Search

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

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).

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'

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

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