Irrigation Management Zones Research Articles

Implications of Spatial Variability of Soil Physical Attributes in Delineating Site-Specific Irrigation Management Zones for Rice Crop

Implications of Spatial Variability of Soil Physical Attributes in Delineating Site-Specific Irrigation Management Zones for Rice Crop

Site‐specific estimation of spatiotemporal crop evapotranspiration through field‐level sensing data

AbstractThis study integrated field‐level sensor data into the FAO‐56 Penman–Monteith algorithm to provide a site‐specific estimate of crop evapotranspiration. This was carried out at two contrasting sites for pea and bean (Manawatū) and barley (Hawke's Bay) crops managed within two irrigation management zones, at each site, under variable‐rate irrigation systems in New Zealand. Daily crop evapotranspiration estimates were calculated using data from a weather station situated at the field site combined with in‐field crop sensing data (spectral reflectance, canopy temperature, and canopy height). In addition, calibrated soil moisture data were used with a soil water balance model to compare estimations of daily crop evapotranspiration with those estimated using the crop sensing method. The results indicated that variable crop responses to different irrigation strategies and soil types provided a good opportunity to quantify different levels of spectral reflectance, canopy temperature, and consequently the estimation of crop water use. The statistical comparisons revealed that the modified FAO‐56 Penman–Monteith using crop sensor data compared well with the more conventional soil water balance approach using soil moisture data (R2 = 0.70, 0.83, 0.91 for barley, pea, and bean, respectively). Overall, the results from this study indicated that crop sensing approaches combined with the FAO‐56 Penman–Monteith model have potential to provide a more easily determined site‐specific field estimation of crop evapotranspiration than other methods, and it can take into consideration the spatiotemporal variability of crop growth in a field.

Modeling spatial distribution patterns to delineate irrigation and nutrient management zones for high-density olive orchards

Modeling spatial distribution patterns to delineate irrigation and nutrient management zones for high-density olive orchards

ORBITAL REMOTE SENSING FOR THE MANAGEMENT OF AREAS IRRIGATED WITH A CENTRAL PIVOT SYSTEM

Irrigated agriculture is considered one of the most important techniques for agricultural production in the world, despite being the sector that uses water resources the most. Thus, technological advances have been constantly developed and applied in the sector as a way to improve water management and reduce its consumption, considering that some cultures are affected by economic costs and feasibilities of production, because the water cost has increased significantly in recent years. Remote sensing is a technology that has provided good results for estimating evapotranspiration (ETr) in producing areas, enabling more efficient irrigation management for low costs. This study aimed to answer the question about how the generation of irrigation management zones (IMZs) can be used to optimize water use and improve irrigation systems and which ETr models are better for it. Three ETr estimation models (Surface Energy Balance Algorithm for Land [SEBAL], Mapping Evapotranspiration at High Resolution and with Internalized Calibration [METRIC] and Simple Algorithm for Evapotranspiration Retrieving [SAFER]) were used to design IMZs in center pivot cultivated with cotton. The design of IMZs with ETr data proved to be a viable alternative, making it possible to improve water management in irrigated systems, reducing costs with viability irrigation for cotton and others cultures. The METRIC method displayed the greatest ease in obtaining the data used in the ETr estimation for generating the IMZs.

Irrigation management zone delineation and optimal irrigation scheduling for center pivot irrigation systems

In this study, a three-stage irrigation management zone delineation approach and a model-based scheduler are proposed to provide variable rate irrigation schedules for a 26.4-hectare field at the Alberta Irrigation Technology Centre in Lethbridge, Alberta, Canada. After an initial demarcation of the investigated field into quadrants according to the four different crops growing in the field, the k-means clustering approach is used to delineate each quadrant further into smaller irrigation management zones on the basis of soil hydraulic parameters, elevation of the field and the variable rate irrigation resolution of the center pivot system. For each management zone, a long short-term memory (LSTM) neural network model is then developed to characterize the soil water dynamics in the root zone. Based on the LSTM models, and based on the LSTM models, a mixed-integer model predictive controller (MPC) is designed to prescribe irrigation schedules for the various management zones in each quadrant to ensure optimal crop water uptake while minimizing water consumption and irrigation costs. The applicability of the proposed approach is illustrated in simulations. The results show that the proposed approach achieves 26% water savings compared to a triggered scheduling scheme.

