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The physiological age of potato seed tubers influences the subsequent field performance of the crop, conditioning its growth and development. This study investigates the effects of the physiological age of seed tubers on the field performance of four contrasting, currently widely grown cultivars (Agria, Festien, Innovator, Lady Claire) across three sites in the Netherlands over 3 years (Cycles 1—3). Seed tubers were produced annually at a single site under uniform conditions, with variations in physiological age induced by applying different storage temperatures (4, 7, 10, and/or 17 °C). Our previous study revealed varying effects of storage temperature on sprouting behaviour among cultivars, suggesting different rates of ageing. After completing the storage season, in the current study these seed tubers were planted in the field and subsequent crop performance was thoroughly phenotyped. Cultivar-specific effects of storage temperature were evident during early growth stages and when seed tubers were stored at 17 °C in Cycle 3. However, within the storage temperature range of 4 to 10 °C, the influence of storage temperatures was small or inconsistent. Overall, effects on canopy cover, stem and tuber number, yield and quality, and tuber yield explanatory variables were limited. The outcomes suggest that while very high storage temperatures should be avoided in regions with long crop cycles, stringent cold storage regimes are unnecessary to achieve optimal growth vigour in potato seed tubers of the studied cultivars in a temperate climate.

Context: Intensification of arable farming in Northwest Europe has led to high yields. However, inadequate use of external inputs such as nutrients, irrigation water and crop protection products has contributed to several environmental problems, such as nitrate leaching and losses of crop protection products. There is a need to reduce environmental losses and contribute to a more circular and sustainable agriculture in Northwest Europe. Objective: Here, we take ware potato production in the Netherlands as an example cropping system to assess if there is scope to reduce input levels and environmental impact of nutrient, water and crop protection product use without compromising yield. Methods: We determined variability in use and use efficiency of nitrogen (N), phosphorus (P), potassium (K) and pesticides as well as water productivity (WP) and yields of 96 on-farm ware potato fields in the Netherlands, on both clay and sandy soils. In addition, we assessed if relatively high performance could be achieved on multiple environmental indicators simultaneously. Results and conclusions: Average N surplus was 265 kg N ha−1 on clay soils and 139 kg N ha−1 on sandy soils and varied among fields by a factor three. Phosphorus and K input exceeded P and K output on clay soils by 33 and 105 kg ha−1, respectively, while on sandy soils P and K balances were close to zero. Mean WP was 43 kg dry matter (DM) mm−1 ha−1 and ranged from 30 to 60 kg DM mm−1 ha−1 for both soil types. In terms of crop protection product use, lowest and highest use differed by a factor four. Unexpectedly, yields did not increase with higher input rates, suggesting that lower input rates are sufficient to obtain current yields. Consequently, input rates were the most important drivers to explain variability in resource use efficiency and environmental impacts. At the same time, a comparison across multiple indicators simultaneously showed that it was possible to achieve relatively high yields with relatively

Crop models are the primary means by which agricultural scientists assess climate change impacts on crop production. Site-based and high-quality weather and climate data is essential for agronomically and physiologically sound crop simulations under historical and future climate scenarios. Here, we describe a bias-corrected dataset of daily agro-meteorological data for 109 reference weather stations distributed across key production areas of maize, millet, sorghum, and wheat in ten sub-Saharan African countries. The dataset leverages extensive ground observations from the Global Yield Gap Atlas (GYGA), an existing climate change projections dataset from the Inter-Sectoral Model Intercomparison Project (ISIMIP), and a calibrated crop simulation model (the WOrld FOod Studies –WOFOST). The weather data were bias-corrected using the delta method, which is widely used in climate change impact studies. The bias-corrected dataset encompasses daily values of maximum and minimum temperature, precipitation rate, and global radiation obtained from five models participating in the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6), as well as simulated daily growth variables for the four crops. The data covers three periods: historical (1995–2014), 2030 (2020–2039), and 2050 (2040–2059). The simulation of daily growth dynamics for maize, millet, sorghum, and wheat growth was performed using the daily weather data and the WOFOST crop model, under potential and water-limited potential conditions. The crop simulation outputs were evaluated using national agronomic expertise. The presented datasets, including the weather dataset and daily simulated crop growth outputs, hold substantial potential for further use in the investigation of future climate change impacts in sub-Saharan Africa. The daily weather data can be used as an input into other modelling frameworks for crops or other sectors (e.g., hydrology). The weather and crop growth data can provide key insights a

