Linear mathematical models for yield estimation of baby corn (Zea mays L.)
DOI:
https://doi.org/10.14719/pst.2618Keywords:
Baby corn, crop yield estimation, maize, mathematical modeling, regression-based models, yield predictionAbstract
Linear mathematical models have been developed for predicting baby corn yield in terms of cob volume for two cycles of maize (Zea mays L.). Cob volume is directly proportional to morphological parameters such as length, weight, and girth; hence, linear mathematical models have been developed. Primary data for a random selection of 60 cobs for each cycle were collected, and lab work was carried out to measure the corn ears and cob growth parameters. An irregular distribution was observed among all six growth parameters examined in the study. Descriptive statistical measures were employed to facilitate the description of growth parameters. The final volume of the baby corn cob was used for crop yield estimation. The water displacement method was employed to measure the actual volume of cobs, which was then compared with the volumes estimated using the developed mathematical models. For both cycles, similar trends were observed in both estimated and actual volumes of cobs, providing numerical confirmation for the validity of the developed mathematical models. The theoretical validity of these models was also established using statistical measures such as R2, adjusted R2, F-test, P-value, and correlation coefficient. Any deviations between estimated and actual volumes would indicate changes in the dependent variables of the model, attributed to the effects of climate change, as other internal and external factors are held constant. These models offer a critical predictive tool for stakeholders, enabling improved yield predictions and optimized resource allocation. As a result, they facilitate strategic planning for increased profitability.
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Copyright (c) 2022 Neetu Rani, Jitender Singh Bamel, Savita Garg, Abhinav Shukla, Sumit Kumar Pathak, Rishta Nandini Singh, Nandini Singh, Sara Gahlot, Kiran Bamel
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