Irrigation management zone strategies impact assessment on potential crop yield, water and energy savings

In irrigated agriculture, soil spatial variability is a primary factor for low irrigation efficiency. In regions such as the Brazilian Cerrado, where there is a continuous growth of irrigated agriculture and limited water supplies, it is important to seek alternatives to reach an efficient and sustainable irrigated agriculture. In this context, precision irrigation has great potential. Better strategies must be established, especially for center pivot irrigation conditions. Irrigation considering management zones is a promising option; however, criteria must be set to delimitate management zones and evaluate irrigation performance in such situations. This study aims to assess the impact of the use of management zones on irrigation performance. Therefore, two center pivots, PivoBHBV and PivoBHALPA, with distinct soil water physical traits, were evaluated. Two management zones strategies were considered to carry out the simulations. One of the strategies was based on regions in the format of square grids with areas of 25, 100 and 225 m2. In the other strategy, the unsupervised grouping algorithm Fuzzy C-Means was used to create management zones. The irrigation performed on those management zones was assessed by comparing their performance with the irrigation conducted in the grid areas. The irrigation management zones were delimited based on the interpolated maps of soil available water capacity (AWC), and the management was conducted individually for each zone. The simulations were made through a model developed with the Python language. The results indicate similarity between the irrigation with management zones and the grid irrigation, as it did not significantly increase water and energy demands or reduce soybean yield. Precision irrigation practiced management zones increased irrigation efficiency, reducing the effect of soil spatial variability. The total irrigation depth applied was, on average, 1.47 % lower than the irrigation by grids. Comparing the grid and the management zones irrigations, water and energy savings potential and the average yield increase potential were 1.85 %, 1.75 %, and 0.41 %.

Correlation of UAV and satellite-derived vegetation indices with cotton physiological parameters and their use as a tool for scheduling variable rate irrigation in cotton

Current irrigation management zones (IMZs) for variable rate irrigation (VRI) systems are static. They are delineated in the beginning of the season and used thereafter. However, recent research has shown that IMZ boundaries are transient and change with time during the growing season. The primary goal of this study was to explore the potential of using vegetation indices (VIs) developed from unmanned aerial vehicle (UAV) and satellite images to predict cotton physiological parameters that can be used to delineate in-season boundaries of IMZs. A 2 year study was conducted in a 38 ha commercial cotton field in southwestern Georgia, USA. Throughout the two growing seasons, VIs were calculated from UAV, PlanetScope, and Sentinel-2 images. Predawn leaf water potential (LWPPD) and plant height were measured at 37 locations in the field on the same day as the flights and correlated with UAV and satellite based-VIs. GNDVI (Green normalized difference vegetation index) was the best predictor of plant height with correlation values of 0.72 (p < .0001) and 0.84 (p < .0001) for 2019 and 2020, respectively. A secondary goal was to compare the performance of dynamic VRI (DVRI) to conventional irrigation. The field was divided into alternating parallel conventional, and DVRI strips to compare the two scheduling methods. The conventional strips were irrigated using the farmer’s standard method and individual IMZs within the DVRI strips were irrigated based on soil water tension (SWT) measured with a wireless soil moisture sensor network. LWP and SWT measurements correlated well. IMZs were initially delineated using soil texture, apparent soil electrical conductivity (ECa), and yield maps and satellite images from previous years and were modified in-season to reflect patterns observed in the plant height maps. In 2020, the DVRI system prescribed an average irrigation amount of 50.8 mm while conventional irrigation applied an average of 58.4 mm. Average yields for DVRI and conventional were 1248 and 1191 kg ha−1, respectively. The DVRI system resulted in average yield 4.6% higher than conventional irrigation, while applying 14.0% less water. Despite the lower water application by the DRVI system, the performance comparison between the DRVI and the conventional irrigation was not conclusive.

Within-Field Variability in Nutrients for Site-Specific Agricultural Management in Irrigated Cornfield