The physiological age of potato seed tubers evolves with chronological age, influenced by growth history and storage conditions. Effective quantification of physiological ageing requires a dynamic and flexible modelling approach. In this study, we conducted three cycles of field production and storage of seed tubers, using contrasting and widely grown cultivars. Different storage temperatures were employed as the sole treatment to introduce variation in the physiological age of the same cultivar. Throughout the storage seasons, periodic assessments of sprouting behaviour were conducted, both at the onset and at the end of a sprouting test. The total sprout dry weight per tuber produced in the sprouting test emerged as the best measure of seed tuber vigour, exhibiting cultivar-specific responses to storage temperatures. To capture these diverse sprouting behaviours, we devised a piecewise sprouting behaviour function. This is the first mathematical model that effectively describes the evolving sprouting patterns and provides biologically relevant parameters for the analysis of cultivar-specific sprouting trends. The temporal parameters of the function displayed sensitivity to storage temperature and year-to-year variation in growth conditions, highlighting distinct physiological phases. Consequently, the function facilitated insightful analysis of these key factors, enabling interpretation of underlying processes involved in the physiological ageing of potato seed tubers.

Nutrient budgets help to identify the excess or insufficient use of fertilizers and other nutrient sources in agriculture. They allow for the calculation of indicators, such as the nutrient balance (surplus if positive or deficit if negative) and nutrient use efficiency, that help to monitor agricultural productivity and sustainability across the world. We present a global database of country-level budget estimates for nitrogen (N), phosphorus (P) and potassium (K) on cropland. The database, disseminated in FAOSTAT, is meant to provide a global reference, synthesizing and continuously updating the state of the art on this topic. The database covers 205 countries and territories, as well as regional and global aggregates, for the period from 1961 to 2020. Results highlight the wide range in nutrient use and nutrient use efficiencies across geographic regions, nutrients, and time. The average N balance on global cropland has remained fairly steady at about 50-55kgha-1yr-1 during the past 15 years, despite increasing N inputs. Regional trends, however, show recent average N surpluses that range from a low of about 10kgNha-1yr-1 in Africa to more than 90kgNha-1yr-1 in Asia. Encouragingly, average global cropland N use efficiency decreased from about 59% in 1961 to a low of 43% in 1988, but it has risen since then to a level of 55%. Phosphorus deficits are mainly found in Africa, whereas potassium deficits occur in Africa and the Americas. This study introduces improvements over previous work in relation to the key nutrient coefficients affecting nutrient budgets and nutrient use efficiency estimates, especially with respect to nutrient removal in crop products, manure nutrient content, atmospheric deposition and crop biological N fixation rates. We conclude by discussing future research directions and highlighting the need to align statistical definitions across research groups as well as to further refine plant and livestock coefficients and expand estimates to all agr

Sub-Saharan Africa's (SSA) demand for cereals is projected to more than double by 2050. Climate change is generally assumed to add to the future challenges of the needed productivity increase. This study aimed to assess (i) the potential climate change impact on four key rainfed cereals (maize, millet, sorghum and wheat) in ten SSA countries namely Burkina Faso, Ghana, Mali, Niger, Nigeria, Ethiopia, Kenya, Tanzania, Uganda, and Zambia using local data and national expertise, and (ii) the potential of cultivar adaptation to climate change for the four crops. We assessed effects on rainfed potential cereal yields per crop and aggregated these to regional level in West (WA), East and Southern Africa (ESA). We made use of a rigorous agronomic dataset for 120 locations in the ten countries and performed simulations of rainfed potential yield (Yw) using bias-corrected climate data from five GCMs, three time periods (1995–2014 as baseline, 2040–2059, and 2080–2099) and two scenarios (SSP3–7.0 as business as usual and SSP5–8.5 as pessimistic). We tested whether better adapted cultivars (taken from the pool of cultivars currently employed in the ten countries) could compensate for climate change. Results showed that climate change decreased aggregated Yw of cereals by around 6% in ESA by 2050, whereas projected impacts in WA were not significant. In 2090, however, the projected impact of climate change in both WA (−24%) and ESA (−9%). was significant. Cultivar adaptation partially compensated the negative impact of climate change. With the adaptation approach, 87% and 82% of potential production in ESA was estimated to occur with higher average Yw and lower variability in, respectively, 2050 and 2090, compared to the baseline period. In WA 67% and 43% of the potential production was estimated to experience such positive effects in 2050 and 2090, respectively. These results highlight remaining adaptation challenges for 13% (2050) and 18% (2090) in ESA and 33% (2050) and 57%

