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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. 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. 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. 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 low N surplus, high WP, and low crop protection product use. Hence, environmental impact could decrease substantially if all fields performed similar to the best performing fields. This study showed that it seems feasible to maintain high yield levels while reducing resource use in a substantial share of the potato production fields in the Netherlands. If put in practice, this will reduce losses to the environment and therefore contribute to a more sustainable but still productive agriculture. [Display omitted] • Variability in nutrient, water, and crop protection products use varied up to a factor four. • Yields did not differ between low and high input rates. • Resource use efficiency was at least 50% higher in best than in worst performing fields. • Environmental targets for N surplus were met in only 8–12% of fields. • There is scope to reduce input use and environmental impact, without compromising crop yield. [ABSTRACT FROM AUTHOR]

Abstract: A major bottleneck for data-based policy making is that data sources are collected, managed, and distributed by different institutions, residing in different locations, resulting in conceptual and practical problems. The use of dispersed data for agricultural systems research requires the integration of data sources, which means to ensure consistency in data interpretations, units, spatial and temporal scales, to respect legal regulations of privacy, ownership and copyright, and to enable easy dissemination of data. This paper describes the SEAMLESS integrated database on European agricultural systems. It contains data on cropping patterns, production, farm structural data, soil and climate conditions, current agricultural management and policy information. To arrive at one integrated database, a shared ontology was developed according to a collaborative process, which facilitates interdisciplinary research. The paper details this process, which can be re-used in other research projects for integrating data sources. [Copyright &y& Elsevier]

Abstract: Agricultural systems continuously evolve and are forced to change as a result of a range of global and local driving forces. Agricultural technologies and agricultural, environmental and rural development policies are increasingly designed to contribute to the sustainability of agricultural systems and to enhance contributions of agricultural systems to sustainable development at large. The effectiveness and efficiency of such policies and technological developments in realizing desired contributions could be greatly enhanced if the quality of their ex-ante assessments were improved. Four key challenges and requirements to make research tools more useful for integrated assessment in the European Union were defined in interactions between scientists and the European Commission (EC), i.e., overcoming the gap between micro–macro level analysis, the bias in integrated assessments towards either economic or environmental issues, the poor re-use of models and hindrances in technical linkage of models. Tools for integrated assessment must have multi-scale capabilities and preferably be generic and flexible such that they can deal with a broad variety of policy questions. At the same time, to be useful for scientists, the framework must facilitate state-of-the-art science both on aspects of the agricultural systems and on integration. This paper presents the rationale, design and illustration of a component-based framework for agricultural systems (SEAMLESS Integrated Framework) to assess, ex-ante, agricultural and agri-environmental policies and technologies across a range of scales, from field–farm to region and European Union, as well as some global interactions. We have opted for a framework to link individual model and data components and a software infrastructure that allows a flexible (re-)use and linkage of components. The paper outlines the software infrastructure, indicators and model and data components. The illustrative example assesses effects of a trade liberalisation proposal on EU’s agriculture and indicates how SEAMLESS addresses the four identified challenges for integrated assessment tools, i.e., linking micro and macro analysis, assessing economic, environmental, social and institutional indicators, (re-)using standalone model components for field, farm and market analysis and their conceptual and technical linkage. [Copyright &y& Elsevier]

• The framework of the EU N Expert Panel was applied to farmer field data covering the period 2015–2017. • N-use efficiency (NUE) was on average 0.7–1.0 kg N kg−1 N for Dutch arable crops. • Annual N surplus (Ns) was on average 20–80 kg N ha−1 for Dutch arable crops. • In Dutch farming, technological advances without reducing N inputs are unlikely to improve NUE or N surplus. • Highest NUE and lowest Ns can be achieved with mineral fertilisers, which comes with trade-offs at national level. 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, high NUE and small Ns were mostly associated with smaller N application rates and with the use of mineral fertilisers instead of organic fertilisers, while there were no clear relationships between the two indicators on the one hand with N application time or method on the other. We conclude NUE and Ns were on average within the EUNEP target range for most crops, but there are still a considerable number of under-performing farms where increases in NUE and reductions in Ns are possible through reducing N inputs. We recommend future research to assess the benefits of organic fertilisers from a circularity perspective at regional and national levels and to cross-validate the crop-specific results presented in this study with NUE assessments at cropping systems level. [ABSTRACT FROM AUTHOR]

