Comparison of methods used in predicting irrigation performance indicators

Small-scale irrigation schemes (SSIS) in developing countries have been crucial, but the evidence about their performance has not been sufficiently analyzed. This chapter documents such evidence by reviewing and classifying the performance indicators. It also assesses literature on whether there are discernible trends in the efficiency of SSIS, identifies and classifies SSIS constraints, and characterizes various channels through which SSIS might affect poverty. Objectives are achieved via a systematic review of literature from 1990 to 2017. Results indicate a lack of standardization of irrigation performance indicators, and there is evidence that irrigation has boosted agricultural performance. Even though SSIS were associated with higher productivity than rain-fed agriculture, they performed below their full potential due to undervaluation of irrigation water by irrigation authorities, farmer characteristics, costs, institutional setups, the policy environment, and design, cultural, community, and environmental issues. SSIS are important tools for poverty reduction, and relevant policy implications are outlined.

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Influence of Spatial Resolution on Remote Sensing-Based Irrigation Performance Assessment Using WaPOR Data

Remote Sensing ◽

10.3390/rs12182949 ◽

2020 ◽

Vol 12 (18) ◽

pp. 2949

Author(s):

Megan Blatchford ◽

Chris M. Mannaerts ◽

Yijian Zeng ◽

Hamideh Nouri ◽

Poolad Karimi

Keyword(s):

Remote Sensing ◽

Open Access ◽

Performance Assessment ◽

Spatial Resolution ◽

Performance Indicators ◽

Crop Production ◽

Water Productivity ◽

Temporal Trends ◽

Irrigation Performance ◽

Crop Water

This paper analyses the effect of the spatial assessment scale on irrigation performance indicators in small and medium-scale agriculture. Three performance indicators—adequacy (i.e., sufficiency of water use to meet the crop water requirement), equity (i.e., fairness of irrigation distribution), and productivity (i.e., unit of physical crop production/yield per unit water consumption)—are evaluated in five irrigation schemes for three spatial resolutions—250 m, 100 m, and 30 m. Each scheme has varying plot sizes and distributions, with average plot sizes ranging from 0.2 ha to 13 ha. The datasets are derived from the United Nations Food and Agricultural Organization (FAO) water productivity through open access of remotely sensed–derived data (the Water Productivity Open Access Portal—WaPOR) database. Irrigation indicators performed differently in different aspects; for adequacy, all three resolutions show similar spatial trends for relative evapotranspiration (ET) across levels for all years. However, the estimation of relative ET is often higher at higher resolution. In terms of equity, all resolutions show similar inter-annual trends in the coefficient of variation (CV); higher resolutions usually have a higher CV of the annual evapotranspiration and interception (ETIa) while capturing more spatial variability. For productivity, higher resolutions show lower crop water productivity (CWP) due to higher aboveground biomass productivity (AGBP) estimations in lower resolutions; they always have a higher CV of CWP. We find all resolutions of 250 m, 100 m, and 30 m suitable for inter-annual and inter-scheme assessments regardless of plot size. While each resolution shows consistent temporal trends, the magnitude of the trend in both space and time is smoothed by the 100 m and 250 m resolution datasets. This frequently results in substantial differences in the irrigation performance assessment criteria for inter-plot comparisons; therefore, 250 m and 100 m are not recommended for inter-plot comparison for all plot sizes, particularly small plots (<2 ha). Our findings highlight the importance of selecting the spatial resolution appropriate to scheme characteristics when undertaking irrigation performance assessment using remote sensing.

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IRRIGATION PERFORMANCE INDICATORS FOR BEST IRRIGATION MANAGEMENT IN AN IRRIGATION DISTRICT

Acta Horticulturae ◽

10.17660/actahortic.2011.889.66 ◽

2011 ◽

pp. 521-528

Author(s):

J. Roldán ◽

M. Díaz ◽

M.F. Moreno

Keyword(s):

Performance Indicators ◽

Irrigation Management ◽

Irrigation District ◽

Irrigation Performance

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Evaluating Irrigation Efficiency with Performance Indicators: A Case Study of Citrus in the East of Spain

Agronomy ◽

10.3390/agronomy10091359 ◽

2020 ◽

Vol 10 (9) ◽

pp. 1359

Author(s):