Context: Yield gap analyses are useful to assess and benchmark the productivity of cropping systems. Often such analyses are performed at higher aggregation levels. As a result, these studies lack the detail to explain yield gaps at field level and hence make it difficult to translate findings into precise recommendations to farmers and extensionists. Objective: This study provides a detailed approach for yield gap assessments at field level through coupling frequent field monitoring in farmers’ fields with crop growth modelling. We used ware potato production in the Netherlands as a case to study yield gaps at field level, as average productivity is high whilst yields are still highly variable among fields, and as ware potato is an important cash crop for farmers. Methods: Over two growing seasons, 96 ware potato fields were monitored throughout the growing season on a biweekly basis, taking measurements on soil, crop growth and yield. The crop growth model SWAP-WOFOST was used to simulate potential and water-limited potential yields. Various statistical methods were used to quantify yield gap explaining factors. Results: The average yield gap ranged from 20 to 31% depending on the year and soil type. Among fields, the yield gap ranged from 0 to 51%. On clayey soils, the yield gap was attributed mostly to oxygen stress. On sandy soils, the yield gap was determined mostly by drought stress in 2020, a relatively dry year, and by reducing factors (pests, diseases and poor agronomic practices) in 2021, an average year in terms of precipitation. The type of reducing factors differed per field. Furthermore, we found that earlier planting and later harvesting can increase yields, as Yp is radiation-limited. Conclusions: Overall, there is limited scope to narrow the yield gap as current ware potato production is already close to 80% of the potential yield, which is assumed to be approximately the maximum farmers can attain. However, yield and resource use efficiency gains

The dataset described includes second bias corrected daily weather data using ground-based weather data and simulated crop growth data under potential and water-limited potential conditions for 109 sites across ten countries in SSA including Burkina Faso, Ghana, Mali, Niger, Nigeria, Ethiopia, Kenya, Tanzania, Uganda, and Zambia. Whereas the weather data covers all sites, the simulated growth data for the four crops (maize, millet, sorghum, wheat) covers subsets of the sites depending on the geographic distribution of cultivation areas for the given crop. The stations can represent 65% of the harvested area for maize, 90% for millet, 83% for sorghum, and 59% for wheat the four crops together can represent 72% of the total harvested area in those countries. The data covers three time periods: historical (1995-2014), 2030 (2020-2039), and 2050 (2040-2059).

Identifying pathways to circular agriculture requires a profound understanding of nutrient flows and losses throughout the food system, and of interactions between biophysical conditions, land use, food production and food consumption. We quantified nitrogen (N) and phosphorus (P) flows of the food system of the North-Netherlands (NN) region and of its 30 subregions varying in biophysical and socio-economic conditions. The food system included agriculture, food processing, consumption, and waste processing. Nitrogen use efficiency (NUE), phosphorus use efficiency (PUE) and the nutrient cycling counts were calculated. Results show a low NUE (25%) and PUE (59%) of the food system. External inputs were used to maintain high yields and production. Nutrient cycling was very limited with losses from agriculture ranging from 143 to 465 kg N ha−1 y−1 and 4 to 11 kg P ha−1 y−1. Food system losses ranged from 181 to 480 kg N ha−1 y−1 and from 7 to 31 kg P ha−1 y−1 and varied with biophysical conditions, population density and farming systems. Large losses were associated with livestock farming and farming on drained peat soils. Food system efficiency was strongly associated with the utilization of produce. We conclude that increasing circularity requires tailoring of agriculture to local biophysical conditions and food system redesign to facilitate nutrient recycling. Steps towards circularity in NN include: matching livestock production to feed supply from residual flows and lands unsuitable for food crops, diversifying crop production to better match local demand and increasing waste recovery.

