Abstract
This study assesses the relationship between key meteorological parameters and crop yield in Kano and Sokoto States, north-western Nigeria. Daily meteorological records of rainfall amount, rainfall rate, minimum and maximum temperatures, and relative humidity for 2022 and 2023 were aggregated to monthly and seasonal scales and compared with crop-performance data, including households reporting, planted and harvested areas, harvested quantity, and yield for maize, rice, sweet potato, yam and cowpea. Meteorological data were obtained from the 2022/2023 National Agricultural Sample Survey (NASS) and the Nigerian Meteorological Agency (NiMet). Descriptive statistics and Pearson correlation analysis were used to characterise the meteorological regime of each state and to examine inter-variable relationships. Results show that Sokoto recorded higher rainfall totals in both years (1,349.5 mm in 2022 and 938.1 mm in 2023) than Kano (828.1 mm and 696.4 mm, respectively), a pattern consistent with the unusually heavy 2022 rainy season recorded across the Sokoto–Rima basin and much of Nigeria. Monthly rainfall and relative humidity were strongly and significantly positively correlated in both states (Kano: r = 0.953, p < 0.001; Sokoto: r = 0.923, p < 0.001), while rainfall was negatively associated with maximum temperature, significantly so in Sokoto (r = −0.602, p = 0.038). Agricultural data showed that Sokoto achieved higher major-season yields than Kano for maize (1,973 vs 1,412 kg/ha), rice (1,936 vs 1,575 kg/ha) and sweet potato (2,273 vs 1,818 kg/ha), broadly mirroring its higher seasonal rainfall, whereas Kano showed substantially greater minor (dry)-season cropping activity, consistent with its more extensive irrigation infrastructure. The findings indicate that rainfall amount and relative humidity were important meteorological characteristics associated with agricultural productivity, while also highlighting the influence of irrigation and other non-meteorological factors. The study demonstrates the value of combining meteorological data with agricultural survey data for local-level food-security planning