Tool For Irrigation Scheduling Research Articles

Coupled weather and crop simulation modeling for smart irrigation planning: a review

ABSTRACT Efficient irrigation scheduling is essential for optimizing crop yields and water-use efficiency. This review examines crop simulation models and methods for improving irrigation management, with a focus on integrating weather forecast data. The FAO (Food and Agriculture Organization) developed models such as AquaCrop, WOFOST (WOrld FOod Studies), DSSAT (Decision Support System for Agrotechnology Transfer), and APSIM (Agricultural Production Systems sIMulator), exploring the incorporation of forecasted ETo (reference evapotranspiration) calculated based on forecasted values of weather through the Penman-Monteith method and rainfall data into the models using modified rule-based approaches with various forecast horizons, which enhances irrigation planning. Optimization methods, including genetic algorithms coupled with crop models, are also assessed and have shown significant water savings and profit gains compared with traditional farming practices. Emerging real-time irrigation scheduling tools, including simulation-optimization, field data assimilation, and human–machine interactions, further improve productivity and water conservation. Studies have also shown that web-based decision support using satellite remote sensing and crop models can be used to effectively monitor crop water status and predict real-time irrigation needs. Ongoing innovations like coupling crop models with optimization techniques, weather forecasting, remote sensing, and recommendations based on field experiments have shown promise for transforming irrigation planning and management.

Real‐Time Irrigation Scheduling Based on Weather Forecasts, Field Observations, and Human‐Machine Interactions

AbstractReal‐time irrigation schedules have been shown to outperform predetermined irrigation schedules that do not consider the present state and requirements. However, implementing real‐time irrigation scheduling requires reliable present soil‐crop‐atmosphere dynamics and weather predictions; moreover, enabling farmers to adopt recommended water applications remains challenging as they rely on personal experience and knowledge. Farmers and computer‐based tools are rarely connected in a closed‐loop and farmers' feedback are usually not incorporated into a real‐time modeling procedure. To resolve these critical issues, this paper addresses the feasibility of a real‐time irrigation scheduling tool (RTIST) based on weather forecasts, field observations, and human‐machine interactions. RTIST integrates a simulation & optimization model, a data assimilation (DA) technique, and a human‐computer interaction method, and enables optimality, accuracy, and applicability of the tool. The principle of the RTIST is to engage farmers directly into computer modeling, and support irrigation scheduling decisions jointly based on model provided information and farmers' own justification. The optimization and simulation are validated by running the tool on two crop fields, showing the accuracy of present estimation and future prediction of soil moisture and leaf area index, taking advantage of field observation and DA. The applicability of RTIST is tested via virtual irrigation exercises with a group of farmers for a corn field in Eastern Nebraska. RTIST with farmers' direct engagement shows increased productivity in comparison to traditional practices. Especially, farmers' feedbacks show interest in using the tool in real‐world irrigation scheduling and providing meaningful suggestions to improve the tool for real‐world application.

In-field assessment of the variability in water and nutrient use efficiency among potato farmers in a semi-arid climate

Precise water management is imperative for sustainable crop production under irrigation in semi-arid regions with increasingly variable rainfall. Water use efficiencies achieved by farmers often differ widely, even within regions with homogenous agro-ecological conditions. To better understand the drivers of this variability and derive management recommendations, detailed measurements of water and associated nutrient dynamics were conducted on 20 potato farms in two regions of South Africa. Crop characteristics and typical growing conditions in South Africa make potato cultivation sensitive to water and nutrient losses. Water and nutrient inputs and losses through drainage and plant uptake were measured in potato and the follow-up crop, along with yield and weather variables. Seasonal mean irrigation and evapotranspiration were lower in winter crops (240 mm irrigation, 236 mm evapotranspiration) than in summer crops (598 mm irrigation, 608 mm evapotranspiration). Seasonal field water balances were often close to zero, indicating that estimations of evapotranspiration and drainage were representative for the greater field. Lysimeter observations however appeared more representative in sandy unstructured soils than in heavier structured soils. Rainfall during cropping reduced demand for irrigation and increased water use efficiency based on irrigation water, but caused substantial drainage. Leaching of N and K was considerable (on average 30 kg N, 55 kg K ha−1, much higher in some fields), while P leaching was negligible, with the exception of one field (51 kg P ha−1). Variation in water and nutrients use efficiencies was the result of management decisions and environmental factors, such as rainfall and soil type. Without substantial rainfall, application of irrigation scheduling tools can keep drainage and nutrient losses to a minimum, as was observed on almost half the fields, even on sandy soils with a low water holding capacity. Potato yields greatly varied among farmers (9.1–24.7 t dry matter tuber ha−1). Efficient farmers included those who used water sparsely and had minimal drainage, but also farmers who used inputs judiciously and obtained excellent productivity. The role of follow-up crops as users of surplus nutrients and water left behind by the potato crop was not always evident.

