Seasonal crop yield forecast: Methods, applications, and accuracies

Basso, Bruno; Liu, Lin

doi:10.1016/bs.agron.2018.11.002

Cited by 153 publications

(90 citation statements)

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“…Traditionally, farmers rely on their experiences and past historical data such as the crop yields and weather to make important decisions to increase short-term profitability and long-term sustainability of their operation (Arbuckle and Rosman 2014). New promising technologies such as machine learning (ML) have emerged over the last years that can potentially aid farmers' decision making (Hoogenboom et al 2004, González Sánchez et al 2014, Togliatti et al 2017, Basso and Liu 2018, Ansarifar and Wang 2018, Moeinizade et al 2019. However, the lack of spatial and temporal data that cover a range of production (e.g.…”

Section: Introductionmentioning

confidence: 99%

Maize yield and nitrate loss prediction with machine learning algorithms

Shahhosseini

Martinez-Feria

et al. 2019

Environ. Res. Lett.

124

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Pre-growing season prediction of crop production outcomes such as grain yields and nitrogen (N) losses can provide insights to farmers and agronomists to make decisions. Simulation crop models can assist in scenario planning, but their use is limited because of data requirements and long runtimes. Thus, there is a need for more computationally expedient approaches to scale up predictions. We evaluated the potential of four machine learning (ML) algorithms (LASSO Regression, Ridge Regression, random forests, Extreme Gradient Boosting, and their ensembles) as meta-models for a cropping systems simulator (APSIM) to inform future decision support tool development. We asked:(1) How well do ML meta-models predict maize yield and N losses using pre-season information? (2) How many data are needed to train ML algorithms to achieve acceptable predictions? (3) Which input data variables are most important for accurate prediction? And (4) do ensembles of ML meta-models improve prediction? The simulated dataset included more than three million data including genotype, environment and management scenarios. XGBoost was the most accurate ML model in predicting yields with a relative mean square error (RRMSE) of 13.5%, and Random forests most accurately predicted N loss at planting time, with a RRMSE of 54%. ML meta-models reasonably reproduced simulated maize yields using the information available at planting, but not N loss. They also differed in their sensitivities to the size of the training dataset. Across all ML models, yield prediction error decreased by 10%-40% as the training dataset increased from 0.5 to 1.8 million data points, whereas N loss prediction error showed no consistent pattern. ML models also differed in their sensitivities to input variables (weather, soil properties, management, initial conditions), thus depending on the data availability researchers may use a different ML model. Modest prediction improvements resulted from ML ensembles. These results can help accelerate progress in coupling simulation models and ML toward developing dynamic decision support tools for pre-season management.

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Section: Introductionmentioning

confidence: 99%

Maize yield and nitrate loss prediction with machine learning algorithms

Shahhosseini

Martinez-Feria

et al. 2019

Environ. Res. Lett.

124

View full text Add to dashboard Cite

show abstract

“…Crop yield forecasts provide a distribution of expected yield prior to crop harvests (Basso and Liu 2019). Knowing the expected yield of major food commodities in advance of harvesting is critical for national food security (Jayne and Rashid 2010;Stone and Meinke 2005).…”

Section: Introductionmentioning

confidence: 99%

“…Government agencies are involved in providing information about field conditions, crop status and weather conditions to release multiple stage yield forecasts before planting, during the growing season, and prior to harvest (Gennari and Fonteneau 2016). The assessment of the current early warning and crop forecasting system which was designed in mid 80's identified a number of deficiencies that have serious impact on the timeliness and reliability of the data (Basso and Liu 2019;Gennari and Fonteneau 2016;Luo et al 2011).…”

Section: Introductionmentioning

confidence: 99%

“…There are three major approaches to forecasting crop yield: expert-based assessments (e.g. interviews and field surveys), statistical models, and process-based models (Basso and Liu 2019). Interviews with farmers can provide subjective expectations on end-of-season yield (Nandram et al 2014;Pease et al 1993).…”

Section: Introductionmentioning

confidence: 99%

“…Despite the extensive studies on yield forecasting methodology, most of the work has been done for developed nations where fields are likely to be large with one single crop per growing season, while only a small fraction of the literature has focused on yield forecasting methods for smallholder farming systems, either pure stands or intercropping (Basso and Liu 2019). Much work done so far in yield prediction has explored the use of statistical agrometeorological models, where yield is forecasted based on in-season agronomic (e.g.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Linking field survey with crop modeling to forecast maize yield in smallholder farmers’ fields in Tanzania

Liu

Basso

2020

Food Sec.