HighlightsPhosphorus and nitrogen in soil and plants varied during the growing season across the cornfield.Spatial variability in nutrients caused spatial variability in plant growth and crop yield across the cornfield.Hydrological characteristics and soil hydraulic properties were responsible for spatial variability in nutrients.Need to consider spatial variability in nutrients in delineating management zones before adopting management practices.Abstract. The adoption of precision agricultural practices in Alabama has been increasing in recent years. Although challenging, understanding nutrient variability in agricultural fields is important for site-specific management. This study investigated phosphorus (P) and nitrogen (N) variability in an irrigated cornfield located in the Town Creek Watershed of Tennessee Valley Region of Alabama, USA during the 2019 growing season. Three different irrigation management zones (high yield-HY, moderate yield-MY, and low yield-LY) were delineated based on ten years of historical records of crop yield, soil texture, and measured topography using the Management Zone Analyst (MZA) software. The soil samples at 0 to 15 cm, 15 to 30 cm, and 30 to 60 cm depths were collected five different times during the 2019 growing season. Both manure and inorganic fertilizer were uniformly applied during the growing season. At all soil depths, HY and MY zones had higher soil nutrient concentrations than the field average nutrient concentration. However, the LY zone had below field average soil nutrient concentration in the entire soil profile. The plants (above-ground) in HY and MY zones had higher than average nutrient concentrations and the plants in the LY zone had lower than average nutrient concentrations throughout the cornfield. This site-specific nutrient variation in these zones was due to within-field terrain attributes, differences in soil properties, and surface runoff losses. A significant difference was recorded in the corn yield in the HY and LY zones of the field. Two extreme groups of nutrients were found in the cornfield similar to irrigation management zones (HY and LY). A zone with a higher yield also had higher soil and plant nutrients in the zone (HY), and a zone with a lower yield also had lower soil and plant nutrients in the zone (LY). Incorporating nutrient variability for site-specific management in management zone delineation can help with reducing nutrient application, nutrient loss, and improving yield. This study supports the adopting of precision agricultural management practices based on in-field nutrient variability. Keywords: Corn, Crop yield variability, Irrigation, Management zones, Nitrogen, Nutrient variability, Phosphorus, Site-specific management, Topographical wetness index (TWI).

Development and Evaluation of a Variable-Rate Irrigation Management Method in the Mississippi Delta

HighlightsWe developed and evaluated a variable-rate irrigation (VRI) management method for five crop years in the Mississippi Delta.VRI management significantly reduced irrigation water use in comparison with uniform-rate irrigation (URI). There was no significant difference in grain yield and irrigation water productivity between VRI and URI management.Soil apparent electrical conductivity (ECa) was used to delineate irrigation management zones and generate VRI prescriptions.Sensor-measured soil water content was used in irrigation scheduling.Abstract. Variable-rate irrigation (VRI) allows producers to site-specifically apply irrigation water at variable rates within a field to account for the temporal and spatial variability in soil and plant characteristics. Developing practical VRI methods and documenting the benefits of VRI application are critical to accelerate the adoption of VRI technologies. Using apparent soil electrical conductivity (ECa) and soil moisture sensors, a VRI method was developed and evaluated with corn and soybean for five crop years in the Mississippi Delta. Soil ECa of the study fields was mapped and used to delineate VRI management zones and create VRI prescriptions. Irrigation was scheduled using soil volumetric water content measured by soil moisture sensors. A center pivot VRI system was employed to deliver irrigation water according to the VRI prescription. Grain yield, irrigation water use, and irrigation water productivity in the VRI treatment were determined and compared with that in a uniform-rate irrigation (URI) treatment. Results showed that the grain yield and irrigation water productivity between the VRI and URI treatments were not statistically different with both corn and soybean crops. The VRI management significantly reduced the amount of irrigation water by 22% in corn and by 11% in soybean (p = 0.05). Adoption of VRI management could improve irrigation water use efficiency in the Mississippi Delta. Keywords: Soil electrical conductivity, Soil moisture sensor, Variable rate irrigation, Water management.