Development of a soil moisture sensor‐based irrigation scheduling program for the midsouthern United States

AbstractThere is limited adoption of irrigation scheduling tools that could improve application timing and water use efficiency in row‐crop production systems common to the mid‐southern United States. The objectives of this manuscript are to describe a sensor‐based irrigation scheduling method and review its effects on water applied and crop productivity. The effects of scheduling irrigation based on the recommended construction, deployment, and utilization of the WATERMARK 200SS granular matrix (WATERMARK) sensor on water applied, crop productivity, and crop water use efficiency were reviewed for corn (Zea mays L.), soybean [Glycine max L. (Merr.)], peanut (Arachis hypogaea L.), and cotton (Gossypium hirsutum L.) produced in the Prairie region of Arkansas and the Delta regions of Arkansas and Mississippi. For corn and soybean, on‐farm research indicates the recommended irrigation threshold of −85 to −100 cbar reduces total water applied up to 40% while maintaining or improving yield up to 3%, net returns up to $39 acre−1, and irrigation water use efficiency up to 51% for soil textures ranging from very fine sandy loam to clay. Similarly, for peanut and cotton, results indicate the irrigation threshold that minimizes water use while maximizing yield and net returns is −50 cbar and −100 cbar, respectively. The recommended method for scheduling irrigations with a WATERMARK 200SS soil moisture sensor promotes the efficient use of water in row‐crop production systems common to the mid‐southern USA.

Evaluation of artificial intelligence algorithms with sensor data assimilation in estimating crop evapotranspiration and crop water stress index for irrigation water management

Irrigation water management using automated irrigation decision support system (IDSS) as a smart irrigation scheduling tool can improve water use efficiency and crop production, especially under circumstances of limited water supply. The current study evaluated the performance of different artificial intelligence (AI) algorithms and their ensembles in forecasting Crop Evapotranspiration (ETc) and Crop Water Stress Index (CWSI) against calculated single crop coefficient FAO56 ETc and Jackson's theoretical CWSI, respectively. Soil moisture, canopy temperatures (Tc) and Normalized Difference Vegetation Index (NDVI) were all measured from irrigated and non-irrigated maize plots in West Central Nebraska during 2020 and 2021 growing seasons. There were fifteen and twelve input combinations used for ETc and CWSI predictions, respectively, having input variables such as weather and soil moisture as well as ancillary variables, including NDVI, reference evapotranspiration (ETr), and cumulative growing degree days (CGDDs). While evaluating the models, four statistical performance indicators including coefficient of determination (r2), root mean square error (RMSE), mean absoluter error (MAE), and mean absolute percentage error (MAPE) were used. Furthermore, ranking scores were performed on statistical results to find the overall best model across all the input combinations. Based on total ranking scores, CatBoost (RMSE ranging between 0.06 – 0.09 unitless) was the best model in predicting CWSI, while Stacked Regression (RMSE ranging between 0.27 – 0.72 mm d−1) was the best model for ETc estimation. Future research will consider designing and evaluating an IDSS using identified best machine learning models to establish soil water and plant stress feedback for automated irrigation scheduling.

