Modelling Soil Moisture Balance for Okra Cultivars in Makurdi Agro Climate Using Decision Support System for Agro Technology Transfer (DSSAT)

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Ben I.E.
Enokela O.S.

Abstract: Soil moisture balance from okra (abelmoschus esculentus) field was performed on experimental plot of the Department of Agricultural and Environmental Engineering, Joseph Sanwuan Tarka University Makurdi -Nigeria. Field and laboratory experiments were conducted on four okra cultivars planted and irrigated by drip system at different levels (80% (I1), 60% (I2), 45% (I3), and 15% (I4)) according to the agronomic practices of okra.  Decision Support System for agro technology Transfer (DSSAT) model for the Crop Environment Resource Synthesis (CERES) was used to model soil moisture balance by linear regression (multivariate analysis of variance, (MANOVA)) and validated. The soil was sandy loam with high field capacity (FC) at I4 and nearly uniform drainage (D) except for I4.. Runoff (R) decreases from 35.71 for I1 to 0.07 for I4 implying that R, D and change in water storage (∆S) are functions of the amount and duration of irrigation. The models reported an acceptable deviation from the ideal line of the R and ∆S at higher values confirming degree of the correlation between the observed and predicted dataset but experienced difficulties with estimating lower values due to lower magnitudes in irrigation. From the foregoing it is concluded that decreasing water application results in an increase in irrigation and the reverse is also true.

Modelling Soil Moisture Balance for Okra Cultivars in Makurdi Agro Climate Using Decision Support System for Agro Technology Transfer (DSSAT). (2024). International Journal of Latest Technology in Engineering Management & Applied Science, 13(7), 120-129. https://doi.org/10.51583/IJLTEMAS.2024.130714

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Modelling Soil Moisture Balance for Okra Cultivars in Makurdi Agro Climate Using Decision Support System for Agro Technology Transfer (DSSAT). (2024). International Journal of Latest Technology in Engineering Management & Applied Science, 13(7), 120-129. https://doi.org/10.51583/IJLTEMAS.2024.130714

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