Can big data explain yield variability and water productivity in intensive cropping systems?

Silva, João Vasco; Tenreiro, Tomás R.; Spätjens, L.; Anten, Niels P. R.; Ittersum, M.K. van; Reidsma, P.

doi:10.1016/j.fcr.2020.107828

Cited by 34 publications

(46 citation statements)

References 42 publications

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“…where n is the number of crops in farm i, MY i is the measured yield for crop i, PY i is the countrywide potential yield for that crop (Silva et al, 2020) and PA i is the proportional area that that crop occupies in the farm. This approach had three limitations: (i) potential yield was only known for the most common crops grown in the Netherlands, which means fields with uncommon crops were not included in the calculation; (ii) the grass yield was not measured, but rather calculated from information on the farm's overall performance using the amount of external feed bought, the number of cows in each dairy farm, the energetic requirements of cows, and the total milk production of the farm (RIVM 2019-0026, 2019; Appendix 2); and (iii) there was no potential yield estimate for grass, therefore, we used the distribution of grass yield to establish the low, medium and high categories, using the first and third quartiles as the threshold values.…”

Section: From Field To Farm Levelmentioning

confidence: 99%

Assessing multifunctionality of agricultural soils: Reducing the biodiversity trade‐off

Vázquez

Goede

Rutgers

et al. 2020

European J Soil Science

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Soils are indispensable for the provision of several functions. Agricultural intensification and its focus on increasing primary productivity (PP) poses a threat to soil quality, due to increases in nutrient loads, greenhouse gas emissions and declining biodiversity. The EU Horizon 2020 Landmark project has developed multi-criteria decision models to assess five soil functions: PP, nutrient cycling (NC), soil biodiversity and habitat provision (B-HP), climate mitigation and water regulation, simultaneously in agricultural fields. Using these algorithms, we evaluated the supply of PP, NC and B-HP of 31 grasslands and 21 croplands as low, medium or high. The multi-criteria decision models showed that 38% of the farms had a medium to high supply of all three soil functions, whereas only one cropland had a high supply for all three. Forty-eight per cent of the farms were characterized by a high supply of PP and NC. We observed a clear trade-off between these two functions and B-HP. Multivariate statistical analyses indicated that higher organic inputs combined with a lower mineral fertilization concur with higher biodiversity scores while maintaining a medium delivery of PP and NC. Additionally, we compared the outputs of the model predictions to independent variables that served as proxies for the soil functions and found: (a) croplands (but not grasslands) with high PP had a higher standardized yield than those with medium PP; (b) grasslands (but not croplands) with high NC had a significantly lower fungal to bacterial biomass ratio, suggesting faster decomposition channels; and (c) a positive though non-significant trend between B-HP score and rank according to soil invertebrate biodiversity. These comparisons suggest a successful upscaling of the models from field to farm level. Our study highlights the need for systematic collection of management-related data for the assessment of soil functions. Multifunctionality can be achieved in agricultural soils; however, without specifically managing for it, biodiversity might come at a loss.

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Section: From Field To Farm Levelmentioning

confidence: 99%

Assessing multifunctionality of agricultural soils: Reducing the biodiversity trade‐off

Vázquez

Goede

Rutgers

et al. 2020

European J Soil Science

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“…Understanding the drivers behind yield gaps and the opportunities to increase crop yield, or reduce inputs without compromising crop yield under on-farm rather than experimental settings, requires a wealth of individual farmer field data with detailed biophysical and crop management information (Beza et al, 2017). Such data are becoming increasingly available across farming systems around the world (Silva et al, 2020;Rattalino-Edreira et al, 2018). When combined with secondary biophysical data, such detailed information can be used to infer the performance of multiple genotypes and their interactions with environmental and management factors.…”

Section: Introductionmentioning

confidence: 99%

Rice yield gaps and nitrogen-use efficiency in the Northwestern Indo-Gangetic Plains of India: Evidence based insights from heterogeneous farmers’ practices

Nayak

Silva

Parihar

et al. 2022

Field Crops Research

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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 3 rd 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.

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“…The Journal of Agricultural Science 3 region to introduce new crops (Nendel et al, 2020) or resource-use efficiency assessments at regional scale (Silva et al, 2020). We also note that most crop model applications currently target cropping systems (n = 276) at the field scale (n = 405; Table 1), with findings being often directly extrapolated to the regional level.…”

Section: Climate Change Adaptation and Mitigationmentioning

confidence: 90%

Grand challenges for the 21st century: what crop models can and can't (yet) do

Silva¹

2020

J. Agric. Sci.

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Crop production is at the core of a ‘perfect storm’ encompassing the grand challenges of achieving food and nutrition security for all, in the face of climate change, while avoiding further conversion of natural habitats for agriculture and loss of biodiversity. Here, we explore current trends in crop modelling related to these grand challenges by reflecting on research presented at the Second International Crop Modelling Symposium (iCropM2020). A keyword search in the book of abstracts of the symposium revealed a strong focus on ‘climate change’, ‘adaptation’ and ‘impact assessment’ and much less on ‘food security’ or ‘policy’. Most research focused on field-level investigations and far fewer on farm(ing) systems levels – the levels at which management decisions are made by farmers. Experimentation is key to development and testing of crop models, yet the term ‘simulation’ outweighed by far the terms ‘experiments’ and ‘trials’, and few contributions dealt with model improvement. Cereals are intensively researched, whereas roots, tubers and tropical perennials are under-researched. Little attention is paid to nutrient limitations apart from nitrogen or to pests and diseases. The aforementioned aspects represent opportunities for future research where crop models can help in devising hypotheses and driving new experimentation. We must also ensure that crop models are fit for their intended purposes, especially if they are to provide advice to policymakers. The latter, together with cross-scale and interdisciplinary efforts with direct engagement of stakeholders are needed to address the grand challenges faced by food and agricultural systems in the next century.

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Can big data explain yield variability and water productivity in intensive cropping systems?

Cited by 34 publications

References 42 publications

Assessing multifunctionality of agricultural soils: Reducing the biodiversity trade‐off

Assessing multifunctionality of agricultural soils: Reducing the biodiversity trade‐off

Rice yield gaps and nitrogen-use efficiency in the Northwestern Indo-Gangetic Plains of India: Evidence based insights from heterogeneous farmers’ practices

Grand challenges for the 21st century: what crop models can and can't (yet) do

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