Effects of Soil Data Accuracy on Outputs of Irrigation Scheduling Tools

Highlights The effects of soil data accuracy on estimated water fluxes by an irrigation scheduling model were investigated. Free and frequently used web soil survey (WSS) soil textural data underestimated sand particles in 89% of cases. Forty-nine percent of the estimated differences in seasonal irrigation based on WSS and measured soil data were within ±25 mm. In most cases, use of WSS data resulted in larger evaporation, smaller deep percolation, and larger runoff compared to those based on measured soil data. Abstract. A widely used irrigation scheduling method is based on modeling soil water balance, which requires several key inputs, including soil data. Many scheduling tools developed using this method rely on publicly available soil data, such as the United States Department of Agriculture's Web Soil Survey (WSS). While soil survey data are a valuable source of information for general farm and natural resource planning and management at large scales, inaccuracies in soil conditions at field and subfield scales can hamper efficient agricultural water management through irrigation scheduling tools. To illuminate the implications of the localized inaccuracies, this study estimated the errors in WSS soil textural data at 18 sites in three regions of western Oklahoma through comparison with in-situ sampling (ISS) data. The effects of errors on estimated water fluxes were also investigated for dominant crops of each region over a 15-year (2006-2020) period. The findings demonstrated that WSS soil textures were finer than ISS at most sites and soil layers, resulting in generally greater root zone total available water estimates. Differences in seasonal irrigation demand estimates when WSS data were used instead of ISS reached 20% at one site but were within ±9% among the regions. Half of the estimated seasonal irrigation differences for all sites, years, and crops were within ±25 mm. Soil evaporation, deep percolation, and runoff fluxes were also impacted by soil data source, albeit to a smaller degree than irrigation, at levels and directions (over or underestimation) that were dependent on the sign and magnitude of WSS errors, as well as precipitation amounts and timing. Overall, errors in WSS data may not have a major impact at regional scales, but the effects on individual irrigated farms may be severe depending on the magnitude of difference between WSS data and true soil conditions. Keywords: Irrigation demand, Soil water balance, SSURGO, Water fluxes, Web soil survey.

Forecasting of Canopy Temperatures Using Machine Learning Algorithms

Highlights This study analyzes the feasibility of using Artificial Neural Networks (ANNs) to estimate canopy temperatures. A methodology is introduced to forecast canopy temperatures using historical canopy temperatures. ANNs can predict canopy temperatures with satisfactory accuracy for plant stress-based irrigation scheduling. The methodology can be useful to add redundancy to plant stress-based irrigation scheduling methods. Abstract. Recent advances can provide farmers with irrigation scheduling tools based on crop stress indicators to assist in the management of Variable Rate Irrigation (VRI) center pivot systems. These tools were integrated into an Irrigation Scheduling Supervisory Control and Data Acquisition System (ISSCADAS) developed by scientists with the USDA-Agricultural Research Service (ARS). The ISSCADAS automates the collection of data from a network of wireless infrared thermometers (IRTs) distributed on a center pivot’s lateral and in the field irrigated by the center pivot, as well as data from a wireless soil water sensor network and a microclimate weather station. This study analyzes the use of Artificial Neural Networks (ANNs), a type of machine learning algorithm, for the forecasting of canopy temperatures obtained by a wireless network of IRTs mounted on a three-span VRI center pivot irrigating corn near Bushland, TX, during the summer of 2017. Among the predictors used by the ANNs were weather variables relevant to the estimation of evapotranspiration (i.e., air temperature, relative humidity, solar irradiance, and wind speed), irrigation management variables (irrigation treatment, irrigation scheduling method, and the amount of water received during the last 5 days as irrigation or rainfall), and days after planting. Two case studies were conducted using data collected from periodic scans of the field performed during the growing season by running the pivot dry. In the first case, data from the first three scans were used to train an ANN, and canopy temperatures estimated using the ANN were then compared against canopy temperatures measured by the network of IRTs during the fourth scan. In the second case, data from the first six scans were used to train ANNs, and canopy temperatures estimated using the ANN were then compared against canopy temperatures measured by the network of IRTs during the seventh scan. The Root of the Mean Squared Error (RMSE) of ANN predictions in the first case ranged from 1.04°C to 2.49°C, whereas the RMSE of ANN predictions in the second case ranged from 2.14°C to 2.77°C. To assess the impact of ANN accuracy on irrigation management, estimated canopy temperatures were fed to a plant-stress-based irrigation scheduling method, and the resulting prescription maps were compared against prescription maps obtained by the same method using the canopy temperatures measured by the network of IRTs. In the first case, no difference was found between both prescription maps. In the second case, only one plot (out of 26) was assigned a different prescription. Results of this study suggest that machine learning techniques can be used to assist the ISSCADAS in situations where canopy temperatures cannot be measured by the network of IRTs due to poor visibility conditions, or because the center pivot cannot traverse the field within a reasonable amount of time. Keywords: Artificial Neural Network, Canopy temperature sensing, Center pivot irrigation, Irrigation scheduling, Machine learning, Metamodeling, Variable rate irrigation.