This file contains the R script and data used to calculate nitrogen and phosphorus flows and indicators for the north Netherlands food system. These scripts were written for the paper: 'Why is nutrient cycling in food systems so limited? A case study from the North-Netherlands region'

CONTEXT: Despite recent improvements in living standards, a substantial proportion of farm households in sub-Saharan Africa (SSA) is food insecure, and increasing crop productivity could help address this problem. OBJECTIVE: We estimated the effect of increasing maize yields with mineral fertilizer on household food security and on regional and national maize supply in two East African countries - Uganda and Tanzania. METHODS: We estimated maize yield response to nitrogen (N) fertilization with a machine learning model trained on 15,952 observations of maize responses to fertilizer across SSA. Together with spatial price data, we used this model to quantify the profit-maximizing N fertilizer input for a nationally-representative sample of 4188 agricultural households in the two countries. We computed a food availability indicator for all households. RESULTS AND CONCLUSIONS: The mean profit-maximizing N input was 82 kg/ha in Tanzania, but it was much lower in Uganda (24 kg/ha) mostly because of less favorable prices. The profit-maximizing N input was above the reported N input for 95% of the households in Tanzania and for 43% of the households in Uganda. It was predicted to increase the food availability ratio of food insecure maize growers by 95% in Tanzania, and by 25% in Uganda. The administrative regions where maize supply could increase most were not the same as the regions where the increase in household-level food security was largest. With increased fertilization, food insecure maize growing households (35% in Tanzania and 42% in Uganda) could only contribute about 20% of the overall increase in maize supply, whereas the 20 to 30% food secure households that have a larger area planted with maize could contribute >60%. SIGNIFICANCE: Our study makes two key contributions: i) a substantial increase in national maize supply is more likely to come from already food secure households with relatively large farms, while food insecure households with small farms may n

In many places on earth, livestock and feed production are decoupled, as feed is grown in one region and fed to livestock in another. This disrupts nutrient cycles by depleting resources in feed producing regions and accumulating resources in livestock areas, which leads to environmental degradation. One solution is to recouple livestock and feed production at a more local level, which enhances nutrient circularity. Recoupling livestock and feed production creates a natural ceiling for livestock numbers based on the feed producing capacity of a region. In this study we assess the consequences of recoupling livestock and feed production (i.e., by avoiding the import and export of animal feed) on ammonia and greenhouse gas (GHG) emissions, with and without feed-food competition. To this end, we used FOODSOM, an agro-ecological food system optimisation model representing the Dutch food system in this study. The Netherlands is one example of a region with high livestock densities and resource accumulation. We found that recoupling decreased livestock numbers (beef cattle: −100 %; dairy cattle: −29 %; broiler chickens: −57 %; laying hens: −67 %; pigs: −62 %; sheep −100 %) and animal-sourced food exports (−59 %) while still meeting the current human diet in the Netherlands. Consequently, ammonia emissions and GHG emissions decreased, and the nitrogen use efficiency increased from 31 % to 38 % at the food systems level. Recoupling alone was almost sufficient to meet national emission targets. Fully meeting these targets required further small changes in livestock numbers. Avoiding feed-food competition decreased livestock productivity and GHG emissions but did not improve nitrogen use efficiency. Total meat production could not meet domestic consumption levels while avoiding feed-food competition, and resulted in additional beef cattle. We show that recoupling livestock and feed production is a promising next step to enhance circularity while decreasing agricultures enviro

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

Partial shifts from animal-based to plant-based proteins in human diets could reduce environmental pressure from food systems and serve human health. Grain legumes can play an important role here. They are one of the few agricultural commodities for which Europe is not nearly self-sufficient. Here, we assessed area expansion and yield increases needed for European self-sufficiency of faba bean, pea and soybean. We show that such production could use substantially less cropland (4–8%) and reduce GHG emissions (7–22% current meat production) when substituting for animal-derived food proteins. We discuss changes required in food and agricultural systems to make grain legumes competitive with cereals for farmers and how their cultivation can help to increase sustainability of European cropping systems.

