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Application of regression modeling for the prediction of field crop coefficients in a humid sub-tropical agro-climate: a study in Hamirpur district of Himachal Pradesh (India)

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Abstract

Prediction of crop coefficients is important to establish optimized irrigation water scheduling and management practices. In the present study, regression modeling was utilized to predict the field crop coefficients of crops grown in the humid sub-tropical agro-climate of Hamirpur (Himachal Pradesh, India). Field experiments were conducted on seven crops categorized as Cereals (Wheat and Maize), Oilseed (Indian mustard), Vegetable (Potato), Fodder crop (Sorghum), Green manure crop (Guar), and Legumes (Pea). The crop coefficients were determined using a modification and field-based approach. In the modification approach, FAO-recommended standard crop coefficients were modified using the crop coefficient modification procedure given in FAO-56. In the field-based approach, crop coefficients were obtained as the ratio of field crop evapotranspiration to the reference evapotranspiration. FAO modified crop coefficients presented satisfactory performance with the field crop coefficients (squared error = 0.0009–0.0225; R2 = 0.80–0.89; bias error = − 0.09–0.15). New crop coefficients were developed by performing regression modeling between the FAO modified and field-based crop coefficients. Furthermore, new crop evapotranspiration values were obtained using new crop coefficients, which presented a strong and reliable agreement with the field crop evapotranspiration values, i.e., they exhibited small bias error = 10–24 mm, and high R2 = 0.90–0.93. The developed regression equations can be employed as useful tools for predicting field crop coefficients from the FAO-56 modified crop coefficients, subsequently resulting in the precise estimation of the crop evapotranspiration.

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Abbreviations

R n :

Net radiation at crop surface (MJ m−2 day−1)

G :

Soil heat flux density (MJ m−2 day−1)

T :

Mean daily air temperature at 2 m height (°C)

(es−ea):

Saturation vapor pressure deficit (kPa)

Δ:

Slope of vapor pressure curve (kPa °C−1)

γ :

Psychrometric constant (kPa °C−1)

I :

Average infiltration depth (mm)

K c ini (FAO) :

FAO-recommended Kc ini value

K c ini (heavy wetting) :

KC ini derived from the FAO-curve corresponding to the heavy wetting

K c ini (light wetting) :

KC ini derived from FAO-curve corresponding to light wetting for the corresponding parameters

K c mid/end(FAO) :

FAO-recommended Kc value

RHmin :

Mean value for daily minimum relative humidity (%) (20% < RHmin < 80%)

u 2 :

Mean value for daily wind speed at 2 m height (m s−1) (1 m s−1 < u2 < 6 m s−1)

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Acknowledgements

The authors would like to acknowledge the support extended by National Institute of Technology Hamirpur (India) and Shoolini University (India). The authors are thankful to the reviewers for providing constructive criticism on the paper.

Funding

The financial support for the study was received through DBT (Department of Biotechnology, Govt. of India) sponsored project titled “Social-economic-environmental tradeoffs in managing Land-river interface (2019–2021)”.

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Authors and Affiliations

  1. Civil Engineering Department, Shoolini University, Solan, 173229, Himachal Pradesh, India

    Arunava Poddar & Navsal Kumar

  2. College of Agricultural Engineering and Technology, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, Srinagar, 190025, India

    Rohitashw Kumar

  3. Civil Engineering Department, National Institute of Technology Hamirpur, 177005, Hamirpur, Himachal Pradesh, India

    Vijay Shankar

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  1. Arunava Poddar
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  2. Navsal Kumar
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  4. Vijay Shankar
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Correspondence to Arunava Poddar.

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Poddar, A., Kumar, N., Kumar, R. et al. Application of regression modeling for the prediction of field crop coefficients in a humid sub-tropical agro-climate: a study in Hamirpur district of Himachal Pradesh (India). Model. Earth Syst. Environ. 8, 2369–2381 (2022). https://doi.org/10.1007/s40808-021-01234-0

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