Information constraint and farmers’ willingness to pay for an irrigation scheduling tool

Sub-Saharan Africa (SSA) has witnessed increasing water scarcity and demand for additional water resources due to climate change. However, lack of information and access to irrigation scheduling decision support tools may limit smallholder farmers’ ability to manage irrigation water efficiently for sustainable crop production. To overcome the complex nature associated with irrigation scheduling, simple low-cost, and user-friendly soil water monitoring tools such as the Wetting Front Detector (WFD) have been developed but the commercial viability of these tools relies on effective demand for the services. This paper assesses how information on the Wetting Front Detector influences farmers’ willingness to pay (WTP). We find firstly that about 57% of the farmers have information about the WFD and 98% reported a WTP higher than zero. Secondly, farmers are highly price sensitive, but most are willing to pay a small amount for the WFD. Thirdly, irrigation information increases farmers’ WTP for WFD by US$6 after controlling for variations existing among irrigation communities. A heterogeneity analysis indicates that WTP is higher for women, youth, and high-income earners than men, adults, and low-income earners. The results imply that increasing initial awareness and providing price subsidies for the new irrigation scheduling tool will increase future adoption.

Developing an Automated Python Surface Energy Balance System (PySEBS) Software for Calculating Actual Evapotranspiration-Software Development and Application Case in Jilin Province, China

In this study, Python Surface Energy Balance System (PySEBS) software was developed in the Python 2.7 programming language for continuous calculation of actual evapotranspiration (ETa) at regional scales. The software is based on the Surface Energy Balance System (SEBS) model, which uses basic meteorological data, MODIS remote sensing data, and Digital Elevation Model (DEM) data as the original input data and finally outputs daily-scale ETa in the form of raster data with a spatial resolution of 1 km × 1 km. To verify the reliability of the PySEBS model, the ETa of spring maize during the growing season in Jilin Province, China, from 2001 to 2020 was calculated and analyzed in this study and compared with the results of similar studies by others. The findings showed that the PySEBS model has a reasonable accuracy in estimating ETa within ±15% and is a robust model that can achieve the continuous calculation of ETa at a regional scale. Therefore, PySEBS software is a useful tool for regional irrigation scheduling and water resources management.

