Variable Rate Irrigation Research Articles

INNOVATIVE TECHNOLOGIES IN SPRINKLER IRRIGATION: AN OVERVIEW

The overuse of water in conventional irrigation at the farm level is a global issue that can be addressed by improving irrigation techniques. Sprinkler irrigation can reduce water losses by carefully planning the crop, soil, landscape, and weather conditions, as well as installing the proper sprinkler hardware. The application efficiency of sprinkler systems may be enhanced when these systems are coupled with automation for precise irrigation. Sprinkler irrigation has advanced significantly, but new ideas and current practices are driving further progress. These novel ideas include low-pressure sprinklers, smart controllers, fertigation, water-pesticide integrated sprinkler systems, and variable-rate irrigation. The objective of this work is to highlight the introduction of innovative technologies to optimize sprinkler irrigation systems. Suggestions for the future development of sprinkler irrigation technology are offered to ensure its long-term viability.

Development of a Fuzzy Variable Rate Irrigation Control System Based on Remote Sensing Data to Fully Automate Center Pivots

Development of a Fuzzy Variable Rate Irrigation Control System Based on Remote Sensing Data to Fully Automate Center Pivots

Relevance of NDVI, soil apparent electrical conductivity and topography for variable rate irrigation zoning in an olive grove

Relevance of NDVI, soil apparent electrical conductivity and topography for variable rate irrigation zoning in an olive grove

Mapping almond stem water potential using machine learning and multispectral imagery

AbstractAlmonds are a major crop in California which produces 80% of all the world’s almonds. Widespread drought and strict groundwater regulations pose significant challenges to growers. Irrigation regimes based on observed crop water status can help to optimize water use efficiency, but consistent and accurate measurement of water status can prove challenging. In almonds, crop water status is best represented by midday stem water potential measured using a pressure chamber, which despite its accuracy is impractical for growers to measure on a regular basis. This study aimed to use machine learning (ML) models to predict stem water potential in an almond orchard based on canopy spectral reflectance, soil moisture, and daily evapotranspiration. Both artificial neural network and random forest models were trained and used to produce high-resolution spatial maps of stem water potential covering the entire orchard. Also, for each ML model type, one model was trained to predict raw stem water potential values, while another was trained to predict baseline-adjusted values. Together, all models resulted in an average coefficient of correlation of R2 = 0.73 and an average root mean squared error (RMSE) of 2.5 bars. Prediction accuracy decreased significantly when models were expanded to spatial maps (R2 = 0.33, RMSE = 3.31 [avg]). These results indicate that both artificial neural networks and random forest frameworks can be used to predict stem water potential, but both approaches were unable to fully account for the spatial variability observed throughout the orchard. Overall, the most accurate maps were produced by the random forest model (raw stem water potential R2 = 0.47, RMSE = 2.71). The ability to predict stem water potential spatially can aid in the implementation of variable rate irrigation. Future studies should attempt to train similar models with larger datasets and develop a simpler faster workflow for producing stress predictions from field measurements.

Waste Utilization in Horticulture: An Overview

Waste management in horticulture plays a pivotal role in achieving environmental sustainability and optimizing resource utilization. One of the primary methods for waste utilization is composting, which involves the decomposition of organic waste materials to produce nutrient-rich compost. Compost serves as an excellent soil amendment, enhancing soil fertility and promoting plant growth. Additionally, anaerobic digestion offers a valuable approach for converting organic waste into biogas, a renewable energy source that can be used for heating, electricity generation, and vehicle fuel. Furthermore, fermentation processes can be employed to convert fruit and vegetable waste into valuable products such as animal feed and biofuels. The by-products of fermentation, including yeast and enzymes, have applications in various industries, contributing to the circular economy model. Advancements in precision agriculture and smart waste management systems have revolutionized waste utilization in horticulture. Technologies such as GPS, GIS mapping, wireless sensor networks and big data analytics enable data-driven, decision-making and optimize resource allocation. Precision planting, targeted spraying, automated pruning/harvesting and variable rate irrigation are among the innovative practices facilitated by these technologies, leading to increased productivity and efficiency in horticultural operations. Moreover, waste utilization in horticulture contributes to mitigating environmental pollution and reducing greenhouse gas emissions. By diverting organic waste from landfills and incineration, the emission of methane, a potent greenhouse gas, is minimized. Instead, organic waste is repurposed into valuable resources, thereby closing the loop on waste streams and promoting a circular economy.

