Determining the Dynamics of Agricultural Water Use: Cases from Asia and Africa

Across Africa and Asia, water resources are being affected by a complex mixture of social, economic, and environmental factors. These include climate change and population growth, food prices, oil prices, financial disruptions, and political fluctuations. The need to produce more food will have one of the largest impacts on water and will continue to reshape the patterns of agricultural water use in major food-growing regions. With this increasing demand on water for agriculture, from large-scale irrigation to intensification of rainfed systems, it is becoming increasingly important to ensure that water resources decision-making has access to information that captures the spectrum of water uses, across seasons, and over time. Furthermore, the major sectors that place demands on water and otherwise affect the resource need water-related information to inform their decisions. In this paper we consider two cases where the range of agricultural water management uses have been examined. We examine the methodologies and approaches used, the utility of this information to decision-making in the water and agricultural sectors, and the limitations of the information gathered

Determining the Dynamics of Agricultural Water Use: Cases from Asia and Africa

Across Africa and Asia, water resources are being affected by a complex mixture of social, economic, and environmental factors. These include climate change and population growth, food prices, oil prices, financial disruptions, and political fluctuations. The need to produce more food will have one of the largest impacts on water and will continue to reshape the patterns of agricultural water use in major food-growing regions. With this increasing demand on water for agriculture, from large-scale irrigation to intensification of rainfed systems, it is becoming increasingly important to ensure that water resources decision-making has access to information that captures the spectrum of water uses, across seasons, and over time. Furthermore, the major sectors that place demands on water and otherwise affect the resource need water-related information to inform their decisions. In this paper we consider two cases where the range of agricultural water management uses have been examined. We examine the methodologies and approaches used, the utility of this information to decision-making in the water and agricultural sectors, and the limitations of the information gathered

Water Metrics

Society has a universal need for water that crosses all sectors of activity. We need to be able to measure progress towards sustainable water for all by working towards targets that consider the different dimensions of water resources and use, including water quantity and quality. A suite of indicators that reflect water use by different sectors is needed to measure progress towards the forthcoming SDGs’ [sustainable development goals] water-related targets. Such indicators will need to rely on national data, must consider the variation in data availability, and can be complemented with new cost-effective ways for data collection. Remote sensing measurements, smart field sensors, ICT technologies, and open access databases create new opportunities to more accurately, cost-effectively and transparently quantify water resources. However, the usefulness and relevance of any indicators will be as important as the ease of measurement. The challenge in progressing towards the water-related targets is to ensure that a balance is achieved between the competing uses of water, meeting human needs while maintaining ecosystem health

Evaluating the Effect of Training Data Size and Composition on the Accuracy of Smallholder Irrigated Agriculture Mapping in Mozambique Using Remote Sensing and Machine Learning Algorithms

Mapping smallholder irrigated agriculture in sub-Saharan Africa using remote sensing techniques is challenging due to its small and scattered areas and heterogenous cropping practices. A study was conducted to examine the impact of sample size and composition on the accuracy of classifying irrigated agriculture in Mozambique’s Manica and Gaza provinces using three algorithms: random forest (RF), support vector machine (SVM), and artificial neural network (ANN). Four scenarios were considered, and the results showed that smaller datasets can achieve high and sufficient accuracies, regardless of their composition. However, the user and producer accuracies of irrigated agriculture do increase when the algorithms are trained with larger datasets. The study also found that the composition of the training data is important, with too few or too many samples of the “irrigated agriculture” class decreasing overall accuracy. The algorithms’ robustness depends on the training data’s composition, with RF and SVM showing less decrease and spread in accuracies than ANN. The study concludes that the training data size and composition are more important for classification than the algorithms used. RF and SVM are more suitable for the task as they are more robust or less sensitive to outliers than the ANN. Overall, the study provides valuable insights into mapping smallholder irrigated agriculture in sub-Saharan Africa using remote sensing techniques

Mapping crop water productivity in the Nile Basin through combined use of remote sensing and census data

In ICID. 21st Congress on Irrigation and Drainage: Water Productivity towards Food Security, Tehran, Iran, 15-23 October 2011. New Delhi, India: ICIDICID Transaction No. 30-ARemote sensed imagery in combination with secondary agricultural statistic was used to map crop water productivity (WP) in the Nile River Basin. Land productivity and crop tandardized gross value production (SGVP) were calculated at administrative level using the agricultural census data. Actual evapotranspiration (Eta) generated from remote sensing was used to assess crops consumptive water use. WP was then calculated by dividing SGVP by Eta in the cropped areas. Results show land productivity has a huge variation across the basin. SGVP per hectare in the basin varies from 20 $/ha to 1833$/ha. Likewise SGVP, water productivity in the basin is highly variable. It ranges from 0.01 $/m3 to 0.2$/m3. Observed patterns in the water productivity indicate that WP differences in the Nile basin are highly related to crop yield, which varies in different regions and also in irrigated and rainfed systems. Similarly, overall low WP is because of low yields, chiefly rainfed agriculture. This indicates that there is scope for enhancing WP in the Nile Basin through expanding irrigated agriculture and generally increasing yield