Crop yield estimation model for Iowa using remote sensing and surface parameters

Prasad, Anup K.; Chai, Lim Young; Singh, Ramesh P.; Kafatos, Menas

doi:10.1016/j.jag.2005.06.002

Cited by 348 publications

(215 citation statements)

References 17 publications

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“…Table 1 shows the summary of dataset used. Previous studies (Prasad et al, 2006;Na et al, 2014;Kim et al, 2014) presented these variables were associated with the corn yield.…”

Section: Study Areamentioning

confidence: 97%

“…Prasad et al (2006) conducted multivariate regression analyses to estimate corn and soybean yields in Iowa using MODIS (Moderate Resolution Imaging Spectroradiometer) NDVI (Normalized Difference Vegetation Index), climate factors and soil moisture. Ren et al (2008) presented regression models for the estimation of winter wheat yields using MODIS NDVI and weather data in Shandong, China.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Approaches to Corn Yield Estimation Using Satellite Images and Climate Data: A Case of Iowa State

Kim¹,

Lee²

2016

Journal of the Korean Society of Surveying, Geodesy, Photogramm

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Remote sensing data has been widely used in the estimation of crop yields by employing statistical methods such as regression model. Machine learning, which is an efficient empirical method for classification and prediction, is another approach to crop yield estimation. This paper described the corn yield estimation in Iowa State using four machine learning approaches such as SVM (Support Vector Machine), RF (Random Forest), ERT (Extremely Randomized Trees) and DL (Deep Learning). Also, comparisons of the validation statistics among them were presented. To examine the seasonal sensitivities of the corn yields, three period groups were set up: (1) MJJAS (May to September), (2) JA (July and August) and (3) OC (optimal combination of month). In overall, the DL method showed the highest accuracies in terms of the correlation coefficient for the three period groups. The accuracies were relatively favorable in the OC group, which indicates the optimal combination of month can be significant in statistical modeling of crop yields. The differences between our predictions and USDA (United States Department of Agriculture) statistics were about 6-8 %, which shows the machine learning approaches can be a viable option for crop yield modeling. In particular, the DL showed more stable results by overcoming the overfitting problem of generic machine learning methods. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http:// creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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“…Table 1 shows the summary of dataset used. Previous studies (Prasad et al, 2006;Na et al, 2014;Kim et al, 2014) presented these variables were associated with the corn yield.…”

Section: Study Areamentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Machine Learning Approaches to Corn Yield Estimation Using Satellite Images and Climate Data: A Case of Iowa State

Kim¹,

Lee²

2016

Journal of the Korean Society of Surveying, Geodesy, Photogramm

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“…According to Bergamaschi et al (2013), a wide range of sowing dates adopted by farmers is problematic for large-area crop modeling. The authors evaluated the GLAM-Maize model in northwestern Rio Grande do (Prasad et al, 2006). Comparing potential and actual yields also allowed detecting when the effect of other production factors may have higher or lower impact on crop yield, depending on the crop season.…”

Section: Regionmentioning

confidence: 99%

Modeling of corn yield in Brazil as a function of meteorological conditions and technological level

Monteiro

Assad

Sentelhas

et al. 2017

Pesq. agropec. bras.

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-The objective of this work was to develop and evaluate a method for estimating corn yield using a minimum number of parameters and limited information about crop management. The proposed method estimates potential and attainable yields based on the technological level of the production systems and on relatively simple agrometeorological models. Corn yield was estimated for the crop seasons from 2000/2001 to 2007/2008, considering several locations and regions in Brazil, and was compared with the actual yield data from official surveys. There was a high correlation between the estimated and observed yield (0.76≤R 2 <0.92; p<0.01), with model efficiency (E1') ranging from 0.45 to 0.73; mean relative error (MRE) between -0.9 and 2.4%; and mean absolute error (MAE) of less than 70 kg ha -1 , depending on the technological level adopted. Based on these results, the proposed yield model can be recommended to forecast yields all over the country, contributing to make this process more precise and accurate.Index terms: Zea mays, large-area crop modeling, paremetrization, risk analysis, technological potential yield, yield forecast. Modelagem da produtividade de milho no Brasil em função das condições meteorológicas e do nível tecnológicoResumo -O objetivo deste trabalho foi desenvolver e avaliar um método para estimar a produtividade de milho com uso de um número mínimo de parâmetros e de informações limitadas sobre o manejo da cultura. O método proposto estima rendimentos potenciais e atingíveis com base no nível tecnológico dos sistemas de produção e em modelos agrometeorológicos relativamente simples. A produtividade de milho foi estimada para as safras de 2000/2001 a 2007/2008, tendo-se considerado vários locais e regiões do Brasil, e comparada aos dados de produção reais de levantamentos oficiais. A produtividade estimada apresentou alta correlação com a observada (0,76≤R 2 <0,92; p<0,01), com eficiência do modelo (E1') entre 0,45 e 0,73; erro médio relativo (MRE) entre -0,9 e 2,4%; e erro médio absoluto (MAE) inferior a 70 kg ha -1 , de acordo com o nível tecnológico considerado. Com base nestes resultados, este modelo pode ser recomendado para estimativas de produtividade em todo o País, e contribuir para tornar este processo mais preciso e exato.Termos para indexação: Zea mays, modelagem de cultura em grandes áreas, parametrização, análise de risco, produtividade potencial tecnológica, previsão de safra.

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“…Regression-based studies that look at the effects of weather on soybean crops in particular include Chen et al (2004), Prasad et al (2006), McCarl et al (2008), and Yu and Babcock (2010. Of these, Yu and Babcock (2010) is one of the few studies that 2 The Roundup Ready Soybean is a herbicide immune crop introduced in the U.S. market in 1996.…”

Section: Introductionmentioning

confidence: 99%

Use of DArT molecular markers for QTL analysis of drought-stress responses in soybean. I. Phenotypic evaluation of traits

James

Lawn

et al. 2015

Crop Pasture Sci.

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Constant research efforts have been undertaken to create and adopt soybean varieties and farming practices that would lead to more drought-tolerant crops. Given that drought-tolerant crops are more stable in terms of price and supply, private genetic companies invest in those genetic materials with the biggest market opportunities. This begs the question: has there been any indication of improvements in drought-tolerance of crops? In this study, we focus on analyzing three soybean distinct relative maturity zones in the state of Missouri and determine if and in what direction is the drought tolerance of these crops changing over time.

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Crop yield estimation model for Iowa using remote sensing and surface parameters

Cited by 348 publications

References 17 publications

Machine Learning Approaches to Corn Yield Estimation Using Satellite Images and Climate Data: A Case of Iowa State

Machine Learning Approaches to Corn Yield Estimation Using Satellite Images and Climate Data: A Case of Iowa State

Modeling of corn yield in Brazil as a function of meteorological conditions and technological level

Use of DArT molecular markers for QTL analysis of drought-stress responses in soybean. I. Phenotypic evaluation of traits

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