Real-time mapping of crop canopy temperature using a wireless network of infrared thermometers on a central pivot

Efficient irrigation is essential for sustainable food production and regional water security. Temperature measurements provide information about crop water status, which allows the estimation of water needs and precise irrigation scheduling. This paper proposes a technology for agricultural irrigation management that combines cost-effective implementation with the ability to map crop canopy temperatures using a wireless network of infrared thermometers on a center pivot. The technology integrates Wireless Sensor Networks (WSN) with scalability options, IoT capabilities, and web data availability, enabling precise plant water stress quantification based on canopy temperature and soil water content. It facilitates real-time irrigation prescriptions while the center pivot is in motion, with canopy temperature sensors providing accurate readings of +/- 0.5 °C without additional calibration. Nonlinear Autoregressive Network with Exogenous Inputs (NARX) and Long Short-Term Memory (LSTM) neural network models were trained to predict irrigation prescriptions, achieving Root Mean Squared Error (RMSE < 4.18 mm) and correlation coefficient (r^2 > 0.82). This technology will allow the definition of real-time and site-specific prescriptions for variable-rate irrigation systems using infrared temperature sensors. Future work includes exploring LoRaWAN technologies, implementing a Differential Global Positioning System (DGPS), improving spatial resolution with data fusion algorithms, refining irrigation prescription models, reducing sensor costs and developing models considering soil and crop effects.

Influence of the sampling time interval of canopy temperature on the dynamic zoning of variable rate irrigation

Generating spatial distribution maps of canopy temperature (Tc) is the basis for the dynamic management of a variable rate irrigation (VRI) system based on infrared thermometers (IRTs). To improve the accuracy of management zone delineation based on Tc maps for summer maize and winter wheat with IRTs mounted on a moving sprinkler irrigation system, the influence of sampling time interval of Tc was studied and the VRI prescription maps were compared with those obtained from unmanned aerial vehicle (UAV) thermal imaging on the same day. The experimental sites were located in Shunyi, Beijing, for summer maize and in Dacaozhuang, Hebei Province, for winter wheat. The sampling time intervals were selected as 0.5–26 min. The results demonstrated that the nRMSE of the predicted and measured values for Tc increased and that their correlation coefficient decreased with the extension of the sampling time interval. When the sampling time interval ranged from 0.5 to 5 min, the correlation coefficient and nRMSE between the predicted and measured values for Tc changed little, and the overlap percentage of the same management zones delineated with NRCT between 5 min and the minimum sampling time interval (0.5 or 1 min) was greater than 79%. For any two measuring dates of Tc, their overlap percentage changed greatly, with a maximum value of 67% for NRCT. The average irrigation amount based on the prescription maps of the IRTs system and UAV thermal imaging system on the three observation days was equal. Our results demonstrated techniques to collect IRTs data and to generate VRI prescription map based on this data, and a sampling time interval of no more than 5 minutes was recommended for Tc.