Crop coefficients and water use of young almond orchards

An observational study was conducted in the northern Sacramento Valley in California, United States to determine crop water use and crop coefficients of three adjacent young Nonpareil/Monterey almond orchards. Methods used to quantify evapotranspiration estimates of crop water use include (1) a soil water balance and (2) a land surface energy balance using eddy covariance. Three adjacent almond orchards that were planted in 2016, 2017, and 2018 were monitored from 2018 to 2020. Actual crop coefficients were determined using actual evapotranspiration estimates from each orchard and short grass reference evapotranspiration from the Gerber South California Irrigation Management Information System station. Results showed that crop water use and crop coefficients increased until the 4th year, indicating the need to closely consider tree development and orchard age as factors in irrigation scheduling of young almond trees. The results led to the conclusion that farmers should use development or age-specific crop coefficients in developing orchards for irrigation scheduling until the 4th year when they can start using mature almond crop coefficients. The mid-season actual crop coefficients were 0.35, 0.55, 0.88, 1.04, and 0.99 for 1-, 2-, 3-, 4-, and 5-year-old almond orchards. This study has generated baseline data on crop water requirements of young almond orchards that could be useful for (1) developing irrigation scheduling tools for young almond orchards, and (2) determining water budgets for areas with new almond orchards.

Development of a new UAV-thermal imaging based model for estimating pecan evapotranspiration

• A new modified model was proposed for calculating pecan water consumption using canopy temperature. • The proposed modified model was calibrated and validated using field measured data. • A good agreement was observed between estimated pecan water consumption and applied water based on ET a . • Utility of proposed model for estimating monthly pecan water use was confirmed. • There is potential for application of the proposed model in other locations with various crops. Pecans are a specialty crop in New Mexico’s Lower Rio Grande Valley (LRGV), a region that produces around 30% of pecans in USA. Pecans are also a major water consumer, requiring 1200–1300 mm depth for maximum yield in this region. The combination of prolonged drought and increasing competition for water among various water consumers has created an urgency for the efficient use of scarce water resources in the LRGV. More efficient water management through the real-time irrigation scheduling is one method to promote reduced water application in agriculture. This study was conducted to calibrate and validate a new modified model for estimating the pecan actual evapotranspiration (ET a ) based on canopy temperature using thermal images taken from an Unmanned Aerial Vehicle (UAV) during three growing seasons in a drip irrigated pecan orchard. A capacity to estimate the relation between ET a and canopy temperature provides an important information to guide water management choices. The Simplified Surface Energy Balance (SSEBop) model was modified and used for calibration and validation. Applied irrigation water based on ET a was used to calibrate and validate the proposed modified model. The scaling factor of K in the SSEBop model was calculated as 0.75 through the calibration process. Findings showed a good agreement between estimated pecan ET a using modified SSEBop model and applied water based on ET a during calibration (R 2 = 0.72, RMSE = 0.6 mm/d, MAE = 0.48 mm/d) and validation period (R 2 = 0.90, RMSE = 0.24 mm/d, MAE = 0.22 mm/d). Also, findings confirmed the utility of modified model for estimating monthly pecan ET a (RMSE = 8.87 mm/month, MAE = 6.55 mm/month). The proposed modified model provides pecan farmers with a simple real-time irrigation scheduling tool where they can better practice precision irrigation. Although the modified model was calibrated and validated for irrigation scheduling in the LRGV, it has potential to see application for other locations with different crops using similar calibration approach.

Making the invisible visible: Co-learning guided development of an operational tool for irrigation management

Inefficient use of irrigation in New Zealand pastoral farms is widespread and the uptake of irrigation scheduling tools to improve efficiency is poor. Recent studies suggest that end-user inclusive participatory approaches to developing tools and practices could improve the uptake, contributing to improved economic and environmental outcomes. Using a co-learning based participatory approach, we (researchers) worked with a group of pastoral farmers, water resources regulators and industry professionals, to develop an operational irrigation scheduling support tool that would facilitate improved water use. The key engagement principles used were: taking time to understand the problem from multiple (stakeholder) perspectives; applying equal value to all sources of knowledge; providing an atmosphere that fosters learning among stakeholders (co-learning); staying aware of the wider (problem) context; and remaining flexible and adaptable. The co-learning approach revealed over-lapping, yet distinct, perceptions of improved water use: for farmers, it meant pasture growth unimpeded by available soil water; for regulators, reduced irrigation-drainage and consequent leaching of nutrients from rootzone; and for researchers, justified use of water based current (crop) demand and forecast (rainfall) supply. The co-learning guided operational tool combined near-real time soil water monitoring (demand) and short-term (two to six days) rainfall forecast (supply), to support on-farm irrigation scheduling decisions. The tool included biophysical (data type and synthesis), structural (data format, presentation and visualisation) and technological (instrumentation and data collection protocols) features that incorporated the diverse perspectives to water use. Uptake of the tool by the pilot study farmers was high, and their irrigation practices evolved because of the information it provided. Inclusion of multiple stakeholder perspectives throughout the tool development process revealed unexpected insights and resulted in improved outcomes for all stakeholders.