Irrigation Scheduling to Promote Corn Productivity in Central Alabama

Agriculture is the largest consumer of water in the United States. Results from previous studies have shown that it is possible to substantially reduce irrigation amounts and maintain corn yield. The objectives of this study were to evaluate the advantages and disadvantages of two irrigation scheduling methods for corn production in Alabama. Two irrigation scheduling methods evaluated were: a) Checkbook, which is one of the conventional methods used by farmers that is based on the soil water balance estimated using water lost by evapotranspiration and its replacement through rainfall or irrigation, and b) Sensor-based, which was based on soil matric potential values recorded by soil moisture tension sensors installed in the field. The experimental field was divided into two irrigation management zones (zone A and zone B) based on soil properties of each field. During the 2014 season in zone A, significant grain yield differences were observed between the two irrigation methods. The Checkbook plots exhibited greater yield than Sensor-based plots: 10181 kg ha-1 and 9696 kg ha-1, respectively. The greater yield on the Checkbook plots could be associated with higher irrigation rate applied, 148 mm more, compared with the Sensor-based plots. In zone B, there were no significant yield differences between both irrigation methods; however, Sensor-based plots out yielded Checkbook plots, with 9673 kg ha-1 and 9584 kg ha-1, respectively. Even though the irrigation amount applied in Checkbook located in zone B was higher, 102 mm more, there were no significant yield differences. Therefore, it suggests that the Sensor-based method was promissory irrigation scheduling strategy under the conditions of zone B. In 2015, there were no significant grain yield differences between zone A and zone B when the data from the Checkbook plots were analyzed. However, the Sensor-based treatment produced a statistically significant difference of grain yield of 13597 kg ha-1 in zone A and 11659 kg ha-1 in zone B, also both zones received the same amount of irrigation. Overall results of both growing seasons indicated that the use of the Sensor-based irrigation scheduling treatment resulted in similar values of total profit per hectare when compared to Checkbook method. The Sensor-based method seems a promising strategy that could result in water and financial savings, but more research is required.

Model-based optimal delineation of drip irrigation management zones

Delineation of management zones for applying Variable Rate Irrigation (VRI) in drip irrigation presents unique challenges due to the directionality of the drip lines and the fact that the water flow rate can not be varied along a drip line. This work presents a method for optimal delineation of irrigation zones under such constraints. It is assumed that historical weather records, a soil map, a simulation model that predicts the crop development in response to weather and irrigation, as well as crop and water prices, are available. The first step consists of estimating the water productivity function of the crop for each soil type if managed optimally, using multi-objective optimization. The second step consists of estimating the water productivity function of the crop on each soil if the irrigation is managed according to another soil. Optimal delineation of the irrigation zones is then obtained by solving a non-linear optimization problem in which the decision variables are the positions of the zones boundaries. This involves an intermediate step in which the area of each soil in each irrigation zone is calculated, and the soil according to which the irrigation should be managed in each zone is determined. The procedure is illustrated on two examples involving three soils and a cotton crop in Greece. In both cases, optimal management zones delineation and management was obtained using the average weather of years 1982–1991 and performance was estimated for years 1992–2016. The results show that splitting the field into several management zones increases profit slightly via yield increase and/or water savings, depending on the relative price of water.

Soil sensing technology improves application of irrigation water

Dynamic irrigation scheduling for Variable-rate irrigation systems is essential to accurately estimate the spatiotemporal pattern of irrigation water requirement. Real-time, sensor-based and soil-water balance scheduling methods were compared on a trial under a Variable-rate center pivot irrigation system. The soil-water balance scheduling used the FAO56-ET model to calculate daily soil-water deficits and to determine crop water requirements using climate data from a local climate station. The sensor-based scheduling system used a wireless soil moisture sensing network to trigger irrigation when soil water deficit reached a critical value in a web-based user interface. The scheduling was conducted on pea and French bean crop trials under one center pivot, with two delineated irrigation management zones at Massey University’s No.1 Farm, Palmerston North, New Zealand.The results showed variation between the two scheduling methods where the soil water balance assumed that the soil is well drained. The sensor-based scheduling technique delivered 27–45% less water. As there were no significant crop growth and yield differences between the two approaches, irrigation-water-use efficiency was greater under the sensor-based scheduling regime. Further research is planned to assess the feasibility of including this monitoring system in a precision irrigation control system.

Assessing the Effectiveness of Variable-Rate Drip Irrigation on Water Use Efficiency in a Vineyard in Northern Italy

Although many studies in the literature illustrate the numerous devices and methodologies nowadays existing for assessing the spatial variability within agricultural fields, and indicate the potential for variable-rate irrigation (VRI) in vineyards, only very few works deal with the implementation of VRI systems to manage such heterogeneity, and these studies are usually conducted in experimental fields for research aims. In this study, a VR drip irrigation system was designed for a 1-ha productive vineyard in Northern Italy and managed during the agricultural season 2018, to demonstrate feasibility and effectiveness of a water supply differentiated according to the spatial variability detected in field. Electrical resistivity maps obtained by means of an electro-magnetic induction sensor were used to detect four homogeneous zones with similar soil properties. In each zone, a soil profile was opened, and soil samples were taken and analyzed in laboratory. Two irrigation management zones (MZs) were identified by grouping homogeneous zones on the basis of their hydrological properties, and an irrigation prescription map was built consistently with the total available water (TAW) content in the root zone of the two MZs. The designed drip irrigation system consisted of three independent sectors: the first two supplied water to the two MZs, while the third sector (reference sector) was managed following the farmer’s habits. During the season, irrigation in the first two sectors was fine-tuned using information provided by soil moisture probes installed in each sector. Results showed a reduction of water use by 18% compared to the ‘reference’ sector without losses in yield and product quality, and a grape’s maturation more homogeneous in time.