Spatiotemporal Modeling of Soil Water Dynamics for Site-Specific Variable Rate Irrigation in Maize

This study aimed to simulate dynamic irrigation management zones (MZs) in two maize fields for a variable rate hose reel fertigation machine (VRFM) with a four-section boom control. Soil moisture content was measured from nine and four soil moisture sensors in Field 1 (8.2 ha) and Field 2 (2.5 ha), respectively, on different dates during the 2022 crop season. Three and five MZs scenarios were simulated per irrigation and the theoretical maps were processed for implementation. The application maps fitted to the VRFM showed significant spatiotemporal variations in irrigation requirements. For instance, in Field 1, 3-MZ modelling showed that the areas requiring high (H), medium (M), and low (L)-level irrigation on 21 July were 1.60, 4.84, and 1.85 ha, respectively, even though the farmer applied uniform rate over the whole field. H-level sub-areas ranged between 1.22 ha (25 July) and 3.25 ha (7 July), showing a coefficient of variation (CV) of 43.32% for the three MZs, whereas H-level sub-areas for the five MZs varied from 0.41 ha (2 July) to 1.49 ha (7 July) with a CV value of 48.84%. High levels of within-field variability can be addressed using precise and dynamic irrigation MZs fitted to the irrigation technology used.

Irrigation Zone Delineation by Coupling Neural Networks with Spatial Statistics

Variable rate irrigation (VRI) seeks to increase the efficiency of irrigation by spatially adjusting water output within an agricultural field. Central to the success of VRI technology is establishing homogeneous irrigation zones. In this research, we propose a fusion of statistical modeling and deep learning by using artificial neural networks to map irrigation zones from simple-to-measure predictors. We further couple our neural network model with spatial correlation to capture smooth variations in the irrigation zones. We demonstrate the effectiveness of our model to define irrigation zones for a farm of winter wheat crop in Rexburg, Idaho.

Integrating multisource information to delineate oasis farmland salinity management zones in southern Xinjiang, China

Soil salinization and the shortage of water resources have become important factors affecting the sustainable development of agriculture in the southern Xinjiang. Agricultural flooding in winter or spring is the main means to reduce the damage of secondary soil salinization in southern Xinjiang. However, the spatial heterogeneity of soil salinization is not considered in previous irrigation strategy. Excessive irrigation always leads to a serious waste of agricultural water resources. Irrigation zones delineation based on the degree of soil salinization is beneficial for the management of agricultural salinized land and the improvement of water use efficiency in arid areas. This study used 1599 sets of apparent electrical conductivity (ECa) data obtained before winter irrigation and after crop harvest to characterize soil salinity directly. And the spatial heterogeneity of soil salinity in the study area was analyzed directly using ECa values and statistical as well as geostatistical methods. Three main variables obtained from apparent electrical conductivity measurements (EC0.375, EC0.75, EC1.5), vegetation information (NDVI, GNDVI, SAVI), which indirectly reflected the degree of salinity, and environmental factors (Clay, Sand, Silt, DEM), which affecting the distribution of salinity, were used as auxiliary variables. Four zoning models including soil information, soil + vegetation information, soil information + environmental factors and soil + vegetation information + environmental factors were constructed. A multi-scale segmentation algorithm was used to delineate for four zone patterns and evaluate the results of management zones based on the different information. The results showed that there was a strong spatial heterogeneity of soil salinity in the study area, with the coefficients of variation of ECa all higher than 74 %. But the mean of coefficient of variation intra-zone in each zone after delineation was less than 24 %, which was significantly lower than the value before delineation. The zoning result based on only soil information was more fragmented than other zoning results, and the homogeneity intra-zone and heterogeneity among zones were both lower. This result was not desirable to the development and effective implementation of water resource allocation. Management zones delineation with additional vegetation information or environmental factors showed a better zoning result. Delineation using multisource data of soil, vegetation information and environmental factors not only had the highest homogeneity intra-zone and heterogeneity among zones, but also facilitated the implementation of variable-rate irrigation. Consequently, salinity management zoning, which integrates soil, vegetation information and environmental factors, will facilitate the implementation of efficient irrigation with variable irrigating rate.

