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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

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

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

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.