Lorena Parra ◽

Marta Botella-Campos ◽

Herminia Puerto ◽

Bernat Roig-Merino ◽

Jaime Lloret

Keyword(s):

Service Delivery ◽

Economic Efficiency ◽

Performance Indicators ◽

Cropping System ◽

Farming Systems ◽

Productive Efficiency ◽

Irrigation Performance ◽

Water Efficiency ◽

Remarkable Effect ◽

Delivery Performance

Improving water efficiency in farming systems is one of the major challenges of these decades. Water scarcity due to climate change, together with the increasing demand of food, is leading experts from around the world find appropriate indicators for water-use efficiency. In this paper we propose and test different indicators for service delivery performance, productive efficiency, and economic efficiency. Since the characteristics of the studied area and the citrus cropping system in the East of Spain are particular, we include in our analysis two other variables which are key to understanding the changes in the indicators: the obtained productivity, and the applied irrigation. The indicators and these two variables are tested with the information provided by farmers of citrus orchards belonging to an irrigation community from the East of Spain. The effect of different factors, such as cultivated varieties, type of farmer (professional or non-professional), or plantations’ size, are evaluated against the productivity and irrigation performance of the evaluated orchards. The effect of excess of irrigation on the indicators is also studied with the previous factors. Finally, an artificial intelligence system is used to predict productive efficiency of an orchard, based on the size and the water supply. Among the proposed indicators, the service delivery performance indicators came out to be the least useful and might provoke overirrigation due to the lack of accuracy of the data used for its calculation. The productive and economic efficiency indicators have been useful to illustrate the remarkable effect that excess of irrigation has on water efficiency, since a reduction of 66% of productive efficiency is found for some of the analysed varieties. On other cases, a reduction of 50% in economic efficiency is detected due to the excess of irrigation. Moreover, the excess of irrigation implied higher economic efficiency in only one of the evaluated varieties.

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A Framework for Irrigation Performance Assessment Using WaPOR data: The case of a Sugarcane Estate in Mozambique

10.5194/hess-2021-409 ◽

2021 ◽

Author(s):

Abebe Demissie Chukalla ◽

Marloes L. Mul ◽

Pieter van der Zaag ◽

Gerardo van Halsema ◽

Evaristo Mubaya ◽

...

Keyword(s):

Performance Assessment ◽

Performance Indicators ◽

Water Productivity ◽

Effective Means ◽

Cost Effective ◽

Absolute Error ◽

Irrigation Performance ◽

Irrigation Method ◽

Spatially Distributed ◽

Response To Water

Abstract. The growing competition for the finite land and water resources and the need to feed an ever-growing population requires new techniques to monitor the performance of irrigation schemes and improve land and water productivity. Datasets from FAO’s portal to monitor Water Productivity through Open access Remotely sensed derived data (WaPOR) is increasingly applied as a cost-effective means to support irrigation performance assessment and identifying possible pathways for improvement. This study presents a framework that applies WaPOR data to assess irrigation performance indicators including uniformity, equity, adequacy and land and water productivity differentiated by irrigation method (furrow, sprinkler and centre pivot) at the Xinavane sugarcane estate, Mozambique. The WaPOR data on water, land and climate is near-real-time and spatially distributed, with the finest spatial resolution in the area of 100 m. The WaPOR data were first validated agronomically by examining the biomass response to water, then the data was used to systematically analyse seasonal indicators for the period 2015 to 2018 on ~8,000 ha. The WaPOR based yield estimates were found to be comparable to the estate-measured yields with ±20 % difference, root mean square error of 19 ± 2.5 ton/ha and mean absolute error of 15 ± 1.6 ton/ha. A climate normalization factor that enables the spatial and temporal comparison of performance indicators are applied. The assessment highlights that in Xinavane no single irrigation method performs the best across all performance indicators. Centre pivot compared to sprinkler and furrow irrigation shows higher adequacy, equity, and land productivity, but lower water productivity. The three irrigation methods have excellent uniformity (~94 %) in the four seasons and acceptable adequacy for most periods of the season except in 2016, when a drought was observed. While this study is done for sugarcane in one irrigation scheme, the approach can be broadened to compare other crops across fields or irrigation schemes across Africa with diverse management units in the different agro-climatic zone within FaO WaPOR coverage. We conclude that the framework is useful for assessing irrigation performance using the WaPOR dataset.

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