Prediction of Kharif cotton yield over Parbhani, Maharashtra : Combination of extended range forecast and DSSAT-CROPGRO-Cotton model

Dakhore, K. K.; Rathod, AS; Kadam, D. R.; Shinde, G. U.; Kadam, Y.E.; Ghosh, K.

doi:10.54302/mausam.v72i3.1313

Cited by 2 publications

(1 citation statement)

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“…The crop simulation model showed similar to the observed flowering days. Results from the crop simulation model evaluation showed that the crop reached flowering stage between 59 and 62 days after sowing for all the various planting dates (similar results are found by [16].…”

Section: Days To Anthesissupporting

confidence: 76%

Estimating Genetic Parameters for DSSAT CROPGRO-Cotton Model Calibration and Validation

Srinivasan

Pazhanivelan

Krishnasamy

et al. 2021

IJPSS

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DSSAT CROPGRO-Cotton Model (version 4.7.5) was generally used to forecast the effect of climate change on productivity. The objective of this study was to calibrate and validate this model in Tamil Nadu, India for simulation of development, growth and seed cotton yield of Suraj cotton cultivars under varied planting dates viz., 28th July, 11th August, 18th August, 25th August, 8th September and 15th September. The model was calibrated with data (phenology, biomass and yield components) collected during 2019. Calibration of CROPGRO-Cotton model with genetic coefficients of cultivar Suraj for seed cotton yield (kg ha-1). Simulation of days to flowering, days from planting to first pod and physiological maturity, LAI and seed cotton yield with normalized RMSE (NRMSE) values of less than 10% across all the various planting dates densities were considered excellent. Finally, we discovered that planting at the right time can mitigate many of the negative effects of fluctuating weather on cotton productivity. As a result conclude that DSSAT model will be used to make decision on cotton planting in changing climates.

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Section: Days To Anthesissupporting

confidence: 76%

Estimating Genetic Parameters for DSSAT CROPGRO-Cotton Model Calibration and Validation

Srinivasan

Pazhanivelan

Krishnasamy

et al. 2021

IJPSS

View full text Add to dashboard Cite

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Novel Ensemble Machine Learning Modeling Approach for Groundwater Potential Mapping in Parbhani District of Maharashtra, India

Masroor

Sajjad

Kumar

et al. 2023

Water

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Groundwater is an essential source of water especially in arid and semi-arid regions of the world. The demand for water due to exponential increase in population has created stresses on available groundwater resources. Further, climate change has affected the quantity of water globally. Many parts of Indian cities are experiencing water scarcity. Thus, assessment of groundwater potential is necessary for sustainable utilization and management of water resources. We utilized a novel ensemble approach using artificial neural network multi-layer perceptron (ANN-MLP), random forest (RF), M5 prime (M5P) and support vector machine for regression (SMOReg) models for assessing groundwater potential in the Parbhani district of Maharashtra in India. Ten site-specific influencing factors, elevation, slope, aspect, drainage density, rainfall, water table depth, lineament density, land use land cover, geomorphology, and soil types, were integrated for preparation of groundwater potential zones. The results revealed that the largest area of the district was found under moderate category GWP zone followed by poor, good, very good and very poor. Spatial distribution of GWP zones showed that Poor GWPZs are spread over north, central and southern parts of the district. Very poor GWPZs are mostly found in the north-western and southern parts of the district. The study calls for policy implications to conserve and manage groundwater in these parts. The ensembled model has proved to be effective for assessment of GWP zones. The outcome of the study may help stakeholders efficiently utilize groundwater and devise suitable strategies for its management. Other geographical regions may find the methodology adopted in this study effective for groundwater potential assessment.

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Prediction of Kharif cotton yield over Parbhani, Maharashtra : Combination of extended range forecast and DSSAT-CROPGRO-Cotton model

Cited by 2 publications

References 0 publications

Estimating Genetic Parameters for DSSAT CROPGRO-Cotton Model Calibration and Validation

Estimating Genetic Parameters for DSSAT CROPGRO-Cotton Model Calibration and Validation

Novel Ensemble Machine Learning Modeling Approach for Groundwater Potential Mapping in Parbhani District of Maharashtra, India

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