Field evaluation of a commercial variable-rate irrigation decision support system – a study for maize and sweet corn

ABSTRACT The agricultural sector is facing a pressing need to adopt variable-rate irrigation decision support systems (VRI–DSS) to address global challenges such as water quality, water scarcity, food security, and climate change. This study evaluated a model-based VRI–DSS for maize and sweetcorn crops in a commercial field with two soil zones and a VRI center pivot. The evaluation involved assessing the farmer’s use of the system and comparing the VRI–DSS outputs using default parameters (e.g. virtual climate data) with outputs using local data. The results showed good agreement between the virtual and local climate data (R2 = 0.94, RMSE = 0.51 °C for air temperature; R2 = 0.79, RMSE = 0.53 mm/day for evapotranspiration), except for rainfall, which was overestimated by 12% by the VRI–DSS. The soil water deficit estimates from the local data also matched well with the neutron probe measurements. The farmer applied less irrigation due to water restrictions, but water use efficiency varied between soil zones for sweetcorn. The evaluation showed that VRI–DSS, with accurate climate data, is a useful tool for estimating variable-irrigation requirements for maize and sweetcorn under one system. However, more field data and local rainfall data are required to validate the decision software system.

Exploring deficit irrigation as a water conservation strategy: Insights from field experiments and model simulation

Deficit irrigation has emerged as a viable approach to increase agricultural water productivity. The objectives of this study were to 1) develop water conservation strategies by investigating responses of green bean (Phaseolus vulgaris) and sweet corn (Zea mays var. saccharata) to four irrigation treatments and 2) evaluate the performance of the Decision Support System for Agrotechnology Transfer (DSSAT) model in simulating growth and yield responses of green bean and sweet corn under four irrigation treatments. A field experiment was conducted using 32 experimental plots, established under a linear move sprinkler irrigation system equipped with variable rate irrigation (VRI) technology, at the Tropical Research and Education Center (TREC), Homestead for two cropping seasons between November and March in 2020–2021 and 2021–2022. The experiment involved four irrigation treatments: full irrigation (FI) (T4), and three levels of deficit irrigation, 75% FI (T3), 50% FI (T2), and 25% FI (T1) with four replications arranged in a completely randomized block design (RCBD) for each crop. The average soil moisture content of the full irrigation treatments was maintained above the management allowable depletion (MAD). Field data collection included various parameters of the crops including plant phenology, plant height, canopy width, fresh and dry biomass, and yield. Results confirmed that all three deficit irrigation treatments did not significantly affect the yield and yield components of the two crops during both seasons compared to the T4. This finding revealed that the highest crop water productivity was achieved from T1, 38.3 and 41.4 kg m−3 for green bean and 54 and 53 kg m−3 for sweet corn during the 2020–2021 and 2021–2022 seasons and up to 75% irrigation water saving could be achieved without affecting plant growth and yield. The DSSAT model performed well in simulating yield and yield components for the two crops in response to the four irrigation treatments. Model evaluation results showed that the minimum mean absolute error (MAE) for green bean fresh pod yield simulation was 2.1 Mg ha−1 with an index of agreement (d-Stat) of 0.6, while for biomass, it was 0.24 Mg ha−1 with a d-Stat of 1. Similarly, for sweet corn, the model simulated fresh cob yield with a minimum MAE of 6.51 Mg ha−1 and d-Stat of 0.9 and biomass with MAE of 1.63 Mg ha−1 and d-Stat of 0.9. Overall, implementing deficit irrigation in green bean and sweet corn fields could result in considerable water savings while achieving acceptable crop yield. The DSSAT model could be also a useful tool to simulate green bean and sweet corn responses to different irrigation scenarios and aid water management decisions under South Florida conditions.

