-related restrictions associated with data sharing represent a common dilemma faced by international research projects. We address these dilemmas by building a community-based, open, harmonised reference data repository at global extent, ready for model training or product validation. Our repository contains data from different sources such as the Group on Earth Observations Global Agricultural Monitoring Initiative (GEOGLAM) Joint Experiment for Crop Assessment and Monitoring (JECAM) sites, the Radiant MLHub, the Future Harvest (CGIAR) centers, the National Aeronautics and Space Administration Food Security and Agriculture Program (NASA Harvest), the International Institute for Applied Systems Analysis (IIASA) citizen science platforms (LACO-Wiki and Geo-Wiki), as well as from individual project contributions. Data of 2016 onwards were collected, harmonised, and annotated. The data sets spatial, temporal, and thematic quality were assessed applying rules developed in this research. Currently, the repository holds around 75 million harmonised observations with standardized metadata of which a large share is available to the public. The repository, funded by ESA through the WorldCereal project, can be used for either the calibration of image classification deep learning algorithms or the validation of Earth Observation generated products, such as global cropland extent and maize and wheat maps. We recommend continuing and institutionalizing this reference data initiative e.g. through GEOGLAM, and encouraging the community to publish land cover and crop type data following the open science and open data principles.

Nitrogen cycles in global croplands altered by elevated CO2

Nitrogen cycles in global croplands altered by elevated CO2

Constraining the Capacity of Global Croplands to CO2 Drawdown via Mineral Weathering.

Terrestrial enhanced weathering of alkaline silicate minerals is a promising climate change mitigation strategy with the potential to limit the global temperature rise. The formation and accumulation of pedogenic carbonate and bicarbonate in soils/subsoils and groundwater offers a large sink for C storage; the amount of soil inorganic carbon (SIC) presently held within soils has been estimated to be 720-950 Gt of C. These values can be augmented by the addition of a variety of calcium and magnesium silicates via enhanced weathering. While the concept of the application of finely milled silicate rocks for faster weathering rates is well established, there has been limited discussion on the role of local climate, natural SIC content (i.e., the SIC innately present in the soil), and soil pH (among other important agronomic factors) on silicate weathering when applied to croplands, especially in view that the aim is to establish terrestrial enhanced weathering as a carbon dioxide removal (CDR) strategy on a global scale. In this work, we emphasized the importance of soil pH and soil temperature on silicate weathering and looked to estimate an upper limit of (i.e., constrain) the global capacity until the year 2100 for enhanced rock weathering (ERW) to draw down CO2 in the form of accumulated pedogenic carbonate or soluble bicarbonate. We assessed the global spatial distribution of cropland soil pH, which serves as a proxy for local innate SIC; annual rate of pluvial (rainfall) precipitation; and soil temperature, and found that the potential CO2 drawdown difference between faster and slower weathering silicates is narrower in Asia, Africa, and South America, while the gap is larger for Europe, North America, and Oceania.

Land Resources in Organic Agriculture: Trends and Challenges in the Twenty-First Century from Global to Croatian Contexts

Organic agriculture (OA) is a continuously growing global concept that emphasizes the use of sustainable and environmentally-friendly practices. By adopting OA, it is possible to improve ecosystems services, increase biodiversity, decrease environmental pollution, reduce carbon footprints and mitigate greenhouse gas emissions, generating food that is free from harmful residues of agrochemicals, thereby enhancing food safety and security. This study provides a comprehensive review of the latest insights on the global utilization of land resources in OA, focusing particularly on some EU countries that experienced a notable and rapid progress in organic farming during the past two decades. With negligible 1.6% (75 Mha) of global cropland is currently dedicated to OA, there is ample opportunity to expand the adoption of OA and realize its multi-beneficial potential for farmers (by premium prices), and consumers (by healthier and nutritious food). The importance of OA has been recognized by the most recent EU agro-environmental policies and green strategies, with an ambitious goal to have at least 25% of agroecosystems under organic management by 2030. Despite numerous financial supports and a multifold increase in OA land area, many member states are unlikely to achieve this goal, including Croatia, which currently has a share of only 8% (~109,000 ha) of lands in OA. Furthermore, converting conventional land to organic farming has not always led to an increase in value-added final OA products. EU policies related to OA have been focused on the area of land cultivated organically, rather than overall production performance, and financial subsidies have been essential to achieve this policy. Therefore, some of critical obstacles and challenges for OA under rising pressures due to global climate change, public health and geopolitical crises need to be managed by specifically designed policies and regulations, which would contribute to more sustainable OA, i.e., food safety and security.