Specialised dairy farms are challenged to be competitive and yet respect environmental constrains. A tighter integration of cropping and livestock systems, both in terms of feed and manure flows, can be beneficial for the farm economy and the environment. The greening of the direct payments, which was introduced in the European Union's greening reform in 2013, is assumed to stimulate the transition towards more sustainable systems. The aim of this study was to quantitatively assess the impacts of greening policies on important economic and environmental indicators of sustainability, and explore potential further improvements in policies. The Farm System SIMulator (FSSIM) bioeconomic farm model was used to simulate the consequences of scenarios of policy change on three representative dairy farms in Piedmont, Italy, i.e., an 'intensive', an 'extensive', and an 'organic' dairy farm. Results showed that in general, there is a large potential to increase the current economic performance of all of the farms. The most profitable activity is milk production, resulting in the allocation of all of the available farm land to feed production. Imposing feed self-sufficiency targets results in a larger adaptation of current managerial practice than the adaptations that are required due to the greening policy scenario. It was shown that the cropping system is not always able to sustain the actual herd composition when 90% feed self-sufficiency is imposed. Regarding the greening policies, it is shown that extensive and organic farms already largely comply with the greening constrains, and the extra subsidy is therefore a bonus, while the intensive farm is likely to sacrifice the subsidy, as adapting the farm plan will substantially reduce profit. The introduction of nitrogen (N)-fixing crops in ecological focus areas was the easiest greening strategy to adopt, and led to an increase in the protein feed self-sufficiency. In conclusion, it is important to note that the greening policy in its current form does not lead to reduced environmental impacts. This implies that in order to improve environmental performance, regulations are needed rather than voluntary economic incentives. [ABSTRACT FROM AUTHOR]

Accurate estimation of yield gaps is only possible for locations where high quality local data are available, which are, however, lacking in many regions of the world. The challenge is how yield gap estimates based on location-specific input data can be used to obtain yield gap estimates for larger spatial areas. Hence, insight about the minimum number of locations required to achieve robust estimates of yield gaps at larger spatial scales is essential because data collection at a large number of locations is expensive and time consuming. In this paper we describe an approach that consists of a climate zonation scheme supplemented by agronomical and locally relevant weather, soil and cropping system data. Two elements of this methodology are evaluated here: the effects on simulated national crop yield potentials attributable to missing and/or poor quality data and the error that might be introduced in scaled up yield gap estimates due to the selected climate zonation scheme. Variation in simulated yield potentials among weather stations located within the same climate zone, represented by the coefficient of variation, served as a measure of the performance of the climate zonation scheme for upscaling of yield potentials. We found that our approach was most appropriate for countries with homogeneous topography and large climate zones, and that local up-to-date knowledge of crop area distribution is required for selecting relevant locations for data collection. Estimated national water-limited yield potentials were found to be robust if data could be collected that are representative for approximately 50% of the national harvested area of a crop. In a sensitivity analysis for rainfed maize in four countries, assuming only 25% coverage of the national harvested crop area (to represent countries with poor data availability), national water-limited yield potentials were found to be over- or underestimated by 3 to 27% compared to estimates with the recommended crop area coverage of ≥50%. It was shown that the variation of simulated yield potentials within the same climate zone is small. Water-limited potentials in semi-arid areas are an exception, because the climate zones in these semi-arid areas represent aridity limits of crop production for the studied crops. We conclude that the developed approach is robust for scaling up yield gap estimates from field, i.e. weather station data supplemented by local soil and cropping system data, to regional and national levels. Possible errors occur in semi-arid areas with large variability in rainfall and in countries with more heterogeneous topography and climatic conditions in which data availability hindered full application of the approach. [ABSTRACT FROM AUTHOR]

Numerous studies have been published during the past two decades that use simulation models to assess crop yield gaps (quantified as the difference between potential and actual farm yields), impact of climate change on future crop yields, and land-use change. However, there is a wide range in quality and spatial and temporal scale and resolution of climate and soil data underpinning these studies, as well as widely differing assumptions about cropping-system context and crop model calibration. Here we present an explicit rationale and methodology for selecting data sources for simulating crop yields and estimating yield gaps at specific locations that can be applied across widely different levels of data availability and quality. The method consists of a tiered approach that identifies the most scientifically robust requirements for data availability and quality, as well as other, less rigorous options when data are not available or are of poor quality. Examples are given using this approach to estimate maize yield gaps in the state of Nebraska (USA), and at a national scale for Argentina and Kenya. These examples were selected to represent contrasting scenarios of data availability and quality for the variables used to estimate yield gaps. The goal of the proposed methods is to provide transparent, reproducible, and scientifically robust guidelines for estimating yield gaps; guidelines which are also relevant for simulating the impact of climate change and land-use change at local to global spatial scales. Likewise, the improved understanding of data requirements and alternatives for simulating crop yields and estimating yield gaps as described here can help identify the most critical “data gaps” and focus global efforts to fill them. A related paper ( Van Bussel et al., 2015 ) examines issues of site selection to minimize data requirements and up-scaling from location-specific estimates to regional and national spatial scales. [ABSTRACT FROM AUTHOR]

Abstract: Cropping system models are powerful tools for regional impact assessment, but their input data requirements for large heterogeneous areas are difficult to fulfil. Hence, the objectives of this paper are to present low-data approaches for specifying detailed management data required by cropping system models, and for calibrating default crop parameters applied to 12 regions in the European Union (EU). Various downscaling and upscaling procedures for different data types are applied to address both objectives. The Agricultural Production and Externalities Simulator (APES) model is used for illustrative purposes. Combining easy-to-collect regional crop management information and expert knowledge enables to develop generic, expert-based rules for specifying crop management. Effects of these expert-based management rules on simulated yields and nitrogen leaching are illustrated using APES. Simulated yields of grain maize, soft wheat and durum wheat using default crop parameters for phenology are compared with crop yields observed in 12 EU regions. The accuracy of the simulated yields was variable, but generally poor. A regional calibration factor Kpheno is developed based on the temperature sum of the average sowing and harvest dates of the three crops in each region. Applying this calibration factor improved the simulated yields in all cases. Results suggest that it is possible to develop expert-based management rules and to capture yield variation across the EU by using the presented low-data approaches. [Copyright &y& Elsevier]