Modeling of soil moisture and water fluxes in a maize field for the optimization of irrigation

Precision irrigation is becoming more and more important in agricultural crop production due to the limited water resources resulting from the negative effects of climate change. Modeling of irrigation can support decision makers in the maintenance of the quantitative and qualitative parameters of the cultivated crops, while improving the efficiency of water consumption. In the present paper, a 3D hydrodynamic model was created in the HYDRUS environment to support the modeling of the temporal changes and spatial variations in the water balance (WB) of a maize cultivated Hungarian study site, thereby providing inputs for irrigation scheduling and variable rate irrigation for stakeholders. Beside soil physical parameters, crop evapotranspiration (ETc) values were also considered as model inputs for the phenological crop development stages of the maize from the sowing to the harvesting. This period was between the 3rd of May and the 10th of September in 2020 and the 20th of May and 14th of September for the year 2021. The performance of the model for soil moisture conditions were validated by on-field soil moisture measurements. The overall performance (full vegetation period) of the model was good (r2 = 0.88 for 2020 and r2 = 0.91 for 2021), but slightly varied among different phenological stages. Based on the model, the water balance of the investigated area was determined without any irrigation, alongside the cumulative water fluxes (CWF) through the boundaries including the root water uptake (RWU) of the maize as well. The results revealed that the incoming precipitation was not sufficient to supplement the water content in the soil to the optimal soil moisture range, except when the precipitation level is high enough to balance it. It was concluded that the water balance was negative for the investigated time periods without any irrigation. The specific water deficit (WD) values were calculated considering the area of the study site, which is 1439 m3/ha for 2020 and 2068 m3/ha for 2021. From the obtained results, optimal irrigation schedules were presented to keep the soil moisture content in the optimal range (20.92–14.41 V/V%) in the modeled area for two different vegetation periods. It was found that due to the irrigation the crop water productivity (CWP) increased by 6% and 4% in 2020 and 2021. Overall, the model is able to cope with changing circumstances that could help to mitigate the negative effects of climate change with the reasonable use of limited water resources in the future.

A planning strategy for sprinkler-based variable rate irrigation

Automated variable rate irrigation (VRI) is one of the frontiers for sustainable water use in agriculture. In this paper, we describe a planning procedure for self-propelled rain gun sprinklers, a type of equipment largely used in irrigation of open field crops thanks to its relatively low cost and easy deployment in multiple sites. The sprinkler is commanded by an information system which acquires data from remote sensors (satellite), processes vegetation indices and spatial maps describing the water need of the crop, and computes a VRI prescription map for a specific field, i.e. the water quantity to be supplied to each field sub-area. The VRI prescription map is used to compute the motion commands for a rain gun system pulled by a hose reel. The procedure is based on a sprinkler model, has been validated by simulation, and is fully automated. Experiments have shown the precision in water distribution achievable with software-controlled sprinklers and effective planning strategies.

An Overview of Smart Irrigation Management for Improving Water Productivity under Climate Change in Drylands

Global drylands, covering about 41% of Earth’s surface and inhabited by 38% of the world’s population, are facing the stark challenges of water scarcity, low water productivity, and food insecurity. This paper highlights the major constraints to agricultural productivity, traditional irrigation scheduling methods, and associated challenges, efforts, and progress to enhance water use efficiency (WUE), conserve water, and guarantee food security by overviewing different smart irrigation approaches. Widely used traditional irrigation scheduling methods (based on weather, plant, and soil moisture conditions) usually lack important information needed for precise irrigation, which leads to over- or under-irrigation of fields. On the other hand, by using several factors, including soil and climate variation, soil properties, plant responses to water deficits, and changes in weather factors, smart irrigation can drive better irrigation decisions that can help save water and increase yields. Various smart irrigation approaches, such as artificial intelligence and deep learning (artificial neural network, fuzzy logic, expert system, hybrid intelligent system, and deep learning), model predictive irrigation systems, variable rate irrigation (VRI) technology, and unmanned aerial vehicles (UAVs) could ensure high water use efficiency in water-scarce regions. These smart irrigation technologies can improve water management and accelerate the progress in achieving multiple Sustainable Development Goals (SDGs), where no one gets left behind.

