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Context or problem: The sustainability of traditional maize-wheat (M-W) double cropping in the North China Plain (NCP) is threatened by excessive nitrogen (N) input and surplus. Meanwhile, there is strong market demand of more protein and oil crops, such as soybean or peanut. Incorporation of legumes into M-W via intercropping with maize is emerging in the NCP to foster China's self-sufficiency for edible oils and proteins. Objective or research question: It is unknown how such a change in cropping system affects the required annual fertilizer N input, and the resulting N-use efficiency (NUE) and N surplus (Ns). Methods: We conducted a four-year field experiment involving four N fertilizer rates and rotations of winter wheat with six different summer crops: maize (conventional and density-increased), peanut (P), soybean (S), and intercrops of density-increased maize and peanut or soybean (MP, MS). The “three-quadrant diagram” and NUE proposed by the EU Nitrogen Expert Panel (EUNEP) were used to assess NUE at crop and cropping system level, respectively. Results: Land equivalent ratios of N uptake in intercrops averaged 1.02–1.07 while N fertilizer equivalent ratios averaged 1.15–1.20, indicating more efficient N uptake and more yield per unit fertilizer than sole crops. Intercropped maize exhibited greater N acquisition efficiency than sole maize. Inclusion of intercrops lowered required annual N inputs and Ns and increased the apparent recovery efficiency (RE) of applied N and EUNEP-NUE. Soybean was a more productive and N-use efficient companion species for maize than peanut. Increasing N decreased RE and EUNEP-NUE while elevated Ns of all rotation systems. N productivity responses of each rotation system to increasing N followed a “linear-plateau” model. Compared to M-W with an optimal 240–360 kg N/ha, MS-W with 210–320 kg N/ha saved 11.1–12.5% fertilizer, increased N uptake by 12.4–16.0%, augmented RE from 36.0–37.0% to 47.8%, increased EUNEP-NUE from 0.50 to 0.

Crop yields are determined by the biophysical environment and by farm management decisions, which in turn depend on socio-economic conditions of the farm(er). The interaction of these factors results in spatial and temporal yield variability. We assessed ware potato yield variability in the Netherlands across four agronomically relevant scales (among provinces, farms and fields and within fields) using five datasets with data on potato yield across space and time. Furthermore, we disseminated an online questionnaire among farmers to identify the perceived yield gap and the key yield gap explaining factors at farm level. Spatial yield variability was largest among fields, with a standard deviation of 8.5–11.1 t ha−1, and within fields, with a standard deviation of 7.7–8.7 t ha−1. Spatial yield variability decreased at higher aggregation levels, i.e., the standard deviation of among-farm yield variability was 4.0–6.1 t ha−1 and that of among-provinces 1.6–3.5 t ha−1. Mean yields of the datasets ranged from 46 to 52 t ha−1. Temporal yield variability explained 10–55 % of the total observed variation in crop yield and its magnitude was equal or larger than the spatial yield variability for almost all datasets. Farmers estimated the ware potato yield gap at 13–18 t ha−1, corresponding to 20–24 % of estimated yield potential, depending on the soil type and variety. Water deficit and water excess were considered the most important yield gap explaining biophysical factors. In addition, soil structure was an important biophysical factor on clay soils and diseases on sandy soils. Irrigation and fertilization were identified as the most important yield gap explaining management factors, whereas legislation and potato prices were identified as the key socio-economic factors influencing potato yields. However, the perceived yield gap explaining factors varied with soil type, variety and year. We conclude that reducing potato yield variability in the Netherlands can be achieved b

Context: Collection and analysis of large volumes of on-farm production data are widely seen as key to understanding yield variability among farmers and improving resource-use efficiency. Objective: The aim of this study was to assess the performance of statistical and machine learning methods to explain and predict crop yield across thousands of farmers’ fields in contrasting farming systems worldwide. Methods: A large database of 10,940 field-year combinations from three countries in different stages of agricultural intensification was analyzed. Random effects models were used to partition crop yield variability and random forest models were used to explain and predict crop yield within a cross-validation scheme with data re-sampling over space and time. Results: Yield variability in relative terms was smallest for wheat and barley in the Netherlands and for wheat in Ethiopia, intermediate for rice in the Philippines, and greatest for maize in Ethiopia. Random forest models comprising a total of 87 variables explained a maximum of 65 % of cereal yield variability in the Netherlands and less than 45 % of cereal yield variability in Ethiopia and in the Philippines. Crop management related variables were important to explain and predict cereal yields in Ethiopia, while predictive (i.e., known before the growing season) climatic variables and explanatory (i.e., known during or after the growing season) climatic variables were most important to explain and predict cereal yield variability in the Philippines and in the Netherlands, respectively. Finally, model cross-validation for regions or years not seen during model training reduced the R2 considerably for most crop x country combinations, while for wheat in the Netherlands this was model dependent. Conclusion: Big data from farmers’ fields is useful to explain on-farm yield variability to some extent, but not to predict it across time and space. Significance: The results call for moderate expectations towards big da

