Comparative evaluation of linear and nonlinear weather-based models for coconut yield prediction in the west coast of India

Das, Bappa; Nair, Bhakti; Arunachalam, V.; Reddy, K. V. Seshu; Paramesh, Venkatesh; Chakraborty, Debashis; Desai, Sujeet

doi:10.1007/s00484-020-01884-2

Cited by 24 publications

(9 citation statements)

References 26 publications

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“…The forecasts of rabi sorghum yield developed by ANN and PCA_ANN in the present study are in corroboration with the findings of Uno et al [46] reporting the potential of ANN for the development of in-season yield mapping and forecasting systems for corn in eastern Canada and they opined that greater prediction accuracy (about 20% validation RMSE) was obtained with an ANN model than with conventional empirical models based on normalized difference vegetation index, simple ratio or photochemical reflectance index. The present findings are also in confirmation with Das et al [47] while comparing ANN and PCA_ANN for rice and coconut yield forecasting for India's west coast. Arvind [48] carried out multistage wheat yield estimation using six different multivariate weather-based models viz., SMLR, PCA_SMLR, ANN, PCA_ANN, LASSO and ENET.…”

Section: Discussionsupporting

confidence: 92%

Weather-Based Neural Network, Stepwise Linear and Sparse Regression Approach for Rabi Sorghum Yield Forecasting of Karnataka, India

et al. 2020

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Sorghum is an important dual-purpose crop of India grown for food and fodder. Prevailing weather conditions during the crop growth period determine the yield of sorghum. Hence, the crop yield forecasting models based on weather parameters will be an appropriate option for policymakers and researchers to develop sustainable cropping strategies. In the present study, six multivariate weather-based models viz., least absolute shrinkage and selection operator (LASSO), elastic net (ENET), principal component analysis (PCA) in combination with stepwise multiple linear regression (SMLR), artificial neural network (ANN) alone and in combination with PCA and ridge regression model are examined by fixing 90% of the data for calibration and remaining dataset for validation to forecast rabi sorghum yield for different districts of Karnataka. The R2 and root mean square error (RMSE) during calibration ranged between 0.42 to 0.98 and 30.48 to 304.17 kg ha−1, respectively, without actual evapotranspiration (AET) whereas, these evaluation parameters varied from 0.38 to 0.99 and 19.84 to 308.79 kg ha−1, respectively with AET inclusion. During validation, the RMSE and nRMSE (normalized root mean square error) varied between 88.99 to 1265.03 kg ha−1 and 4.49 to 96.84%, respectively without AET and including AET as one of the weather variable RMSE and nRMSE were 63.48 to 1172.01 kg ha−1 and 4.16 to 92.56%, respectively. The performance of six multivariate models revealed that LASSO was the best model followed by ENET compared to PCA_SMLR, ANN, PCA_ANN and ridge regression models because of reduced overfitting through penalisation of regression coefficient. Thus, it can be concluded that LASSO and ENET weather-based models can be effectively utilized for the district level forecast of sorghum yield.

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Section: Discussionsupporting

confidence: 92%

Weather-Based Neural Network, Stepwise Linear and Sparse Regression Approach for Rabi Sorghum Yield Forecasting of Karnataka, India

et al. 2020

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“…As rice is a primary source of food for more than half the world's population, numerous research approaches were proposed for predicting the rice yield [4]. Similar research studies were conducted to model the relationship between the climatic factors and the yield of some other crops such as barley [5], corn [6], sugar cane [7], citrus [8], tea [9], coconut [10], sorghum [11], maize, and soybean [12]. A multiple number of climatic factors were considered in such research studies for the application of statistical methods and machine learning techniques.…”

Section: Introductionmentioning

confidence: 99%

Modeling the Relationship between Rice Yield and Climate Variables Using Statistical and Machine Learning Techniques

Wickramasinghe

Weliwatta

Ekanayake

et al. 2021

Journal of Mathematics

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This paper presents the application of a multiple number of statistical methods and machine learning techniques to model the relationship between rice yield and climate variables of a major region in Sri Lanka, which contributes significantly to the country’s paddy harvest. Rainfall, temperature (minimum and maximum), evaporation, average wind speed (morning and evening), and sunshine hours are the climatic factors considered for modeling. Rice harvest and yield data over the last three decades and monthly climatic data were used to develop the prediction model by applying artificial neural networks (ANNs), support vector machine regression (SVMR), multiple linear regression (MLR), Gaussian process regression (GPR), power regression (PR), and robust regression (RR). The performance of each model was assessed in terms of the mean squared error (MSE), correlation coefficient (R), mean absolute percentage error (MAPE), root mean squared error ratio (RSR), BIAS value, and the Nash number, and it was found that the GPR-based model is the most accurate among them. Climate data collected until early 2019 (Maha season of year 2018) were used to develop the model, and an independent validation was performed by applying data of the Yala season of year 2019. The developed model can be used to forecast the future rice yield with very high accuracy.

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“…Similarly, Rajegowda et al [6] used the model for pre-harvest forecasting of ragi and groundnut yield in Karnataka. In different linear and nonlinear models Das et al [7] used 14 years of total monthly rainfall and monthly average value of other weather parameter for west coast of India to develop weather indices which were used to forecast coconut yield for the same region.…”

Section: Introductionmentioning

confidence: 99%

Forecasting of Kharif Rice and Jute Yield in North Bengal through Statistical Model

Nandi

Rai²,

Mondal³

et al. 2021

IJECC

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Crop yield forecasting under the present climate change scenario needs an effective model and its parameter that how crop respond to the weather variable. A number of weather based models have been developed to estimate the crop yield for the various crops at block, district and state level. Among the different model statistical model is more popular and commonly used. The current study was undertaken to evaluate the performance of statistical model for rice and jute yield forecast of four different district viz. Cooch Behar, Jalpaiguri, Uttar Dinajpurand and Dakhin Dinajpur. Among the four districts Cooch Behar district found superior for kharif rice yield prediction (1.46% error with RMSE 177.68 kg/ha) whereas in case of jute crop its performance was the best in the Jalpaiguri district (-0.44% error with RMSE 217.50 kg/ha).

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Comparative evaluation of linear and nonlinear weather-based models for coconut yield prediction in the west coast of India

Cited by 24 publications

References 26 publications

Weather-Based Neural Network, Stepwise Linear and Sparse Regression Approach for Rabi Sorghum Yield Forecasting of Karnataka, India

Weather-Based Neural Network, Stepwise Linear and Sparse Regression Approach for Rabi Sorghum Yield Forecasting of Karnataka, India

Modeling the Relationship between Rice Yield and Climate Variables Using Statistical and Machine Learning Techniques

Forecasting of Kharif Rice and Jute Yield in North Bengal through Statistical Model

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