Spatial Panel Models of Crop Yield Response to Weather: Econometric Specification Strategies and Prediction Performance

Yun, Seong Do; Gramig, Benjamin M.

doi:10.1017/aae.2021.29

Cited by 4 publications

(3 citation statements)

References 39 publications

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“…(1 - {\mathrm{exp}}\left( { - \frac{s}{{{a}_{Bph}}}} \right) \right]\end{eqnarray}$$where

nu{g}_{Bph}

is the nugget effect,

psil{l}_{Bph}

is the partial sill, and

{a}_{Bph}

is the range. We use the exponential because it is commonly used (Niyibizi et al., 2022; Park et al., 2019; Sharma et al., 2022) but numerous other functional forms have been suggested (Yun & Gramig, 2022).…”

Section: Theorymentioning

confidence: 99%

Optimal grid size for site‐specific nutrient application

Mills,

Brorsen,

Poursina

et al. 2023

Agricultural Economics

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This article develops a theoretical framework to determine the economically optimal grid size when sampling to guide precision application of inputs. Our theoretical model shows that a finer grid is optimal with increases in output price. A coarser grid is optimal with increased sampling costs and increased spatial correlation. With an exponential variogram, for example, the optimal grid size increases as the range increases. An applied example of lime application for winter wheat is used to demonstrate the framework. Spatial variograms for pH and Buffer pH were estimated using data from nine farmer fields. At the average spatial variability from these fields and using typical Oklahoma wheat yields of 40 bu/ac, grid sampling was economical when prices were above $5.43/bu. The optimal grid sizes were slightly larger than the 2.5‐acre sizes that are typically used by producers. The empirical example verified the theory in that it found that optimal grid size decreased with higher output prices and crop yields and increased with greater spatial correlation. For example, optimal grid size approximately doubled as the range of the exponential correlation function doubled.

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“…(1 - {\mathrm{exp}}\left( { - \frac{s}{{{a}_{Bph}}}} \right) \right]\end{eqnarray}$$where

nu{g}_{Bph}

is the nugget effect,

psil{l}_{Bph}

is the partial sill, and

{a}_{Bph}

Section: Theorymentioning

confidence: 99%

Optimal grid size for site‐specific nutrient application

Mills,

Brorsen,

Poursina

et al. 2023

Agricultural Economics

View full text Add to dashboard Cite

show abstract

“…Yu Arkhipova & Smirnov (2020) predicted crop yields in Russia by using econometric models such as the traditional least squares regression model and the truncated sample regression model. Yun & Gramig (2022) used regression modelling (nonspatial panel regression) of corn yield in US counties using weather and site characteristics as independent variables. Okorie et al, (2023) conducted yield forecasting of major crops (banana, plantain, beans, cassava, coffee, sorghum, potato, sweet potato, maize, rice, sugarcane, wheat, millet and cotton seed) in East African countries (Burundi, Kenya, Somalia, Tanzania, Uganda and Rwanda) using an ARIMA model.…”

Section: Introductionmentioning

confidence: 99%

Implications of Neural Network as a Decision-Making Tool in Managing Kazakhstan’s Agricultural Economy

KULISZ,

DUISENBEKOVA,

KUJAWSKA

et al. 2024

Appl. Comput. Sci.

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This study investigates the application of Artificial Neural Networks (ANN) in forecasting agricultural yields in Kazakhstan, highlighting its implications for economic management and policy-making. Utilizing data from the Bureau of National Statistics of the Republic of Kazakhstan (2000-2023), the research develops two ANN models using the Neural Net Fitting library in MATLAB. The first model predicts the total gross yield of main agricultural crops, while the second forecasts the share of individual crops, including cereals, oilseeds, potatoes, vegetables, melons, and sugar beets. The models demonstrate high accuracy, with the total gross yield model achieving an R-squared value of 0.98 and the individual crop model showing an R value of 0.99375. These results indicate a strong predictive capability, essential for practical agricultural and economic planning. The study extends previous research by incorporating a comprehensive range of climatic and agrochemical data, enhancing the precision of yield predictions. The findings have significant implications for Kazakhstan's economy. Accurate yield predictions can optimize agricultural planning, contribute to food security, and inform policy decisions. The successful application of ANN models showcases the potential of AI and machine learning in agriculture, suggesting a pathway towards more efficient, sustainable farming practices and improved quality management systems.

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“…However, there are some limitations and potential sources of error that are worth considering. Here are some of them [48][49][50][51][52] It is important to understand these limitations and potential sources of error in crop yield prediction. Models can be useful tools, but they should be used carefully, take into account a variety of factors, and evolve with advances in knowledge and data availability.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Potato (Solanum tuberosum L.) Yield Based on Machine Learning Methods

Kurek,

Niedbała,

Wojciechowski

et al. 2023

Agriculture

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This research delves into the application of machine learning methods for predicting the yield of potato varieties used for French fries in Poland. By integrating a comprehensive dataset comprising agronomical, climatic, soil, and satellite-based vegetation data from 36 commercial potato fields over five growing seasons (2018–2022), we developed three distinct models: non-satellite, satellite, and hybrid. The non-satellite model, relying on 85 features, excludes vegetation indices, whereas the satellite model includes these indices within its 128 features. The hybrid model, combining all available features, encompasses a total of 165 features, presenting the most-comprehensive approach. Our findings revealed that the hybrid model, particularly when enhanced with SVM outlier detection, exhibited superior performance with the lowest Mean Absolute Percentage Error (MAPE) of 5.85%, underscoring the effectiveness of integrating diverse data sources into agricultural yield prediction. In contrast, the non-satellite and satellite models displayed higher MAPE values, indicating less accuracy compared to the hybrid model. Advanced data-processing techniques such as PCA and outlier detection methods (LOF and One-Class SVM) played a pivotal role in model performance, optimising feature selection and dataset refinement. The study concluded that machine learning methods, particularly when leveraging a multifaceted approach involving a wide array of data sources and advanced processing techniques, can significantly enhance the accuracy of agricultural yield predictions. These insights pave the way for more-efficient and -informed agricultural practices, emphasising the potential of machine learning in revolutionising yield prediction and crop management.

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Spatial Panel Models of Crop Yield Response to Weather: Econometric Specification Strategies and Prediction Performance

Cited by 4 publications

References 39 publications

Optimal grid size for site‐specific nutrient application

Optimal grid size for site‐specific nutrient application

Implications of Neural Network as a Decision-Making Tool in Managing Kazakhstan’s Agricultural Economy

Prediction of Potato (Solanum tuberosum L.) Yield Based on Machine Learning Methods

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