Search Results (3)

This study uses a triple-sensor collocation approach to evaluate the performance of small-holder irrigation schemes in the Zamra catchment of Northern Ethiopia. Crop water productivity (CWP), as an integrator of biomass production and water use, was used to compare the overall efficiencies of three types of irrigation systems: traditional and modern diversions, and dam-based irrigation water supply. Farmer-reported data often rely on observations, which can introduce human estimation and measurement errors. As a result, the evaluation of irrigation scheme performance has frequently been insufficient to fully explain crop water productivity. To overcome the challenges of using one single estimation method, we used a triple-sensor collocation approach to evaluate the efficiency of three small-scale irrigation schemes, using water productivity as an indicator. It employed three independent methods: remotely sensed data, a model-based approach, and farmer in-situ estimates to assess crop yields and water consumption. To implement the triple collocation appraisal, we first applied three independent evaluation methods, i.e., remotely sensed, model-based, and farmer in-situ estimates of crop yields and water consumption, to assess the crop water productivities of the systems. Triple-sensor collocation allows for the appraisal and comparison of estimation errors of measurement sensor systems, and enables the ranking of the estimators by their quality to represent the de-facto unknown true value, in our case: crop yields, water use, and its ratio CWP, in small-holder irrigated agriculture. The study entailed four main components: (1) collecting in-situ information and data from small-holder farmers on crop yields and water use; (2) derivation of remote sensing-based CWP from the FAO WaPOR open database and time series; (3) evaluation of biomass, crop yields and water use (evapotranspiration) using the AquaCrop model, integrating climate, soil data, and irrigation management practices; (4) performing and analysis of a categorical triple collocation analysis of the independent estimator data and performance ranking of the three sensing and small-holder irrigation systems. Maize and vegetables were used as main crops during three consecutive irrigation seasons (2017/18, 2018/19, 2019/20). Civil war prevented further field surveying, in-situ research, and data collection. The results indicate that remote sensing products are performed best in the modern and dam irrigation schemes for maize. For vegetables, AquaCrop performed best in the dam irrigation scheme. Full article

The mapping of small-scale irrigation areas is essential for food security and water resource management studies. The identification of small-scale irrigation areas is a challenge, but it can be overcome using expert knowledge and satellite-derived high-spatial-resolution multispectral information in conjunction with monthly normalized difference vegetation index (NDVI) time series, and additional terrain information. This paper presents a novel approach to characterize small-scale irrigation schemes that combine expert knowledge, multi-temporal NDVI time series, multispectral high-resolution satellite images, and the random forest classifier in the Zamra catchment, North Ethiopia. A fundamental element of the approach is mapping small-scale irrigation areas using expert decision rules to incorporate the available water resources. We apply expert decision rules to monthly NDVI composites from September 2020 to August 2021 along with the digital elevation model (DEM) data on the slope, drainage order, and distance maps to derive the sample set. The samples were based on the thresholds obtained by expert knowledge from field surveys. These data, along with the four spectral bands of a cloud-free Planet satellite image composite, 12 NDVI monthly composites, slope, drainage order, and distance map were used as input into a random forest classifier which was trained to classify pixels as either irrigated or non-irrigated. The results show that the analysis allows the mapping of small-scale irrigation areas with high accuracy. The classification accuracy for identifying irrigated areas showed a user accuracy ranging from 81% to 87%, along with a producer accuracy ranging from 64% to 79%. Furthermore, the classification accuracy and the kappa coefficient for the classified irrigation schemes were 80% and 0.70, respectively. As a result, these findings highlight a substantial level of agreement between the classification results and the reference data. The use of different expert knowledge-based decision rules, as a method, can be applied to extract small-scale and larger irrigation areas with similar agro-ecological characteristics. Full article

This paper characterizes and compares three types of small-scale irrigation scheme practices in Northern Ethiopia. A multidisciplinary survey approach, collecting information on socioeconomic, biophysical, and institutional aspects of irrigation by the smallholder farmers, was used to investigate and compare aspects of land, water use, and crop productivity, including farmer income and livelihood sustainability. The study was conducted in the Zamra catchment, a sub-basin of the large Tekeze river basin and Nile basin tributary. Three common small-scale irrigation scheme types, i.e., traditional diversion, modern diversion, and dam (reservoir) based irrigation, were compared using four pilot survey areas. From the total of 618 farmer households in the study areas, 242 farmers were selected using stratified random sampling and participated in the survey and research. More than 100 input data were collected from the farmers related to the biophysical, socioeconomic, and institutional factors affecting their work practice and livelihood. Focus group discussions were conducted with elders, the water users association committee, and women-headed households. Descriptive statistics and multivariate analysis were used for quantitative analysis. The result indicates a significant difference between the three irrigation schemes. One important conclusion of this study was that the explanatory value of a single factor (e.g., biophysical), as commonly done in irrigation research and assessment, was seldom sufficient to explain water use, crop yield, and farmer income. Institutional and/or socioeconomic drivers also played an important role in the entire farming practice, income generation, and livelihood of the farmers. This study highlighted the value-added of the multidisciplinary approach (socioeconomic, biophysical, and institutional) for the evaluation of small-scale irrigation practices and livelihood analysis of agricultural smallholders in climate-affected regions, such as the Northern Ethiopian highlands. Full article