Cumulative crop recovery of synthetic fertiliser nitrogen (N) over several cropping seasons (legacy effect) generally receives limited attention. The increment in crop N uptake after the first-season uptake from fertiliser can be expressed as a fraction (∆RE) of the annual N application rate. This study aims to quantify ∆RE using data from nine long-term experiments (LTEs). As such, ∆RE is the difference between first season (RE1st) and long-term (RELT) recovery of synthetic fertiliser N. In this study, RE1st was assessed either by the 15N isotope method or by a zero-N subplot freshly superimposed on a long-term fertilised LTE treatment plot. RELT was calculated by comparing N uptake in the total aboveground crop biomass between a long-term fertilised and long-term control (zero-N) treatment. Using a mixed linear effect model, the effects of climate, crop type, experiment duration, average N rate, and soil clay content on ∆RE were evaluated. Because the experimental setup required for the calculation of ∆RE is relatively rare, only nine suitable LTEs were found. Across these nine LTEs in Europe and North America, the mean ∆RE was 24.4% (±12.0%, 95% CI) of annual N application, with higher values for winter wheat than for maize. This result shows that fertiliser-N retained in the soil and stubble may contribute substantially to crop N uptake in subsequent years. Our results suggest that an initial recovery of 43.8% (±11%, 95% CI) of N application may increase to around 66.0% (±15%, 95% CI) on average over time. Furthermore, we found that ∆RE was not clearly related to long-term changes in topsoil total N stock. Our findings show that the—often used—first-year recovery of synthetic fertiliser N application does not express the full effect of fertiliser application on crop nutrition. The fertiliser contribution to soil N supply should be accounted for when exploring future scenarios on N cycling, including crop N requirements and N balance schemes. Highlights: Nine lon

CONTEXT: Recent studies on yield gap analysis for rice in Southeast Asia revealed different levels of intensification across the main ‘rice bowls’ in the region. Identifying the key crop management and biophysical drivers of rice yield gaps across different ‘rice bowls’ provides opportunities for comparative analyses, which are crucial to better understand the scope to narrow yield gaps and increase resource-use efficiencies across the region. OBJECTIVE: The objective of this study was to decompose rice yield gaps into their efficiency, resource, and technology components and to map the scope to sustainably increase rice production across four lowland irrigated rice areas in Southeast Asia through improved crop management. METHODS: A novel framework for yield gap decomposition accounting for the main genotype, management, and environmental factors explaining crop yield in intensive rice irrigated systems was developed. A combination of crop simulation modelling at field-level and stochastic frontier analysis was applied to household survey data to identify the drivers of yield variability and to disentangle efficiency, resource, and technology yield gaps, including decomposing the latter into its sowing date and genotype components. RESULTS AND CONCLUSION: The yield gap was greatest in Bago, Myanmar (75% of Yp), intermediate in Yogyakarta, Indonesia (57% of Yp) and in Nakhon Sawan, Thailand (47% of Yp), and lowest in Can Tho, Vietnam (44% of Yp). The yield gap in Myanmar was largely attributed to the resource yield gap, reflecting a large scope to sustainably intensify rice production through increases in fertilizer use and proper weed control (i.e., more output with more inputs). In Vietnam, the yield gap was mostly attributed to the technology yield gap and to resource and efficiency yield gaps in the dry season and wet season, respectively. Yet, sustainability aspects associated with inefficient use of fertilizer and low profitability from high input levels shoul

A large database of individual farmer field data (n = 4,107) for rice production in the Northwestern Indo-Gangetic Plains of India was used to decompose rice yield gaps and to investigate the scope to reduce nitrogen (N) inputs without compromising yields. Stochastic frontier analysis was used to disentangle efficiency and resource yield gaps, whereas data on rice yield potential in the region were retrieved from the Global Yield Gap Atlas to estimate the technology yield gap. Rice yield gaps were small (ca. 2.7 t ha−1, or 20% of potential yield, Yp) and mostly attributed to the technology yield gap (ca. 1.8 t ha−1, or ca. 15% of Yp). Efficiency and resource yield gaps were negligible (less than 5% of Yp in most districts). Small yield gaps were associated with high input use, particularly irrigation water and N, for which small yield responses were observed. N partial factor productivity (PFP-N) was 45–50 kg grain kg−1 N for fields with efficient N management and approximately 20% lower for the fields with inefficient N management. Improving PFP-N appears to be best achieved through better matching of N rates to the variety types cultivated and by adjusting the amount of urea applied in the 3rd split in correspondance with the amount of diammonium-phosphate applied earlier in the season. Future studies should assess the potential to reduce irrigation water without compromising rice yield and to broaden the assessment presented here to other indicators and at the cropping systems level.