Integrating SEBAL with in-Field Crop Water Status Measurement for Precision Irrigation Applications—A Case Study

The surface energy balance algorithm for land (SEBAL) has been demonstrated to provide accurate estimates of crop evapotranspiration (ET) and yield at different spatial scales even under highly heterogeneous conditions. However, validation of the SEBAL using in-field direct and indirect measurements of plant water status is a necessary step before deploying the algorithm as an irrigation scheduling tool. To this end, a study was conducted in a maize field located near the Venice Lagoon area in Italy. The experimental area was irrigated using a 274 m long variable rate irrigation (VRI) system with 25-m sections. Three irrigation management zones (IMZs; high, medium and low irrigation requirement zones) were defined combining soil texture and normalized difference vegetation index (NDVI) data. Soil moisture sensors were installed in the different IMZs and used to schedule irrigation. In addition, SEBAL-based actual evapotranspiration (ETr) and biomass estimates were calculated throughout the season. VRI management allowed crop water demand to be matched, saving up to 42 mm (−16%) of water when compared to uniform irrigation rates. The high irrigation amounts applied during the growing season to avoid water stress resulted in no significant differences among the IMZs. SEBAL-based biomass estimates agreed with in-season measurements at 72, 105 and 112 days after planting (DAP; r2 = 0.87). Seasonal ET matched the spatial variability observed in the measured yield map at harvest. Moreover, the SEBAL-derived yield map largely agreed with the measured yield map with relative errors of 0.3% among the IMZs and of 1% (0.21 t ha-1) for the whole field. While the FAO method-based stress coefficient (Ks) never dropped below the optimum condition (Ks = 1) for all the IMZs and the uniform zone, SEBAL Ks was sensitive to changes in water status and remained below 1 during most of the growing season. Using SEBAL to capture the daily spatial variation in crop water needs and growth would enable the definition of transient, dynamic IMZs. This allows farmers to apply proper irrigation amounts increasing water use efficiency.

Irrigation Management Scale and Water Application Method to Improve Yield and Water Productivity of Field-Grown Strawberries

Improvements in water productivity are of primary importance for maintaining agricultural productivity and sustainability. Water potential-based irrigation management has proven effective for this purpose with many different crops, including strawberries. However, problems related to spatial variability of soil properties and irrigation efficiency were reported when applying this management method to strawberries in soils with rock fragments. In this study, a field-scale experiment was performed to evaluate the impacts of three irrigation management scales and a pulsed water application method on strawberry yield and water productivity. An analytical solution to Richards’ equation was also used to establish critical soil water potentials for this crop and evaluate the effects of the variability in the soil properties. Results showed that spatial variability of soil properties at the experimental site was important but not enough to influence crop response to irrigation practices. The studied properties did not present any spatial structure that could allow establishing specific management zones. A four-fold reduction in the size of the irrigation management zones had no effect on yield and increased the water applications. Pulsed application led to significant yield (22%) and water productivity (36%) increases compared with the standard water application method used by the producer at the experimental site.

A weighted multivariate spatial clustering model to determine irrigation management zones

Management of agricultural fields according to spatial and temporal variability is an important aspect of precision agriculture. Precision management relies on division of a field into areas with homogeneous characteristics, management zones (MZs), which are likely affected by multiple, interrelated factors. We present a method, based on machine learning and spatial statistics, to analyze the spatial relationship between a set of variables and determine management zones in a vineyard. The method involves: (1) fitting a model that quantifies the relationship between multiple variables and yield; (2) fitting a model that quantifies the effect of the spatial variability of multiple variables on yield spatial characteristics; and (3) developing a weighted multivariate spatial clustering model as a method to determine MZs. Twelve variables were sampled for 3893 vines in the wine grape vineyard. These variables included soil properties, terrain characteristics, and environmental impact, as well as crop-condition, using indices calculated from remote sensing images. The predictor variables were spatially characterized using hot-spot analysis (Getis Ord Gi* Z-score values) to assess their spatial variability. A gradient boosted regression trees (BRT) algorithm was used to analyze the spatial multivariable effect on yield spatial characteristics. MZs were determined using multivariate K-means clustering, with relative weights given to the predictors, based on their relative influence on yield spatial variability provided by the BRT model. This method was compared to ordinary K-means clustering and K-means with spatial representation of the variables without weights using a dissimilarity index and spatial autocorrelation measures. Model performance was found to be very high and demonstrated that among the evaluated predictors, crop condition indices were the most important regressors for yield and its spatial characteristics. The weighted multivariate spatial clustering was found to perform better in terms of separability of the points and their spatial distribution than the other two clustering techniques. Quantifying yield and its within-field spatial variability, ranking the effects of the predictors and their spatial variabilities, and segmentation of MZs through multivariable spatial analysis, are expected to benefit irrigation management and agricultural decision-making processes.