On-line monitoring of plant water status: Validation of a novel sensor based on photon attenuation of radiation through the leaf

Non-destructive real-time monitoring of leaf water status is important for precision irrigation practice to increase water productivity and reduce its use. To this end, we tested and validated a novel leaf sensor (Leaf Water Meter, LWM), based on the photon attenuation during the passage of the light through the leaf, to monitor plant water status. Four woody species were subjected to multiple cycles of dehydration and re-hydration, and the signals recorded by the LWM were compared with classical measurements of plant water relations (relative water content and water potential). A good agreement between the signals recorded by LWM sensor and the destructive measurements, throughout the repeated water stress and rewatering cycles, was found across all species. These results demonstrate that LWM sensor is a sensitive, non-destructive and easy-to-handle device to reliably monitor in continuous fashion leaf water status. In conclusion, this sensor may be considered a promising tool for smart irrigation scheduling in precision agriculture context to decrease water wastage in light of global change and increasing conflicts over water demand.

Simulating Soil Water Status of Irrigated Fields: The Effects of Soil Data and Root Water Uptake Distribution

<b>Highlights</b> <list list-type=bullet><list-item> The effects of variability in two key inputs to irrigation scheduling models were investigated. </list-item><list-item> Commonly used soil textural data underestimated sand particles, resulting in larger errors in simulated water content. </list-item><list-item> Sensor-based root water uptake distribution led to better model performance than constant and linear patterns. </list-item><list-item> Irrigation timing and amount variables were more impacted by variability in soil data than water uptake distributions. </list-item></list> <b>Abstract</b>. With recent advances in web-based irrigation scheduling tools and mobile applications and the possibility of using more complex modeling approaches, it is important to evaluate the effects of variable input data on the output of these tools and models. Two types of input data that are highly variable across irrigated fields and soil profiles are soil textural data and root water uptake distribution (RWUD). In this study, root zone soil textural data from two sources of commonly used, freely available web soil survey (WSS) and time-consuming, labor-intensive in-situ sampling (ISS) were used in combination with three RWUDs (constant, linear, and sensor-based) to simulate volumetric water content (θv) at four soil layers in six irrigated fields, using the HYDRUS model. The percentage of sand particles based on WSS was about half of the measured amount on average, resulting in a considerable difference in estimated hydraulic properties and soil water thresholds. Sensor data revealed that RWUDs were highly nonuniform, with more than 60% of water extraction occurring from the top 30 cm of the root zone. Among the six combinations of two sources of soil data and three RWUDs, ISS-sensor resulted in the smallest errors in simulated θv, and WSS-constant yielded the largest errors. Simulated θv data were translated to actionable end-user variables of irrigation trigger (IT) and soil water depletion (SWD), which determine the timing and the amount of irrigation applications, respectively. Relying on WSS resulted in irrigation trigger being called about four times more than when measured soil data were used. The average SWD based on WSS was 157 mm, about two times larger than the average SWD based on ISS (68 mm).

