Predicting Nutritional Quality of Dual-Purpose Cowpea Using NIRS and the Impacts of Crop Management

Junior Bruno Ndiaye, Augustine K. Obour (), Keith Harmoney, Doudou Diouf, Aliou Faye, Lamine Diamé, Dioumacor Fall and Yared Assefa ()
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Junior Bruno Ndiaye: Regional Study Center for Improving Adaptation to Drought (CERAAS), Senegalese Institute of Agricultural Research (ISRA), Thies BP 3320, Senegal
Augustine K. Obour: Agricultural Research Center-Hays, Kansas State University, Hays, KS 67601, USA
Keith Harmoney: Agricultural Research Center-Hays, Kansas State University, Hays, KS 67601, USA
Doudou Diouf: Regional Study Center for Improving Adaptation to Drought (CERAAS), Senegalese Institute of Agricultural Research (ISRA), Thies BP 3320, Senegal
Aliou Faye: Regional Study Center for Improving Adaptation to Drought (CERAAS), Senegalese Institute of Agricultural Research (ISRA), Thies BP 3320, Senegal
Lamine Diamé: Department of Animal Biology, Cheikh Anta Diop University of Dakar (UCAD), Km 1, Avenue Cheikh Anta Diop, Dakar BP 5005, Senegal
Dioumacor Fall: National Agricultural Research Center, Bambey BP 0053, Senegal
Yared Assefa: Agricultural Research Center-Hays, Kansas State University, Hays, KS 67601, USA

Sustainability, 2023, vol. 15, issue 16, 1-14

Abstract: Cowpea fodder has been one of the favored livestock forages for centuries in sub-Saharan Africa, particularly in Senegal. However, little research has been conducted on quantifying the nutritional quality of cowpea fodder because of the costly wet chemistry analysis. The main objective of this study was to develop predictive equations for a sustainable quantification of the nutritional quality of dual-purpose cowpea fodder using near infrared spectroscopy (NIRS) and to investigate the influence of cropping system, fertilizer, genotype, and their interaction on biomass yield and cowpea forage nutritional value. In this study, 120 samples from a dual-purpose cowpea variety trial were used to develop NIRS equations to estimate forage quality parameters including concentrations of crude protein (CP), acid detergent fiber (ADF), neutral detergent fiber (NDF), calcium (Ca), phosphorus (P), potassium (K), and iron (Fe). Partial least squares (PLS) regression generated prediction equations using NIRS wavelength measurements, and reference wet chemistry analysis from calibration samples were developed. The PLS prediction equations for the different forage quality parameters had an R 2 of calibration 0.94, 0.93, 0.88, 0.63, 0.69, 0.87, and 0.94 for CP, ADF, NDF, Ca, P, K, and Fe, respectively. Using these prediction equations, correlation of the predicted values of the calibration subset and the prediction test subset resulted in significant positive relationships, with R 2 of 0.83, 0.74, 0.70, 0.63, 0.59, 0.75, and 0.83 for CP, ADF, NDF, Ca, P, K, and Fe, respectively. The corresponding RMSE of these relationships was 0.91, 2.68, 3.45, 0.23, 0.06, 0.11, and 100 for CP, ADF, NDF, Ca, P, K, and Fe, respectively. The range and mean concentrations of the calibration subset overlapped with that of the prediction subset for all parameters evaluated. Cross-validation procedures indicated good correlations between wet chemistry analysis and NIRS forage quality estimates. Results of the second experiment showed that the cropping system had no significant effect on cowpea forage yield and nutritive value. However, cowpea variety and fertilizer, both individually and their interaction, had a significant effect on fodder yield and cowpea forage quality. We conclude that the NIRS calibration equations developed can be used to accurately predict the cowpea forage quality parameters evaluated in this study.

Keywords: dual-purpose; cowpea; forage quality; near infrared spectroscopy (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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