O.J. Mudiare’s research while affiliated with Ahmadu Bello University and other places

Adoption of appropriate soil, water and crop management practices could be the gate way to enhanced soil chemical quality, crop productivity as well as national and global food security. A study was undertaken to assess the influence of irrigation regimes, with two rice varieties and varying cowdung manure application rates on some selected chemical properties of a sandy clay loam soil in a semi-arid zone of Sokoto, Nigeria. The study evaluated four water management practices [alternate wetting and drying (AWD) treatments: AWD1: irrigating when water level was 10 cm below the soil surface, AWD2: irrigate when water level was 15 cm below the soil surface, AWD3: irrigate when water level fell to 20 cm below the soil surface and CF: continuous flooding] with two rice varieties (FARO 44 and TOFA) and with four rates of cowdung amendment (0, 5, 10 and 15 t ha-1). The Plots were arranged in a split-split plot design replicated three times. Rice was grown on the field in 2020 and 2021 seasons. Measurements of chemical properties of the soils were carried out using standard procedures. The results revealed that water management and rice varieties had no significant effect on the chemical properties of the soils. The results showed significant effects of cow dung rates on OC, Ca and K contents of the soils with 5, 10 and 15 t ha-1 better than 0 t ha-1. The study recommended that similar research should be extended to 3-4 years to assess residual effects of the management practices imposed on soil chemical quality.

The objective of this study was to predict the probabilities of occurrences of long dry spells and their lengths during the planting period in rainfed farming season for future planning in Gusau and its environs North-Western Nigeria. Markov chain and probability distribution models were used to help predict in advance the longtime dry spells occurrences in the study area. Daily rainfall amount for each year was used to determine the probabilities of wet and dry days at different orders of Markov Chain. Gamma distribution was used with the help of INSTAT plus statistical package to estimate the length of dry spells in May, June and July. The early season dry spells were determined to occur usually between the first and the last decades of May. The Longest dry spells for the month of May were determined to be 26, 25 and 21 days in 2020, 2022 and 2030 respectively. Low frequencies of dry spells are to be anticipated in June with only 10 days in 2026 and July with only 12 days in 2024. The month of May from 2011 to 2020 with mean dry days of measured and predicted data were found to be 14 and 15 days respectively while coefficient of variation (CV) of 0.3, shows a stable dry spell in the coming years in May. The R between the observed and the predicted values were averagely good, mean error (ME) -1.25,-1.00 and 1.63 between the longest monthly observed and predicted dry spell were less than all the observed data. The root mean square error (RMSE) indicated that the month of June has the highest measure of precision 3.18, followed by the month of July4.46 and May 5.50. Since, early season rainfall is uncertain and erratic than the mid-season, early planting of moisture sensitive crop like maize in Gusau without supplementary irrigation would be highly risky.

The study aimed at evaluating two models (Swartzendruber and Horton) using furrow infiltration data measured in Samaru, Zaria. These measurements were carried out on the three field plots A, B and C. Infiltration parameters were generated from the data measured from plot A and B and fitted into the models for predictions of water infiltrated depths at different time intervals. The predictions from the models were compared with the measured field data from plot C. Statistical indices such as coefficient of determination (R2), root mean square error (RMSE) and T-test at 5% level of significance were used to determine the best performing model. The results show that, value of R2 and RMSE recorded for Horton’s model was 0.996 and 3.05, while the value of R2 and RMSE recorded for Swartzendruber’s model was 0.998 and 3.01, respectively. The T-test values obtained for Horton’s model was 2.93, while 2.51 was recorded for Swartzendruber’s model. The results of the evaluation indicated that both models were considered suitable because they presented high values of R2 and low values of RMSE as suggested by Aminu (2019). Similarly, the results obtained from the T-test statistics indicated that there is a no significant difference between the models evaluated because the calculated values of ‘T’ 2.93 and 2.51were greater than the tabulated value 2.365 at 5% confidence interval. Therefore, this study recommends that both models should be used to predict infiltration rates of soils in the study area.

Soil air-filled pore spaces play a crucial role in the exchange of greenhouse gases between agricultural soils and the adjacent atmos-phere. Soil bulk densities, air-filled porosity, total porosity were calculated from soil samples while Static chamber methods were used in the collection of gas samples for the measurement of greenhouse gases (CO2, CH4 and N2O). Air-filled porosity alone and air-filled plus porosity calculated from the soil samples were fitted in models used for the prediction of relative gas diffusion coeffi-cients (DS/Do) and the pore tortuosity factor (TF). The relationship between pore space indices and greenhouse gases emissions from such soil was also studied. The result shows the relationship between soil pore space indices (DS/Do and TF) and greenhouse gas emissions of rice fields under irrigation management with a correlation coefficient range from -0.04 to 0.57. Generally, the rela-tionship between CO2 and CH4 with DS/Do predict either with air-filled alone or air-filled plus porosity models were negatively cor-related in irrigated plots. However, N2O emissions positively correlate with the DS/Do. Almost all the fluxes were positively corre-lated with the soil tortuosity factor predicted with all models. The result from these studies confirmed the contribution of irrigation water management practices adopted for this study revealing that air permeability may contribute to the variability of greenhouse gas emissions hence for our understanding of the dynamics of greenhouse fluxes from soil to be improved, we need to include soil diffusions coefficients (Ds/Do) and soil tortuosity factor (TF) in predictive models as controlling factors.