Changing cropland in changing climates: quantifying two decades of global cropland changes

Climate change is impacting global crop productivity, and agricultural land suitability is predicted to significantly shift in the future. Responses to changing conditions and increasing yield variability can range from altered management strategies to outright land use conversions that may have significant environmental and socioeconomic ramifications. However, the extent to which agricultural land use changes in response to variations in climate is unclear at larger scales. Improved understanding of these dynamics is important since land use changes will have consequences not only for food security but also for ecosystem health, biodiversity, carbon storage, and regional and global climate. In this study, we combine land use products derived from the Moderate Resolution Imaging Spectroradiometer with climate reanalysis data from the European Centre for Medium-Range Weather Forecasts Reanalysis v5 to analyze correspondence between changes in cropland and changes in temperature and water availability from 2001 to 2018. While climate trends explained little of the variability in land cover changes, increasing temperature, extreme heat days, potential evaporation, and drought severity were associated with higher levels of cropland loss. These patterns were strongest in regions with more cropland change, and generally reflected underlying climate suitability—they were amplified in hotter and drier regions, and reversed direction in cooler and wetter regions. At national scales, climate response patterns varied significantly, reflecting the importance of socioeconomic, political, and geographic factors, as well as differences in adaptation strategies. This global-scale analysis does not attempt to explain local mechanisms of change but identifies climate-cropland patterns that exist in aggregate and may be hard to perceive at local scales. It is intended to supplement regional studies, providing further context for locally-observed phenomena and highlighting patterns that require further analysis.

Estimating the Global Influence of Cover Crops on Ecosystem Service Indicators in Croplands With the LPJ‐GUESS Model

AbstractCover crops (CCs) can improve soil nutrient retention and crop production while providing climate change mitigation co‐benefits. However, quantifying these ecosystem services across global agricultural lands remains inadequate. Here, we assess how the use of herbaceous CCs with and without biological nitrogen (N) fixation affects agricultural soil carbon stocks, N leaching, and crop yields, using the dynamic global vegetation model LPJ‐GUESS. The model performance is evaluated with observations from worldwide field trials and modeled output further compared against previously published large‐scale estimates. LPJ‐GUESS broadly captures the enhanced soil carbon, reduced N leaching, and yield changes that are observed in the field. Globally, we found that combining N‐fixing CCs with no‐tillage technique could potentially increase soil carbon levels by 7% (+0.32 Pg C yr−1 in global croplands) while reducing N leaching loss by 41% (−7.3 Tg N yr−1) compared with fallow controls after 36 years of simulation since 2015. This integrated practice is accompanied by a 2% of increase in total crop production (+37 million tonnes yr−1 including wheat, maize, rice, and soybean) in the last decade of the simulation. The identified effects of CCs on crop productivity vary widely among main crop types and N fertilizer applications, with small yield changes found in soybean systems and highly fertilized agricultural soils. Our results demonstrate the possibility of conservation agriculture when targeting long‐term environmental sustainability without compromising crop production in global croplands.

Cropland and rooftops: the global undertapped potential for solar photovoltaics

The utilization of cropland and rooftops for solar photovoltaics (PVs) installation holds significant potential for enhancing global renewable energy capacity with the advantage of dual land-use. This study focuses on estimating the global area suitable for agrivoltaics (PV over crops) and rooftop PVs by employing open-access data, existing literature and simple numerical methods in a high spatial resolution of 10 km × 10 km. For agrivoltaics, the suitability is assessed with a systematic literature review on crop-dependent feasibility and profitability, especially for 18 major crops of the world. For rooftop PV, a non-linear curve-fitting method is developed, using the urban land cover to calculate the PV-suitable built-up areas. This method is then verified by comparing the results with open-access building footprints. The spatially resolved suitability assessment unveils 4.64 million km2 of global PV-usable cropland corresponding to a geographic potential of about 217 Terawatts (TW) in an optimistic scenario and 0.21 million km2 of rooftop-PV suitable area accounting for about 30.5 TW maximum installable power capacity. The estimated suitable area offers a vast playground for energy system analysts to undertake techno-economic assessments, and for technology modellers and policy makers to promote PV implementation globally with the vision of net-zero emissions in the future.