Self Cite

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Short term food security issues require reliable crop forecasting data to identify the population at risk of food insecurity and quantify the anticipated food deficit. The assessment of the current early warning and crop forecasting system which was designed in mid 80's identified a number of deficiencies that have serious impact on the timeliness and reliability of the data. We developed a new method to forecast maize yield across smallholder farmers' fields in Tanzania (Morogoro, Kagera and Tanga districts) by integrating field-based survey with a process-based mechanistic crop simulation model. The method has shown to provide acceptable forecasts (r 2 values of 0.94, 0.88 and 0.5 in Tanga, Morogoro and Kagera districts, respectively) 14-77 days prior to crop harvest across the three districts, in spite of wide range of maize growing conditions (final yields ranged from 0.2-5.9 t/ha). This study highlights the possibility of achieving accurate yield forecasts, and scaling up to regional levels for smallholder farming systems, where uncertainties in management conditions and field size are large.

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Predicting crop yields and soil‐plant nitrogen dynamics in the US Corn Belt

et al. 2020

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We used the Agricultural Production Systems sIMulator (APSIM) to predict and explain maize and soybean yields, phenology, and soil water and nitrogen (N) dynamics during the growing season in Iowa, USA. Historical, current and forecasted weather data were used to drive simulations, which were released in public four weeks after planting. In this paper, we (1) describe the methodology used to perform forecasts;(2) evaluate model prediction accuracy against data collected from 10 locations over four years; and (3) identify inputs that are key in forecasting yields and soil N dynamics. We found that the predicted median yield at planting was a very good indicator of end-of-season yields (relative root mean square error [RRMSE] of ∼20%). For reference, the prediction at maturity, when all the weather was known, had a RRMSE of 14%. The good prediction at planting time was explained by the existence of shallow water tables, which decreased model sensitivity to unknown summer precipitation by 50-64%. Model initial conditions and management information accounted for Abbreviations: APSIM, Agricultural Production Systems sIMulator; RRMSE, relative root mean square error.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. one-fourth of the variation in maize yield. End of season model evaluations indicated that the model simulated well crop phenology (R 2 = 0.88), root depth (R 2 = 0.83), biomass production (R 2 = 0.93), grain yield (R 2 = 0.90), plant N uptake (R 2 = 0.87), soil moisture (R 2 = 0.42), soil temperature (R 2 = 0.93), soil nitrate (R 2 = 0.77), and water table depth (R 2 = 0.41). We concluded that model set-up by the user (e.g. inclusion of water table), initial conditions, and early season measurements are very important for accurate predictions of soil water, N and crop yields in this environment. Neil Huth from CSIRO for their support with the APSIM model, Iowa State University students () for assistance with data collection and managing the field experiments. We also thank the APSIM Initiative for making the software publicly available and for ensuring software quality. ORCIDSotirios V. Archontoulis https://orcid.org/0000-0001-7595-8107 Mark A. Licht https://orcid.org/0000-0001-6640-7856 Kendall R. Lamkey

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Seasonal crop yield forecast: Methods, applications, and accuracies

Cited by 153 publications

References 250 publications

Maize yield and nitrate loss prediction with machine learning algorithms

Maize yield and nitrate loss prediction with machine learning algorithms

Linking field survey with crop modeling to forecast maize yield in smallholder farmers’ fields in Tanzania

Predicting crop yields and soil‐plant nitrogen dynamics in the US Corn Belt

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