Yield gap (Yg) analysis is useful to map the scope for sustainable intensification of agriculture, but explaining and quantifying the underlying causes of yield gaps remains a considerable challenge. The objective of this study was to decompose maize yield gaps under different nitrogen (N) application rates and soil quality conditions across irrigated and rainfed cropping systems in China. A comprehensive database consisting of 5228 on-farm trials located in three major maize production regions of China was used for this purpose. The on-farm trials contained detailed information for four different treatments: fertilizer omission (control), optimal N rate (optimal N), 50% of optimal N rate (low N) and 150% of optimal N rate (high N). These were combined with biophysical and yield potential data from the Global Yield Gap Atlas (http://yieldgap.org). An analytical framework integrating stochastic frontier analysis and principles of production ecology was applied to decompose the overall maize yield gap into components of efficiency (and respective management and soil quality effects, Yg-M and Yg-S), resource (Yg-R), and technology Yg (Yg-T). The potential yield (Yp) of irrigated maize averaged 14.5 Mg/ha in Northeast China (NE) and 11.9 Mg/ha for North China Plain (NCP), and the water-limited potential yield (Yw) of rainfed maize averaged 12.0 Mg/ha in NE and 10.5 Mg/ha in Southwest China (SW), respectively. Maize yield gaps were highly variable across N treatments and cropping systems and ranged between 27–56% of Yp or Yw. Larger absolute yield gaps were observed in irrigated cropping systems in NE (4.8–8.1 Mg/ha) than in NCP (3.8–6.1 Mg/ha) and in rainfed cropping systems in NE (3.6–6.7 Mg/ha) and SW (2.8–5.9 Mg/ha). The components of the yield gap differed in size across cropping systems and N treatments. Yg-T was fairly small and consistent across N treatments ranging between 7.0% and 12.0% of Yp for irrigated maize and only ca. 2.0% of Yw for rainfed maize in NE.

PR, IFPRI3; DCA; ISI; 1 Fostering Climate-Resilient and Sustainable Food Supply; 4 Transforming Agricultural and Rural Economies; CRP3.2; CRP3.7; Africa Rising; Feed the Future Initiative, SAR, CGIAR Research Program on Maize (MAIZE); CGIAR Research Program on Livestock, Achieving SDG2 (zero hunger) in a situation of rapid global population growth requires a continued focus on food production. Farming not merely needs to sustainably produce nutritious diets, but should also provide livelihoods for farmers, while retaining natural ecosystems and services. Rather than focusing on production principles, this article explores the interrelations between farms and farming systems in the global food system. Evaluating farming systems around the world, we reveal a bewildering diversity. While family farms predominate, these range in size from less than 0.1 ha to more than 10,000 ha, and from hand hoe use to machine-based cultivation, enabling one person to plant more than 500 ha in a day. Yet, farming in different parts of the world is highly interdependent, not least because prices paid for farm produce are largely determined by global markets. Furthermore, the economic viability of farming is a problem, globally. We highlight trends in major regions of the world and explore possible trajectories for the future and ask: Who are the farmers of the future? Changing patterns of land ownership, rental and exchange mean that the concept of ‘what is a farm’ becomes increasingly fluid. Next to declining employment and rural depopulation, we also foresee more environmentally-friendly, less external input dependent, regionalised production systems. This may require the reversal of a global trend towards increasing specialisation to a recoupling of arable and livestock farming, not least for the resilience it provides. It might also require a slow-down or reversal of the widespread trend of scale enlargement in agriculture. Next to this trend of scale enlargement, small farms persist in Asia: consolidation of farms proceeds at a snail’s pace in South-east Asia and 70% of farms in India are ‘ultra-small’ – less than 0.05 ha. Also in Africa, where we find smallholder farms are much smaller than often assumed (< 1 ha), farming households are often food

Nitrogen (N) management is important for farmers to balance production, economic and environmental performance of their farms. This is particularly true in the intensive cropping systems of northwest Europe where tuber, root and bulb crops are cultivated in rotation with cereal crops and where an intensive livestock sector makes organic manures available at low cost for arable farmers. Here, we build upon a large database of farmer field data to assess mineral and organic fertiliser N performance, and its determinants, for the major arable crops in the Netherlands according to the guidelines provided by the EU N Expert Panel (EUNEP). The EUNEP framework quantifies N outputs and N inputs, N-use efficiency as the ratio between N outputs and N inputs (NUE in kg N output harvested per kg N input) and N surplus as the difference between N inputs and N outputs (Ns in kg N ha−1). As a next step, biophysical and crop management determinants of N performance were explored using data from different years, soil types and N management in relation to the amount, source, time and method of N applied. NUE was on average ca. 0.95 kg N kg−1 N for seed potato, sugar beet and spring onion, 0.87 kg N kg−1 N for ware potato, ca. 0.80 kg N kg−1 N for starch potato and winter wheat and, ca. 0.70 kg N kg−1 N for spring barley, all within or above the target range of 0.50–0.90 kg N kg−1 N proposed by the EUNEP. Ns was on average below the EUNEP threshold of 80 kg N ha−1 for all crops: 78 kg N ha−1 for ware potato and winter wheat, ca. 70 kg N ha−1 for starch potato, ca. 50 kg N ha−1 for spring barley, ca. 25 kg N ha−1 for sugar beet and spring onion and less than 20 kg N ha−1 for seed potato. Although average Ns was below 80 kg N ha−1, ca. 40% of the ware potato, starch potato and winter wheat fields analyzed had Ns above this threshold. The relatively high NUE combined with high Ns for most crops are the result of high N outputs (yields) combined with high N application rates. Moreover, hi