Potential Water Conservation Using Site-Specific Variable Rate Irrigation

Abstract. Site-specific variable-rate irrigation (VRI) systems can be used to spatially manage irrigation within sub-field-sized zones and optimize spatial water use efficiency. The goal of the research is to provide farmers and consultants a tool to evaluate the potential benefits of implementing VRI. The specific objective of this research is to evaluate the potential water savings using VRI management compared with uniform irrigation management to maintain soil water holding capacity above 50% depletion using two irrigation scenarios: 1) a standard 12.5 mm irrigation per application; and 2) an application to refill the soil profile to field capacity. A 21-year simulation study was carried out on a selected field with varying degrees of soil and topographic variability. The simulated field had 12 soil mapping units with water holding capacities in the top 0.30-m ranging from 42 to 70 mm. The 21-year simulation covering all weather conditions for each soil produced only two significantly different irrigation management zones for scenario 1, and for scenario 2 only one management zone. However, when the 21-year period was divided into periods with different ratios of rainfall to reference evapotranspiration, the simulations identified 1 to 5 management zones with significantly different irrigation requirements. These results indicate that variable rate irrigation system design and management should not be solely based on long term average weather conditions. Years with differing weather conditions should be used for potentially identifying management zones for VRI systems. Irrigation application depths between management zones ranged from 17 to 38 mm. However, when the actual soil areas of the study field were utilized to calculate the total volume of irrigation water applied, it resulted in an increase in water usage in the 2 and 4 management zones ranging from -1.2% to 5.8%. Water usage with VRI over uniform irrigation was greater by -1.6% to 6.8% in the 12.5 mm irrigations and by -1.2% to 2.2% for the field capacity irrigations Keywords: Management zones, Precision farming, Variable-rate irrigation, Water conservation.

Integration of hydrogeophysical datasets and empirical orthogonal functions for improved irrigation water management

Precision agriculture offers the technologies to manage for infield variability and incorporate variability into irrigation management decisions. The major limitation of this technology often lies in the reconciliation of disparate data sources and the generation of irrigation prescription maps. Here the authors explore the utility of the cosmic-ray neutron probe (CRNP) which measures volumetric soil water content (SWC) in the top ~ 30 cm of the soil profile. The key advantages of CRNP is that the sensor is passive, non-invasive, mobile and soil temperature-invariant, making data collection more compatible with existing farm operations and extending the mapping period. The objectives of this study were to: (1) improve the delineation of irrigation management zones within a field and (2) estimate spatial soil hydraulic properties to make effective irrigation prescriptions. Ten CRNP SWC surveys were collected in a 53-ha field in Nebraska. The SWC surveys were analyzed using Empirical Orthogonal Functions (EOFs) to isolate the underlying spatial structure. A statistical bootstrapping analysis confirmed the CRNP + EOF provided superior soil hydraulic property estimates, compared to other hydrogeophysical datasets, when linearly correlated to laboratory measured soil hydraulic properties (field capacity estimates reduced 20–25% in root mean square error). The authors propose a soil sampling strategy for better quantifying soil hydraulic properties using CRNP + EOF methods. Here, five CRNP surveys and 6–8 sample locations for laboratory analysis were sufficient to describe the spatial distribution of soil hydraulic properties within this field. While the proposed strategy may increase overall effort, rising scrutiny for agricultural water-use could make this technology cost-effective.

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

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.

Assessment of Field Spatial and Temporal Variabilities to Delineate Site-Specific Management Zones for Variable-Rate Irrigation

AbstractQuantification and analysis of field variability are important initial steps in delineating potential variable-rate irrigation (VRI) management zones within an agricultural field. This stud...