Toward automated irrigation management with integrated crop water stress index and spatial soil water balance

AbstractDecision support systems intended for precision irrigation aim at reducing irrigation applications while optimizing crop yield to achieve maximum crop water productivity (CWP). These systems incorporate on-site sensor data, remote sensing inputs, and advanced algorithms with spatial and temporal characteristics to compute precise crop water needs. The availability of variable rate irrigation (VRI) systems enables irrigation applications at a sub-field scale. The combination of an appropriate VRI system along with a precise decision support system would be ideal for improved CWP. The objective of this study was to compare and evaluate two decision support systems in terms of seasonal applied irrigation, crop yield, and CWP. This study implemented the Spatial EvapoTranspiration Modeling Interface (SETMI) model and the Irrigation Scheduling Supervisory Control and Data Acquisition (ISSCADA) system for management of a center pivot irrigation system in a 58-ha maize-soybean field during the 2020 and 2021 growing seasons. The irrigation scheduling methods included: ISSCADA plant feedback, ISSCADA hybrid, common practice, and SETMI. These methods were applied at irrigation levels of 0, 50, 100, and 150% of the full irrigation prescribed by the respective irrigation scheduling method. Data from infrared thermometers (IRTs), soil water sensors, weather stations, and satellites were used in the irrigation methods. Mean seasonal irrigation prescribed was different among the irrigation levels and methods for the 2 years. The ISSCADA plant feedback prescribed the least irrigation among the methods for majority of the cases. The common practice prescribed the largest seasonal irrigation depth among the methods for three crop-year cases. The maize yield in rainfed was found to be significantly lower than the irrigated levels in 2020 since 2020 was a dry year. No significant differences were observed in crop yield among the different irrigation methods for both years. The CWP among the different irrigation methods ranged between 2.72 and 3.15 kg m−3 for 2020 maize, 1.03 and 1.13 kg m−3 for 2020 soybean, 3.57 and 4.24 kg m−3 for 2021 maize, and 1.19 and 1.48 kg m−3 for 2021 soybean. Deficit level (50%) had the largest irrigation water productivity in all crop-year cases in this study. The ISSCADA and SETMI systems were found to reduce irrigation applications as compared to the common practice while maintaining crop yield. This study was the first to implement the newly developed integrated crop water stress index (iCWSI) thresholds and the ISSCADA system for site-specific irrigation of maize and soybean in Nebraska.

Automated analysis of snowmelt from Sentinel-2 imagery to determine variable rate irrigation zones in the American Mountain West

Variable rate irrigation (VRI) is used to save water whilst maintaining crop yields in semiarid regions. A key problem is to be able to inexpensively determine spatial patterns in soil moisture so that VRI zones can be defined. In Southern Idaho, USA, the annual precipitation is low and most fall as winter snow. This research investigates whether snow melt patterns measured using freely available time-series Sentinel 2 imagery from Google Earth Engine can define useful VRI zones for two arable fields (Grace and Rexburg). The normalized difference snow index (NDSI) was computed for each 10 m pixel with snow for all winter images of the fields for 2018–2022. NDSI values were ranked within each image and average ranks were calculated for each month and over several years. The patterns of March NDSI were most similar to patterns in yield and soil moisture observed in previous years. Zones were determined using K-means classification of the mean ranks of March NDSI. Kruskal Wallis H tests showed consistent and significant differences between zones for key soil, plant, and topographic variables. For the Grace site, differences between zones were more consistent in their order of magnitude than VRI zones which were calculated using a labor-intensive method. For the Rexburg site, zones were shown to be better when based on snowmelt data from March 2018 to 2022 rather than just March 2019. It is important to base zones on several years of data because in some years there was no snow observed in the Grace field in March. In locations where the majority of soil moisture comes from snowmelt, basing VRI zones on several years of snowmelt patterns in March is a useful and inexpensive tool for deriving meaningful VRI zones. The code used to automatically extract suitable sentinel images and calculate the NDSI is included so that practitioners can use this approach in other locations.