Agricultural nutrient budgets help to identify an excess or an insufficiency in use of fertilizers and other nutrient sources. Nutrient budgets allow for indicators such as the nutrient balance (surplus or deficit) and nutrient use efficiency to be estimated. This can help in the monitoring of agricultural productivity and sustainability globally. The present dataset is a global database of country-level budget estimates for nitrogen (N), phosphorus (P) and potassium (K) in cropland. The database is disseminated in FAOSTAT and provides a global reference, synthesizing and continuously updating the state-of-the-art on this topic. The database covers the period 961 to 2020 for 205 countries and territories, as well as regional and global aggregates. Results indicate the wide range in nutrient use and use efficiencies across regions, nutrients, and time. This dataset introduces improvements over previous work in relation to key nutrient coefficients affecting nutrient budgets and use efficiency estimates. This is especially for nutrient removal in crop products, manure nutrient content, atmospheric deposition and crop biological N fixation rates.

Estimates of crop nutrient removal (as crop products and crop residues) are an important component of crop nutrient balances. Crop nutrient removal can be estimated through multiplication of the quantity of crop products or crop residues (removed) by the nutrient concentration of those crop products and crop residue components respectively. Data for quantities of crop products removed at a country level are available through FAOSTAT (https://www.fao.org/faostat/en/), but equivalent data for quantities of crop residues are not available at a global level. However, quantities of crop residues can be estimated if the relationship between quantity of crop residues and crop products is known. Harvest index (HI) provides one such indication of the relationship between quantity of crop products and crop residues. HI is the proportion of above-ground biomass as crop products and can be used to estimate quantity of crop residues based on quantity of crop products. Previously, meta-analyses or surveys have been performed to estimate nutrient concentrations of crop products and crop residues and harvest indices (collectively known as crop coefficients). The challenges for using these coefficients in global nutrient balances include the representativeness of world regions or countries. Moreover, it may be unclear which countries or crop types are actually represented in the analyses of data. In addition, units used among studies differ which makes comparisons challenging. To overcome these challenges, data from meta-analyses and surveys were collated in one dataset with standardised units and referrals to the original region and crop names used by the sources of data. Original region and crop names were converted into internationally recognised names, and crop coefficients were summarised into two Tiers of data, representing the world (Tier 1, with single coefficient values for the world) and specific regions or countries of the world (Tier 2, with single coefficient values for

Current ware potato yields in the Netherlands are approximately 70% of their potential. It is hypothesised by several stakeholders within the potato value chain that part of the potato yield gap is caused by a lack of phosphorus (P) and potassium (K) application. In this study, we analysed for 46 farmers’ fields if increasing P and K fertiliser application rates led to a higher yield and yield quality. We found that, on average, increased P and K fertiliser application did not result in a significantly higher yield for two currently cropped potato cultivars on two different soil types in the Netherlands (Innovator on clay soils and Fontane on sandy soils) and in two years (2019 and 2020). However, on sandy soils at relatively lower farmer K application rates, our K application led to a small positive yield response up to 5 t ha−1. On clay soils, there was an average positive yield response to our K application at lower yield levels of the control. For P, we did not find any correlation between yield response to P application and the amount of P applied by farmers or any of the measured soil parameters. In terms of yield quality, the K application led to a slight reduction in underwater weight on sandy soils in 2019 and a slight increase in the yield of large tubers in 2020. We conclude that, although in some fields there was a small positive yield effect of increased K application, increasing P and K application rates will not narrow the potato yield gap and improve potato yield quality in the Netherlands. Instead, increasing the P and K application will decrease P and K use efficiency and hence is not recommended from an environmental and economic perspective.

