Articles

Poor irrigation water quality due to oil spillage on surface water can result in food insecurity, health and economic challenges. This paper investigated the effect of total petroleum hydrocarbon (TPHC) and lead (Pb) on irrigation water quality in the oil spill prone area of the Niger Delta region of Nigeria. Water samples were taken from five different sections labelled A, B, C, D, and E along the Choba River, in Rivers State, Nigeria. Sections B, C, D and E were direct industrial effluent discharge points while section A was without direct industrial effluent discharge. Standard methods were employed in the water sampling and analysis. Suitability of Choba river water for irrigation was assessed by comprehensive pollution index (CPI) that incorporated salinity, sodicity, and permeability hazard potentials as well as the specific toxicity hazard potentials of TPHC and Pb. Results showed that all primary water parameters except pH were within the Food and Agriculture Organization (FAO) guidelines. The pH was low, ranging between 4.48 and 5.6. TPHC values for four out of the five samples were greater than the 10mg/l guideline as recommended by the Directorate of Petroleum Resources for surface water. TPHC for the four samples ranged between 14.52 and 174.32mg/l. The parameters with the most impact on CPI include EC, PI and TPHC with TPHC having the most impact. Water samples from sections A, B and E with CPI values 0.14, 0.37, and 0.8 respectively were classified in the clean, sub clean and slightly polluted categories respectively, while water samples from sections C and D with CPI values greater than 1 range from moderately to heavily polluted and not suitable for agricultural irrigation. Only water sample A was found suitable for irrigation.

Estimation of crop water requirements is of paramount importance towards the management of agricultural water resources, which is a major mitigating strategy against the effects of climate change on food security. South Africa water shortage poses a threat on agricultural efficiency. Since irrigation uses about 60% of the fresh water available, it therefore becomes important to optimise the use of irrigation water in order to maximize crop yield at the farm level in order to avoid wastage. In this study, combined application of an artificial neural network (ANN) and a crop – growth simulation model for the estimation of crop irrigation water requirements and the irrigation scheduling of potatoes at Winterton irrigation scheme, South Africa was investigated. The crop-water demand from planting to harvest date, when to irrigate, the optimum stage in the drying cycle when to apply water and the amount of irrigation water to be applied per time, were estimated in this study. Five feed –forward backward propagation artificial neural network predictive models were developed with varied number of neurons and hidden layers and evaluated. The optimal ANN model, which has 5 inputs, 5 neurons, 1 hidden layer and 1 output was used to predict monthly reference evapotranspiration (ETo) in the Winterton area. The optimal ANN model produced a root-mean-square error (RMSE) of 0.67, Pearson correlation coefficient (r) of 0.97 and coefficient of determination (R²) of 0.94. The validation of the model between the measured and predicted ET_o shows a r value of 0.9048. The predicted ETo was one of the input variables into a crop growth simulation model, called CROPWAT. The results indicated that the total crop water requirement was 1259.2 mm/decade and net irrigation water requirement was 1276.9 mm/decade, spread over a 5-day irrigation time during the entire 140 days of cropping season for potatoes. A combination of the artificial neural networks and the crop growth simulation models have proved to be a robust technique for estimating crop irrigation water requirements in the face of limited or no daily meteorological datasets.