Identifying barriers to adoption of irrigation scheduling tools in Rio Grande Basin

Irrigated agriculture in the Rio Grande region faces water management related challenges due to climate variability and rise in non-agricultural water demand. Scientific irrigation scheduling (SIS) tools allow growers to optimize the water use and conserve water by making informed decisions. Nevertheless, multiple technological and economic barriers could slow down the adoption of these technologies. This study investigates the barriers to adoption of SIS methods in the U.S. part of Rio Grande basin by getting irrigators’ perspective and outlines the factors that influence adoption. Multiple adoption barriers are listed, and the most important ones are lack of access to weather data, uncertainty about future water availability, cost effectiveness of technologies, reliability of weather data, lack of availability of irrigation scheduling tools, and risk of reduced yield. Factors that influence the growers’ decision to adopt SIS are also explored, which are quality of land, yield, water use efficiency, and water availability for future generations. Age, education, and years involved in agriculture may also govern the knowledge and adoption of SIS methods. The results of this study provide guidance to policy makers and extension experts to strengthen water conservation efforts in Rio Grande basin and other comparable regions in the world.

Detecting crop water requirement indicators in irrigated agroecosystems from soil water content profiles: An application for a citrus orchard

Most perennial crops sensitive to water scarcity, such as citrus, can benefit from efficient water management, which allows for reduced water consumption while increasing crop production on a long-term basis. However, when implementing water-saving strategies, it is necessary to monitor soil and/or plant water status in order to determine crop water demand. A plethora of devices providing indirect measurements of volumetric soil water content, such as the “drill and drop” multi-sensors probes (Sentek, Inc., Stepney, Australia), have been developed over the last decade.The objective of the paper was to analyse time-series of soil water content profiles and meteorological data collected in an adult citrus orchard over three years of field observations (2017–2020) in order to estimate actual crop evapotranspiration and derive crop coefficients. Simultaneous measurements of sap fluxes also allowed for the estimation of the basal crop coefficient, Kcb, used as a control variable.The temporal dynamics of soil water content profiles following rainfall or irrigation events provided information on soil evaporation, root water uptake, and actual crop transpiration. After soil wetting events, in particular, it was possible to recognize patterns of actual crop evapotranspiration similar to those detected with sap flow sensors. The knowledge of actual crop evapotranspiration at the daily time-step, in conjunction with the corresponding reference crop evapotranspiration, allowed for appropriate estimations of the crop coefficient associated with the various development stages. The proposed method provided interesting insights into the dynamics of root water uptake and crop evapotranspiration of the studied citrus orchard, and it represents a promising tool for precise irrigation scheduling in other agroecosystems.

Optimizing Irrigation and Young Tree Management

Young citrus trees require optimal irrigation management for vigorous vegetative growth, leaf flushing, and establishment of a dense canopy. Poor irrigation practices, such as infrequent irrigation or irrigating without using irrigation scheduling tools, could be costly. Besides excessive loss of water and nutrients, the growth of young trees might be impacted by an excess or deficit of water. Crop water stress in young trees directly affects yield and fruit quality. This new 2-page publication of the UF/IFAS Department of Soil and Water Sciences, written by Davie Kadyampakeni and Sandra Guzmán, covers some strategies for optimizing young tree care and irrigation management to in turn optimize grove efficiency and productivity.https://edis.ifas.ufl.edu/ss701

Identification of water stress conditions in olive trees through frequencies of trunk growth rate

Continuous monitoring of the tree water status will enhance irrigation performance, particularly when applying deficit schedules. The olive tree is a highly drought-resistant species and management of the water stress could increase water savings. Trunk diameter fluctuations can be displayed as daily curves representing the shrinkage and swelling, and can provide information about tree water status. In olive trees, trunk growth rate (TGR) is the most useful indicator, but the daily variability reduced the commercial applications. Recently, weekly frequencies of TGR values were associated to the water status in one seasonal experiment. The aim of this work is to study the seasonal pattern and the interannual variations of these parameters in order to integrate them in an irrigation scheduling tool. The experiment was performed during two consecutive seasons (2018 and 2019) in a superhigh density mature olive orchard at Carmona (Seville, Spain). Three different irrigation scheduling treatments were considered in a randomized complete block design. The control treatment was fully irrigated with 150–175% crop evapotranspiration (ETc) in order to ensure an optimum water status. Regulated deficit irrigation-1 (RDI-1) was scheduled using only TGR data provided through the continuous measurements from a dendrometer. In this treatment, water stress conditions were controlled during the pit hardening period. RDI-2 was similar to RDI-1, but with a more severe water stress conditions during pit hardening and a maximum seasonal amount of water that limited rehydration. Water stress was greater during the 2019 season than the 2018 season, according to the midday stem water potential (SWP). Weekly frequencies of TGR values lower than −0.3 mm day−1 (Severe FR) and values between −0.1 and 0.3 mm day−1 (Good FR) described the water status pattern in the three treatments for both seasons. Only under severe water stress conditions (SWP more negative than −4 MPa) the values of these frequencies did not identify accurately the water status. However, the use of weekly frequencies of values greater than 0.3 mm day−1 (Alert FR) and the pattern of these Severe FR and Good FR themselves identified such conditions. The use of these three weekly frequencies (Severe, Good and Alert (SGA) approach) are suggested for continuous deficit irrigation scheduling in olive trees.