Circular resource use in agriculture and food systems could play an important role when aiming for sufficient food output with limited environmental impact and resource depletion. Circularity, however, is not a goal in itself. With respect to nutrient use and emissions, agricultural system sustainability is currently commonly assessed by nutrient output/input ratio (O/I, nutrient use efficiency) or surplus per ha (I–O). OBJECTIVE: Our aim is to assess how these sustainability indicators are related to nutrient cycling. METHODS: Starting from basic circularity concepts, a set of equations (frame) is presented that relates nitrogen (N) and phosphorus (P) cycling to food product output, or to food use by human consumers. Circularity indicators express how many times a nutrient input cohort completes a full cycle (CyCt), or passes through the system's top trophic compartment (UseCt). Examples of such compartments are the crop (arable systems), the herd (livestock farms), and the human population (regional food systems). UseCt governs export in useful product. The frame allows to predict equilibrium O/I from system properties, and to attribute parts of O/I to direct (linear) and cycled flow. CyCtR quantifies how many cycles could be completed by nutrients in absence of product export. CyCtR allows to assess the efficacy of returning waste from exported products. Above indicators are compared against Finn cycling index and Figge circularity index, more commonly used in ecological and industrial research respectively. All indicators are calculated for systems of increasing complexity: (i) a UK wheat field, (ii) a Dutch dairy farm, and (iii) the Flanders regional food system. Their responses to changes in system properties are analysed for examples ii and iii. RESULTS AND CONCLUSIONS: Nutrient flows in UK arable field and Flanders are almost linear. In UK arable field, O/I equals 0.74 (N) and 0.66 (P), with small contributions from cycled flow (9% for N, 5% for P). In Fland

It is not known whether dietary guidelines proposing a limited intake of animal protein are compatible with the adoption of circular food systems. Using a resource allocation model, we compared the effects of circularity on the supply of animal-source nutrients in Europe with the nutritional requirements of the EAT-Lancet reference diet. We found the two to be compatible in terms of total animal source proteins but not specific animal-source foods; in particular, the EAT-Lancet guidelines recommend larger quantities of poultry meat over beef and pork, while a circular food system produces mainly milk, dairy-beef and pork. Compared with the EAT-Lancet reference diet, greenhouse gas emissions were reduced by up to 31% and arable land use reduced by up to 42%. Careful consideration of the feasible substitutability between animal-source foods is needed to define potential roles of animal products in circular human diets.

Current potential yield simulations of potato crops can be lower than actual yields observed in fields and the potential yield simulations miss important cultivar differences, for instance in terms of cultivar earliness. This causes issues not only for potential yield simulations but also when estimating the limiting effects of water (and nitrogen) and yield gaps in farmers’ fields. To address this issue, we recalibrated potential yield simulations with WOFOST for five commercial Dutch potato cultivars. An experiment conducted during the 2020 season was used for model calibration while the 2019 version of the same experiment was used for evaluation. The focus of the calibration was on phenology, photosynthesis, and biomass allocation. To aid the latter, the WOFOST biomass allocation table functions were replaced with sigmoid functions and biomass reallocation from haulm to tubers was introduced. In total 19 parameters were adjusted and/or introduced. Twelve of these parameters were not cultivar-specific, e.g. the specific leaf area over the plant developmental stage (SLATB), and the maximum leaf CO2 assimilation rate as function of developmental time (AMAXTB). Seven parameters differed between the cultivars, e.g. the thermal time between emergence and tuber initiation (TSUM1), and most of these were correlated to cultivar earliness. For the calibration year, final yields were underestimated by on average 5.4%, while for the evaluation year the final yield was overestimated by 4.4%. In comparison, the final dry matter yields ranged from 16.4 to 18.5 t ha−1 in 2019 and 15.5–17.5 t ha−1 in 2020. The old WOFOST parameters for potato resulted in an underestimation of 23.7% and 28.7% for, respectively, 2019 and 2020. Improving the parameter sets greatly increased accuracy of the simulations and provided insight in which parameters are cultivar specific and related to cultivar-earliness.

Reliable estimates of crop nitrogen (N) uptake and offtake are critical in estimating N balances, N use efficiencies and potential losses to the environment. Calculation of crop N uptake and offtake requires estimates of yield of crop product (e.g. grain or beans) and crop residues (e.g. straw or stover) and the N concentration of both components. Yields of crop products are often reasonably well known, but those of crop residues are not. While the harvest index (HI) can be used to interpolate the quantity of crop residue from available data on crop product yields, harvest indices are known to vary across locations, as do N concentrations of residues and crop products. The increasing availability of crop data and advanced statistical and machine learning methods present us with an opportunity to move towards more locally relevant estimates of crop harvest index and N concentrations using more readily available data. The aim of this study was to investigate whether improved estimates of maize crop HI and N concentrations of crop products and crop residues can be based on crop data available at the global scale, such as crop yield, fertilizer application rates and estimates of yield potential. Experiments from 1487 different locations conducted across 31 countries were used to test various prediction models. Predictions from mixed-effects models and random forest machine learning models provided reasonable levels of prediction accuracy (R2 of between 0.33 and 0.68), with the random forest method having greater accuracy. Although the mixed-effects prediction models had lower prediction accuracy than random forest, they did provide better interpretability. Selection of which method to use will depend on the objective of the user. Here, the random forest and mixed-effects methods were applied to N in maize, but could equally be applied to other crops and other nutrients, if data becomes available. This will enable obtaining more locally relevant estimates of crop nutrien

