Crop–weather model for turmeric yield forecasting for Coimbatore district, Tamil Nadu, India

Kandiannan, K.; Chandaragiri, K. K.; Sankaran, N.; Balasubramanian, T. N.; Kailasam, C.

doi:10.1016/s0168-1923(02)00125-9

Cited by 24 publications

(7 citation statements)

References 12 publications

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“…The common approach to yield prediction using weather data is linear regression (Sheehy, Mitchell, and Ferrer 2006;Kandiannan et al 2002). Because of the large number of temperature and rainfall predictors, we investigate the reduced model in (3.3) assuming constant regression coefficients (α j (t) = a j for j = 1, .…”

Section: Yield Forecasting: Results and Discussionmentioning

confidence: 99%

“…However, there are significant variations in crop yield by location due to varying environmental conditions. In many previous studies, yield forecasting models incorporate a series of weather predictors (Hoogenboom 2000; Kandiannan et al 2002), more specifically, temperature (Peng et al 2004;Wheeler et al 2000;Batts et al 1997) and rainfall (Mkhabela, Mkhabela, and Mashinini 2005). In line with the existing work, we develop a crop-weather forecasting model using a regression approach where the weather factors are rainfall and temperature.…”

Section: Introductionmentioning

confidence: 94%

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Yield and Price Forecasting for Stochastic Crop Decision Planning

Kantanantha

Serban

Griffin

2010

JABES

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The primary objective of this paper is to develop yield and price forecasting models employed in informed crop decision planning-a key aspect of effective farm management. For yearly yield prediction, we introduce a weather-based regression model with time-dependent varying coefficients. In order to allow for within-year climate variations, we predict yearly crop yield using weekly temperature and rainfall summaries resulting in a large number of correlated predictors. To overcome this difficulty, we reduce the space of predictors to a small number of uncorrelated predictors using Functional Principal Component Analysis (FPCA). For detailed price forecasting, we develop a futures-based model for long-range cash price prediction. In this model, the cash price is predicted as a sum of the nearby settlement futures price and the predicted commodity basis. We predict the one-year commodity basis as a mixture of historical basis data using a functional model-based approach. In both forecasting models, we estimate approximate prediction confidence intervals that are further integrated in a decision planning model. We applied our methods to corn yield and price forecasting for Hancock County in Illinois. Our forecasting results are more accurate in comparison to predictions based on existing methods. The methods introduced in this paper generally apply to other locations in the US and other crop types. The supplemental materials for this article are available online.

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Section: Yield Forecasting: Results and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 94%

Yield and Price Forecasting for Stochastic Crop Decision Planning

Kantanantha

Serban

Griffin

2010

JABES

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“…Paul and Sinha [13] forecasted wheat yield concerning weather variables in the Kanpur district of Uttar Pradesh using parametric and non-parametric tests. Turmeric yield was similarly forecasted on climate parameters using a data set for 20 years [14]. In another study performed by Kandiannan et al [14], turmeric yield was predicted using a multiple regression model where a significant correlation was found between yield and rainfall, evaporation wind speed, minimum temperature, and humidity.…”

Section: Overview Of the Studymentioning

confidence: 99%

Comparison of ARIMA, SutteARIMA, and Holt-Winters, and NNAR Models to Predict Food Grain in India

et al. 2023

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The agriculture sector plays an essential function within the Indian economic system. Foodgrains provide almost all the calories and proteins. This paper aims to compare ARIMA, SutteARIMA, Holt-Winters, and NNAR models to recommend an effective model to predict foodgrains production in India. The execution of the SutteARIMA predictive model used in this analysis was compared with the established ARIMA, Neural Network Auto-Regressive (NNAR), and Holt-Winters models, which have been widely applied for time series prediction. The findings of this study reveal that both the SutteARIMA model and the Holt-Winters model performed well with real-life problems and can effectively and profitably be engaged for food grain forecasting in India. The food grain forecasting approach with the SutteARIMA model indicated superior performance over the ARIMA, Holt-Winters, and NNAR models. Indeed, the actual and predicted values of the SutteARIMA and Holt-Winters forecasting models are quite close to predicting foodgrains production in India. This has been verified by MAPE and MSE values that are relatively low with the SutteARIMA model. Therefore, India’s SutteARIMA model was used to predict foodgrains production from 2021 to 2025. The forecasted amount of respective crops are as follows (in lakh tonnes) 1140.14 (wheat), 1232.27 (rice), 466.46 (coarse), 259.95 (pulses), and a total 3069.80 (foodgrains) by 2025.

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“…Compared with crop growth models, regression models have fewer data demands (Horie, Yajima, and Nakagawa, 1992;Kandiannan et al, 2002;Tannura, Irwin, and Good, 2008). Nonetheless, developing a multiple regression model requires determining the appropriate set of weather factors affecting crop yields.…”

Section: Literature Reviewmentioning

confidence: 99%

Using a Climate Index to Measure Crop Yield Response

Cai

Mullen

Bergstrom

et al. 2013

J. Agric. Appl. Econ.

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Using principal component analysis, a climate index is developed to estimate the linkage between climate and crop yields. The indices based on three climate projections are then applied to forecast future crop yield responses. We identify spatial heterogeneity of crop yield responses to future climate change across a number of U.S. northern and southern states. The results indicate that future hotter/drier weather conditions will likely have significant negative impacts on southern states, whereas only mild impacts are expected in most northern states.

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Crop–weather model for turmeric yield forecasting for Coimbatore district, Tamil Nadu, India

Cited by 24 publications

References 12 publications

Yield and Price Forecasting for Stochastic Crop Decision Planning

Yield and Price Forecasting for Stochastic Crop Decision Planning

Comparison of ARIMA, SutteARIMA, and Holt-Winters, and NNAR Models to Predict Food Grain in India

Using a Climate Index to Measure Crop Yield Response

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