Long Short-Term Memory Neural Network for irrigation management: a case study from Southern Alabama, USA

The metabolism and growth of vegetation are highly dependent on the changes in soil water content. Irrigation scheduling and application of water at the right time and rate are a key aspect for precision irrigation. In this study, the Long Short-Term Memory (LSTM) Neural Network model was studied to predict irrigation prescriptions for 1, 3, 6, 12 and 24 h in advance. Training data for LSTM were collected from a precision irrigation study conducted in Alabama, USA. The prediction estimation of irrigation prescription used soil matric potential data measured within two contrasting soil types. Performance of the LSTM models were evaluated by comparing neural network parameters and prediction capability by soil type. The optimal learning algorithm for each case was also determined. The LSTM Neural Network showed good prediction capabilities for both soil types, with $$R^{2}$$ ranging between 0.82 and 0.98 for one hour ahead prescription and getting smaller as prediction time increases. The irrigation rate prediction was verified by actual observations that demonstrate the suitability of the machine learning technique as a decision-support tool for irrigation scheduling.

A Decade of Unmanned Aerial Systems in Irrigated Agriculture in the Western U.S.

Highlights Unmanned aerial systems (UAS) are able to provide data for precision irrigation management. Improvements are needed regarding UAS platforms, sensors, processing software, and regulations. Integration of multi-scale imagery into scientific irrigation scheduling tools are needed for technology adoption. Abstract . Several research institutes, laboratories, academic programs, and service companies around the United States have been developing programs to utilize small unmanned aerial systems (sUAS) as an instrument to improve the efficiency of in-field water and agronomical management. This article describes a decade of efforts on research and development efforts focused on UAS technologies and methodologies developed for irrigation management, including the evolution of aircraft and sensors in contrast to data from satellites. Federal Aviation Administration (FAA) regulations for UAS operation in agriculture have been synthesized along with proposed modifications to enhance UAS contributions to irrigated agriculture. Although it is feasible to use sUAS technology to produce maps of actual crop coefficients, actual crop evapotranspiration, and soil water deficits, for irrigation management, the technology and regulations need to evolve further to facilitate a successful wide adoption and application. Improvements and standards are needed in terms of cameras’ spectral (bands) ranges, radiometric resolutions and associated calibrations, fuel/power technology for longer missions, better imagery processing software, and easier FAA approval of higher altitudes flight missions among other issues. Furthermore, the sUAS technology would play a larger role in irrigated agriculture when integrating multi-scale data (sUAS, ground-based or proximal, satellite) and soil water sensors is addressed, including the need for advances on processing large amounts of data from multiple and different sources, and integration into scientific irrigation scheduling (SIS) systems for convenience of decision making. Desirable technological innovations, and features of the next generation of UAS platforms, sensors, software, and methods for irrigated agriculture, are discussed. Keywords: Agricultural water management, Irrigation prescription mapping, Irrigation scheduling, Precision irrigation, Remote sensing, Sensors, Spatial crop evaOotranspiration, Unmanned aerial systems.