Estimates of crop nitrogen (N) uptake and offtake are critical in estimating N balances, N use efficiencies and potential losses to the environment. Calculation of crop N uptake and offtake requires estimates of crop product yield (e.g. grain or beans) and crop residue yield (e.g. straw or stover) and the N concentration of both components. Yields of crop products are often reasonably well known, but those of crop residues are not. While the harvest index (HI) can be used to interpolate the quantity of crop residue from available data on crop product yields, harvest indices are known to vary across locations, as do N concentrations of residues and crop products. The increasing availability of crop data and advanced statistical and machine learning methods present us with an opportunity to move towards more locally relevant estimates of crop harvest index and N concentrations using more readily available data. This dataset includes maize field experiment data. It is a culmination of summary statistic data collected from the literature as well as raw data requested from various researchers and organisations from around the world. These data will enable more locally relevant estimates of crop nutrient offtake, nutrient balances and nutrient use efficiency at national, regional or global levels, as part of strategies towards more sustainable nutrient management., Estimates of crop nitrogen (N) uptake and offtake are critical in estimating N balances, N use efficiencies and potential losses to the environment. Calculation of crop N uptake and offtake requires estimates of crop product yield (e.g. grain or beans) and crop residue yield (e.g. straw or stover) and the N concentration of both components. Yields of crop products are often reasonably well known, but those of crop residues are not. While the harvest index (HI) can be used to interpolate the quantity of crop residue from available data on crop product yields, harvest indices are known to vary across locations, as do N concentrations of residues and crop products. The increasing availability of crop data and advanced statistical and machine learning methods present us with an opportunity to move towards more locally relevant estimates of crop harvest index and N concentrations using more readily available data. This dataset includes maize field experiment data. It is a culmination of summary statistic data collected from the literature as well as raw data requested from various researchers and organisations from around the world. These data will enable more locally relevant estimates of crop nutrient offtake, nutrient balances and nutrient use efficiency at national, regional or global levels, as part of strategies towards more sustainable nutrient management.

In the Central Rift Valley of Ethiopia (CRV), actual productivity of most cereals is less than 3 t ha−1 associated with low input use and poor crop management. After calibrating and validating the Agricultural Production System Simulator (APSIM) using experimental data, we explored and prioritized promising intensification options for rainfed maize and wheat that enable to narrow prevailing yield gaps in the CRV, and quantified trade-offs with the water balance and gross margins. We set up a factorial simulation experiment combining Genetic x Environment x Management factors that influence crop yield and water use at field scale to simulate yield and water balance components under current and future climate scenarios (pessimistic scenario for mid-century). Varietal selection and nitrogen (N) fertilization were the most important factors contributing to yield gap closure. Although yields were maximized with N application rates up to 250 kg−1 in most soils and varieties, maximum gross margin and maximum water use efficiency (WUE) were attained at lower N rates, associated with a small yield reduction compared to the maximum. There was a trade-off between intensification and increased absolute water use through transpiration, while the water use per kg product was decreased. However, location-specific N application rates that allow producing at least 80% of the water-limited potential yield (Yw) of maize and wheat resulted in high water use efficiencies as well as favorable cost-benefit ratios. Climate change was projected to lower yield as it advanced maturity, and to result in decreased drainage and increased soil evaporation across all variety, location and management combinations for both crops. Climate change reduced crop yield by 15–25% for wheat and 2–30% for maize. We conclude that the locally-calibrated APSIM model could be used to derive key lessons from the genetic, environment and management interactions, and generate information on sustainable intensificat

