A new methodology to assess the maximum irrigation rates at catchment scale using geostatistics and GIS

Paz, José Miguel de; Albert, Carlo; Visconti, Fernando; Jimenez, Mariana; Ingelmo, F.; Molina, M. J.

doi:10.1007/s11119-015-9392-y

Cited by 6 publications

(3 citation statements)

References 33 publications

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“…Geostatistical methods are widely employed to assess the spatial distribution of soil properties in agricultural areas (Robinson and Metternicht, 2006;Fu et al, 2013;De Paz et al, 2015) and contribute to better land use management (Nael et al, 2004;Liu et al, 2016). There are many different univariate kriging methods used with soil variables (e.g.…”

Section: Introductionmentioning

confidence: 99%

Spatial distribution of soil chemical properties in an organic farm in Croatia

Bogunović

Pereira

Brevik

2017

Science of The Total Environment

112

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Soil pH, electrical conductivity (EC), organic matter (OM), available phosphorus (AP), and potassium (AK) are some of the most important indicators of soil fertility. These soil parameters are highly variable in space and time, especially in agricultural areas, with implications for crop production. The aim of this work was to study the spatial variability of pH, EC, OM, AP and AK using kriging and co-kriging methods in the Rasa River Valley (Croatia). As co-variates for each variable we considered the distance from the sea (DFS), distance from the river channels (DFC), pH, EC, OM, AP and AK. Only the variables with a significant correlation with the predictor were used as predictor variables. The results showed that soils of the study area had high pH, EC, OM and AK values and a low concentration of AP. The spatial variability was high for EC and low for pH levels. pH, EC, OM and AK had significant positive correlations. All these variables had significant negative correlations with AP. The exponential model was the best to model OM, AK and AP. Spherical and Gaussian models were the most accurate to model pH and EC. Spatial dependence was high for soil AK, EC and pH, and moderate for soil OM and AP. The incorporation of auxiliary variables increased the precision of the estimations. CoK_DFS was the best method to predict soil EC and AP, while Cok_EC, was better to estimate soil pH and Cok_pH and Cok_OM predicted soil OM and AK with the best accuracy. The maps produced with the best predictors showed that pH, EC, OM and AK had high levels in the northern and eastern parts of the study area. The opposite trend was identified in relation to the AP spatial pattern.

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Section: Introductionmentioning

confidence: 99%

Spatial distribution of soil chemical properties in an organic farm in Croatia

Bogunović

Pereira

Brevik

2017

Science of The Total Environment

112

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“…The spatial maps of AWC parameter prepared from the best interpolation method could be used for efficiently estimating the irrigation water requirement and irrigation interval for different crops at different regions at a given point of time. This information would be very much useful for planners, irrigation engineers and decision makers to recommend and plan efficient water management strategies (De Paz et al, 2015). Greenholtz et al, (1988) examined about the spatial and temporal variability of hydrologic soil characteristics on Glendale clay loam soil.…”

Section: Introductionmentioning

confidence: 99%

Geo-Statistical Assessment of Spatial Variability of Soil Water-Holding Capacity for Optimal Irrigation Under Semi-Arid Vertisols in South India

