ASSESSMENT OF THE EFFECT OF VARIABILITY IN RAINFALL PARAMETERS ON SELECTED CROPS YIELD IN BENUE STATE, NIGERIA
ASSESSMENT OF THE EFFECT OF VARIABILITY IN RAINFALL PARAMETERS ON SELECTED CROPS YIELD IN BENUE STATE, NIGERIA
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Date
2023-06
Authors
AGASHUA, Hungur
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Abstract
Rainfall plays a key role in crop production and agriculture as a whole. There are many parameters of rainfall all of which are of great importance to agriculture but most researchers studied onset date (OD), cessation date (CD) of rains and Length of growing season (LGS). Also, most researchers used Walter‟s 1960 method to estimate onset and cessation dates of rain. This research assessed the effect of rainfall variability on selected crops yield in Benue State. Rainfall and crops yield data were used for the study. Rainfall data were obtainedfrom Akperan Orshi Polytchnic, Gboko and Nigerian Meteorological Agency (NiMet) Makurdi stations whereas crops yield data were obtained from Benue State Agricultural and Rural DevelopmentAgency(BNARDA),Makurdi.Itexaminedthetrendsofrainfallparametersand selectedcropsyield,assessedtherelationshipbetweenthemandforecastedtheyieldfor2050. The research employed Man Kendall test to estimate the trends of the derived rainfall parameters and selected crops yield, Pearson Product Momentum Correlation to estimate the relationship between rainfall parameters and crops yield, stepwise multiple regression to develop forecast models and forecast error percentage formula (FE %) was used to test the validity of the developed forecast models. This research used the developed forecast models to forecast crops yield for 2050. This research found out that no rainfall parameter indicated significant trend at both Gboko and Makurdi stations. Maize, sorghum and soya beans yield showed increasing significant trends at 0.01 significant level. Among the rainfall parameters, ODhadsignificantrelationshipwithsoyabeansat0.05significantlevel,numberofrainydays (NRD) had significant relationship with rice at 0.05 significant level and amount of rain in the months of the growing season (ARMGS) showed significant relationship with rice and soyabeansyieldsinGbokoat0.01and0.05significantlevelsrespectivelywhileonlyCDhad significant relationship with rice yield at Makurdi station at 0.01 significant level. Resultsfor stepwise multiple regression analysis gave three (3) yield forecast models: Y = 2.214 – 0.637LogX1 for rice and Y = 0.005 + 0.418LogX2 for soya beans at Gboko and Y = 4.130–
3.805LogX3 for rice at Makurdi. The forecast models that passed the validity test using FE% andwerebestfitfortheforecastoftheselectedcropsyieldinBenuestateweretwo;Y=2.214
–0.637LogX1forriceatGbokoonthebasisofARMGSandY=4.130–3.805LogX3forrice inMakurdionthebasisofcessationdate(CD).Researchesofthissuchareofgreatimportance as it will foster adequate planning by the BNARDA and other agencies, since the selected crops constitute the main staple food crops among the populace. The research recommends a comparative study on the use of Walter‟s (1967), Adefolalu‟s (1993) and Hybrid methods to estimate onset, cessation dates and length of growing season and that farmers should apply the two developed forecast models for rice yield forecast in the state.
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A DISSERTATION SUBMITTED TO THE SCHOOL OF POSTGRADUATE STUDIES, AHMADU BELLO UNIVERSITY, ZARIA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE AWARD OF MASTER OF SCIENCE DEGREE IN GEOGRAPHY DEPARTMENT OF GEOGRAPHY AND ENVIRONMENTAL MANAGEMENT, FACULTY OF PHYSICAL SCIENCES, AHMADU BELLO UNIVERSITY, ZARIA, NIGERIA