CONTEXT: Yield gap analysis plays an important role in determining potential food availability. The Global Yield Gap Atlas maps yield gaps of crops from point to regional scale across the globe. The calculated yield gaps are based on comparisons between modelled potential yields with actual farmers' yields derived from statistical sources. The calculations are subject to uncertainty due to various sources, including measurement errors, modelling limitations, and scaling issues. OBJECTIVES: An important goal of the Atlas is to convey an uncertainty evaluation of the yield gap analysis. The aim of this paper is to provide a practical methodology that can make the assessment of the uncertainty by experts explicit and accessible for users of the Atlas. METHODS: We developed an uncertainty protocol and guidelines listing several sources of uncertainty to be considered by country agronomists who were involved in the calculation of the yield gaps. These experts are asked to score the level of uncertainty of each source, as well as the relative impact of each source. Both scores are combined into uncertainty scores for each source. Aggregated uncertainty scores for yield gaps, potential and actual yields are mapped as colours in the Atlas to indicate ranking. Moreover, experts are encouraged to provide a justification for their scores, which are also made available to users of the Atlas. RESULTS AND CONCLUSIONS: The uncertainty protocol was applied to 189 country-crop combinations by fourteen experts. They ranked lack of data for model calibration, model sensitivity to specific conditions, weather data, and the data quality on cropping system as the most important uncertainty sources for potential yields. The quality of yield data was ranked as the highest source of uncertainty for actual yields. The justifications provided by experts suggest which uncertainty sources may be reducible with relatively little effort, while other uncertainty sources may be more difficult or im

To meet the goals of the Climate Agreement, policy makers consider incentivizing soil carbon sequestration by carbon credits to offset greenhouse gas emissions from agriculture, industry and other sectors. Therefore, the interest in monitoring soil organic matter dynamics is growing rapidly, yet factors affecting the precision of the monitoring results are rarely quantified. We used the rhetorical scheme “The seven circumstances” to structure the methodological aspects of setting up a soil organic matter (SOM) monitoring program. The rhetorical scheme was applied during four years on two grass-based dairy farms in the Netherlands to assess in detail: conversion of SOM weight concentrations to SOM stocks (HOW), the effect of soil depth and the integration of scales field vs. farm (WHERE), and the effect of sampling date and sampling frequency on SOM estimates (WHEN). We found that all three circumstances affect conclusions on SOM stocks or SOM dynamics strongly. Considerable variation was found in the relationship between soil bulk density and SOM weight concentration (i.e. pedotransfer curves) among fields, depth and literature reference equations. Therefore, preferably a site specific pedotransfer curve should be used when comparing SOM stocks based on SOM weight concentrations across sites. Large differences in trends of SOM stock changes over time were found between fields and sampling depths. We conclude that a sampling depth in grassland soils up to 60 cm may be relevant to capture the dynamics in deeper layers. Furthermore, for quantitative underpinning of carbon payment schemes, the whole farm should be monitored rather than a few fields as trends between fields are highly variable.

CONTEXT: In northern West Bengal and southern Bangladesh, Rabi crops, sown in winter and harvested in the spring, are an important source of income and nutrition for the target communities. In the study areas, NGOs and extension services have been engaging with farming communities on selecting suitable crops for the upcoming season. This engagement took place in the absence of quantitative tools to discuss trade offs and what-if scenarios to support an informed discussion. OBJECTIVE: The objective of this study was to design a crop-choice model to support extension agronomists in engaging with the farming communities more effectively using a quantitative analysis tool. In this process, we explored how agricultural system models can be inclusive and allow participation and eventual application by NGO and government extension agents, using a process of gender sensitive contribution. Methods: The crop-choice model includes several constraints related to available resources, including land, labour, capital and technologies to calculate optimal crop areas within the Rabi season. The crop choices are determined considering gender specific community perceptions of risks, labour use, market, price-volatility, self-consumption, water use and cash flow. The model was used to explore the consequences of different crop choices on income, gender specific labour, use of inputs and markets, and to reveal the trade-offs of pursuing different crop choice pathways in the context of agricultural intensification. The initial model was demonstrated to the farmers and the extension agents, and based on their reflections, it was fine-tuned further to make the engagement process more effective. For better communication with the participating farmers, the model related questions and findings were translated in the local language (Bengali). RESULTS AND CONCLUSIONS: We found that the modelling process can be made more inclusive from the outset by including farmers, NGO and extension agents as