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. 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. 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. 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 impractical to address. The decision making on options to improve food production is better informed when uncertainties are accounted for. The proposed uncertainty protocol allows users to distinguish between different sources of uncertainty as well as their level and relative effect on the end result. The ranking of uncertainty sources suggests a prioritization of future effort to reduce the uncertainty around yield gaps. The justifications given by the experts can provide suggestions for options to reduce uncertainty. [Display omitted] • The Global Yield Gap Atlas maps yield gaps of crops from point to regional scale across the globe. • Many different uncertainty sources affect the calculation of yield gaps. • We present a protocol for experts to score uncertainties with their justifications made available to users • The expert scores of the uncertainty sources provide a ranking for users to consider per crop-country combination. • The expert justifications reveal which uncertainties can be reduced and which are not reducible [ABSTRACT FROM AUTHOR]

Abstract: Agricultural research in East and Southeast Asia is increasingly challenged by the search for land-use options that best match multiple development objectives of rural societies (e.g., increased income, food security, and reduced environmental pollution). In order to support the identification of sustainable land-use options and to support decision making with respect to land use, a tool was developed for quantifying inputs and outputs of cropping systems at the field level. TechnoGIN, the tool described in this paper, integrates systems analytical and expert knowledge and different types of agronomic data enabling the assessment of inputs and outputs of a broad range of cropping systems and the evaluation of their resource use efficiencies. By using methods of spatial aggregation in combination with linear programming, results can also be used to explore trade-offs in resource-use efficiencies at higher levels such as the farm household, municipality and province. New features in TechnoGIN compared with similar tools include the annual rotation of up to three crops, the distinction between aerobic and anaerobic growing conditions of crops, and the procedure for estimating crop nutrient uptake. TechnoGIN is illustrated with results from the Tam Duong district in North Vietnam. The design of TechnoGIN enables easy access to its data, parameters and assumptions, and rapid generation and evaluation of input–output relationships of cropping systems in order to add new information and to improve data. TechnoGIN raises awareness about the assumptions incorporated and thus supports data collection and setting of the research agenda with respect to agro-ecological processes for which knowledge is incomplete, and is relevant for showing trade-offs between production, economic and environmental impacts of different land-use systems. [Copyright &y& Elsevier]

• A large database of farmer's field data was combined with crop growth modelling. • Yield gaps for the main arable crops in the Netherlands were ca. 30% of Yp. • Variation in Ya was mostly explained by sowing and harvest dates. • Actual water productivity was about half of its potential for most arable crops. • Big data are useful to describe current systems but not to predict their performance. Yield gaps and water productivity are key indicators to monitor the progress towards more sustainable and productive cropping systems. Individual farmers are collecting increasing amounts of data ('big data'), which can help monitor the process of sustainable intensification at local level. In this study, we build upon such data to quantify the magnitude and identify the biophysical and management determinants of on-farm yield gaps and water productivity for the main arable crops cultivated in the Netherlands. The analysis focused on ware, seed and starch potatoes, sugar beet, spring onion, winter wheat and spring barley and covered the period 2015–2017. A crop modelling approach based on crop coefficients (kc) and daily weather data was used to estimate the potential yield (Yp), radiation intercepted and potential evapotranspiration (ETP) for each crop. Yield gaps were estimated to be ca. 10% of Yp for sugar beet, 25–30% of Yp for ware, seed and starch potato and spring barley, and 35–40% of Yp for spring onion and winter wheat. Variation in actual yields was associated with water availability in key periods of the growing season as well as with sowing and harvest dates. However, the R 2 of the fitted regressions was rather low (20–49%). Current levels of crop water productivity ranged between 13 kg DM ha−1 mm−1 for spring barley, ca. 15 kg DM ha−1 mm−1 for seed potato, spring onion and winter wheat, 23 kg DM ha−1 mm−1 for ware potato and ca. 25 kg DM ha−1 mm−1 for starch potato and sugar beet. These values are about half of their potential, but increasing actual water productivity further is restricted by rainfall amount and distribution. However, doing so should not be prioritized over reducing environmental impacts of these intensive cropping systems in the short-term and may require large investments from farm to regional levels in the long-term. Although these findings are most relevant to similar cropping systems in NW Europe, the underlying methods are generic and can be used to benchmark crop performance in other cropping systems. Based on this work, we argue that 'big data' are currently most useful to describe cropping systems at regional scale and derive benchmarks of farm performance but not as much to predict and explain crop yield variability in time and space. [ABSTRACT FROM AUTHOR]