Evaluation of new pivoting linear‐move precision irrigation machine

AbstractDue to population growth, freshwater resources around the world are becoming increasingly scarce, and the water supply in agriculture has emerged as one of the limitations of food production. Variable‐rate irrigation (VRI), a type of precision irrigation, allows water‐efficient irrigation techniques to ensure an optimal water supply. The University of Debrecen, in collaboration with Magtár Kft., was the first in Hungary to develop a new laterally mobile irrigation machine equipped with VRI. The subject of our study was the testing of this system. According to the research, high and homogeneous irrigation uniformity was achieved in practice, with a Christiansen uniformity coefficient (CUc%) of 93 ± 2, distribution uniformity (DU%) of 88 ± 2 and coefficient of variation (CV) of 9 ± 2. Irrigation accuracy was also found to be satisfactory (mean absolute error 0.6 ± 0.1, mean bias error 0.2 ± 0.2, normalized root mean square error 8.6 ± 2), and only 1.4% ± 2% was overirrigated and 0.4% ± 0.3% underirrigated. In addition, the uniformity and accuracy of irrigation in different management zones along the pipeline were also investigated, and significant differences (p &lt; 0.05) were found between irrigation water depths. Based on the above, a new laterally mobile irrigation machine equipped with VRI can be used to develop more uniform and accurate irrigation schedules in the future in arable fields as this is critical for water‐saving irrigation management.

Assessing Within-Field Variation in Alfalfa Leaf Area Index Using UAV Visible Vegetation Indices

This study examines the use of leaf area index (LAI) to inform variable-rate irrigation (VRI) for irrigated alfalfa (Medicago sativa). LAI is useful for predicting zone-specific evapotranspiration (ETc). One approach toward estimating LAI is to utilize the relationship between LAI and visible vegetation indices (VVIs) using unmanned aerial vehicle (UAV) imagery. This research has three objectives: (1) to measure and describe the within-field variation in LAI and canopy height for an irrigated alfalfa field, (2) to evaluate the relationships between the alfalfa LAI and various VVIs with and without field average canopy height, and (3) to use UAV images and field average canopy height to describe the within-field variation in LAI and the potential application to VRI. The study was conducted in 2021–2022 in Rexburg, Idaho. Over the course of the study, the measured LAI varied from 0.23 m2 m−2 to 11.28 m2 m−2 and canopy height varied from 6 cm to 65 cm. There was strong spatial clustering in the measured LAI but the spatial patterns were dynamic between dates. Among eleven VVIs evaluated, the four that combined green and red wavelengths but excluded blue wavelengths showed the most promise. For all VVIs, adding average canopy height to multiple linear regression improved LAI prediction. The regression model using the modified green–red vegetation index (MGRVI) and canopy height (R2 = 0.93) was applied to describe the spatial variation in the LAI among VRI zones. There were significant (p &lt; 0.05) but not practical differences (&lt;15%) between pre-defined zones. UAV imagery coupled with field average canopy height can be a useful tool for predicting LAI in alfalfa.

Irrigation Zone Delineation and Management with a Field-Scale Variable Rate Irrigation System in Winter Wheat

Understanding spatial and temporal dynamics of soil water within fields is critical for effective variable rate irrigation (VRI) management. The objectives of this study were to develop VRI zones, manage irrigation rates within VRI zones, and examine temporal differences in soil volumetric water content (VWC) from irrigation events via soil sensors across zones. Five irrigation zones were delineated after two years (2016 and 2017) of yield and evapotranspiration (ET) data collection. Soil sensors were placed within each zone to give real time data of VWC values and assist in irrigation decisions within a 23 ha field of winter wheat (Triticum aestivum ‘UI Magic’) near Grace, Idaho, USA (2019). Cumulative irrigation rates among zones ranged from 236 to 298 mm. Although a statistical comparison could not be made, the irrigation rates were 0.6 to 21% less than an estimated uniform grower standard practice (GSP) irrigation approach. Based on soil sensor data, crop water stress was avoided with VRI management in all but Zone 3. Thus, this simple approach to VRI zone delineation and VWC monitoring has the potential to reduce irrigation, such as this study, on average by 12% and should be evaluated in other site-years to assess its viability.