Ramachandran

Lalitha

Kannan

et al. 2022

Preprint

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Based on a field survey conducted in Lalgudi block, Tiruchirapalli district, Tamil Nadu, India, details pertaining to the crops grown, cropping pattern, land utilization and soil samples were collected from 20 locations under 7 different soil textures of semi-arid vertisols. Using the data, efficient geostatistical models have been explored to study the spatial variation of irrigation water requirement of paddy, sugarcane and banana grown in the study area. Field capacity and wilting point are two major soil physical properties that would influence the available plant water. These parameters were estimated with the pressure plate apparatus using soil samples collected in the block. Soil texture-wise assessment was made with regard to the field capacity, wilting point and soil water holding capacity based on the observations. The locations of Lalgudi block were grouped into three categories based on the mean and standard deviation of available water holding capacity, field capacity and wilting point parameters and were statistically assessed. The groups were made by considering the locations with values of a parameter lying under (i) < (Mean – SD); (ii) (Mean – SD) to (Mean + SD); and (iii) > (Mean + SD) limits. Geostatistics were applied for identifying the best interpolation method in order to acquire the spatial map of the available water holding capacity. An initial data exploration indicated about non-normal distribution of the available water holding capacity. Accordingly, log-transformation was made before kriging technique was used for the data. Ordinary kriging and Disjunctive kriging were explored with six models viz., Circular, Spherical, Exponential, Gaussian, Penta-spherical and Sine-Hole effect models and were used for spatial prediction. An estimate of Root Mean Square Error (RMSE) based onthe six models ranged from 2.1 to 2.5 for available water holding capacity parameter. The cross validation statistics indicated that Sine-Hole effect model with disjunctive kriging of available water holding capacity was superior for interpolation with minimum value of RMSE and moderate spatial dependency. The Sine-Hole effect model was found to overestimate the soil properties when interpolation was carried out and gave mean prediction error of -0.07 which indicated that the model was highly efficient. With the available water holding capacity map of Lalgudi block, total available water, readily available water and irrigation interval of paddy, sugarcane and banana were estimated. The irrigation interval of paddy, sugarcane and banana were found to vary between 1-2, 3-5 and 2-4 days respectively within Lalgudi block. Adoption of spatial algorithms for estimating the crop water requirement would greatly help the irrigation planners and water policy makers to create efficient regional plans for making precision irrigation under semi-arid vertisols.

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“…In this context, irrigated agriculture will play a critical role supporting increased production in arid and semi-arid regions, and enhancing crop quality through supplemental irrigation in temperate or humid regions (Daccache et al 2014a, b;De Paz et al 2015). However, freshwater availability and abstraction to support an expanding agricultural sector will need to be balanced against competing demands for domestic (household) water supply, water for industrial processing and to support environmental flows and protect ecosystems.…”

Section: Introductionmentioning

confidence: 99%

Modelling impacts of precision irrigation on crop yield and in-field water management

et al. 2017

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Precision irrigation technologies are being widely promoted to resolve challenges regarding improving crop productivity under conditions of increasing water scarcity. In this paper, the development of an integrated modelling approach involving the coupling of a water application model with a biophysical crop simulation model (Aquacrop) to evaluate the in-field impacts of precision irrigation on crop yield and soil water management is described. The approach allows for a comparison between conventional irrigation management practices against a range of alternate so-called 'precision irrigation' strategies (including variable rate irrigation, VRI). It also provides a valuable framework to evaluate the agronomic (yield), water resource (irrigation use and water efficiency), energy (consumption, costs, footprint) and environmental (nitrate leaching, drainage) impacts under contrasting irrigation management scenarios. The approach offers scope for including feedback loops to help define appropriate irrigation management zones and refine application depths accordingly for scheduling irrigation. The methodology was applied to a case study in eastern England to demonstrate the utility of the framework and the impacts of precision irrigation in a humid climate on a high-value field crop (onions). For the case study, the simulations showed how VRI is a potentially useful approach for irrigation management even in a humid environment to save water and reduce deep percolation losses (drainage). It also helped to increase crop yield due to improved control of soil water in the root zone, especially during a dry season.

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A new methodology to assess the maximum irrigation rates at catchment scale using geostatistics and GIS

Cited by 6 publications

References 33 publications

Spatial distribution of soil chemical properties in an organic farm in Croatia

Spatial distribution of soil chemical properties in an organic farm in Croatia

Geo-Statistical Assessment of Spatial Variability of Soil Water-Holding Capacity for Optimal Irrigation Under Semi-Arid Vertisols in South India

Modelling impacts of precision irrigation on crop yield and in-field water management

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