Irrigation Scheduling Decisions Research Articles

Irrigation Scheduling Impacts Vegetative Growth, Seed Yield, and Fungal Diseases of Spinach Seed Crops in a Maritime Mediterranean Climate

Although irrigation scheduling has been studied for diverse vegetable crops, much less attention has been given to irrigation scheduling for the seed crops on which these production systems rely. In spinach, for which irrigation scheduling needs are likely to vary greatly between seed and leaf production, this leaves seed producers without adequate resources to make irrigation scheduling decisions. Our research sought to fill this gap by evaluating two alternative irrigation scheduling strategies (a publicly available decision-support tool and soil moisture sensors) and four soil moisture thresholds for irrigation for their impacts on vegetative growth, marketable seed yield, seed quality, and the severity of Stemphylium leaf spot (caused by Stemphylium vesicarium and Stemphylium beticola), a common foliar disease of spinach, under sprinkler irrigation. We found that in all 3 years of the study, earlier and more frequent irrigation increased vegetative growth. However, marketable seed yield only increased relative to the control treatment based on farmers’ standard irrigation practices in 1 of the 3 years—a year with an abnormally late planting date. This indicates that vegetative growth is more responsive than seed yield to earlier and more frequent irrigation, and that increases in vegetative growth do not translate directly to increased marketable seed yield. Contrary to the expected increase in Stemphylium leaf spot severity with increasing irrigation, the severity decreased in both years it was measured, likely as a result of the small stature of the spinach seed parent lines used in our study and opportunistic pathogenicity on moisture-stressed plants. These results provide a useful foundation from which spinach seed producers can make irrigation management decisions for their crops that underpin a valuable global industry.

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.

Data-driven irrigation scheduling increases the crop water use efficiency of Cabernet Sauvignon grapevines

In the context of water management in agriculture, irrigation scheduling is critically important as it optimises water application to crops and can also target specific production goals. However, there is no consensus on the ideal irrigation scheduling strategy regarding crop water use efficiency (WUEc). In a premium Cabernet Sauvignon vineyard in Coonawarra, South Australia, over three growing seasons, irrigation scheduling strategies based on experience or historical knowledge (‘GROW’ treatment) were compared to data-driven strategies including crop evapotranspiration, and plant and soil water status thresholds to evaluate their effects on leaf- and vine-level WUEs. A final treatment, GROW + , that doubled the GROW level of irrigation was also evaluated in the third season. The WUE metrics were determined at the leaf, vine, and fruit scales as intrinsic WUE (WUEi), crop WUE (WUEc), and carbon isotope ratio (δ13C), respectively. Furthermore, the irrigation strategies were evaluated in the background of two contrasting soil types: Terra Rossa (light clay, well-drained) and Rendzina (heavier clay, poorly drained). Seasonal soil and vine water status, leaf gas exchange, and light interception were measured, and yield components and pruning weights were obtained following harvest. The amount of seasonal irrigation water based on the data-driven strategies was up to 65% lower across both soil types compared with the GROW or GROW + approaches. WUEi and δ13C were largely similar between treatments. However, for vines grown on Terra Rossa soil, little to no yield penalty was observed when data-driven irrigation scheduling was applied, in addition to increased WUEc values of up to 41%. It can be concluded that irrigation scheduling decisions based on data were superior to the conventional irrigation scheduling method on account of reducing irrigation water volume and increasing WUE, particularly in Terra Rossa soils.

Data-Driven Modelling of Soil Moisture Dynamics for Smart Irrigation Scheduling

In the face of increasing water scarcity and uncertainties of climate change, improving crop water use efficiency and productivity, while minimizing negative environmental impacts, is becoming crucial to meet the surging global food demand. Smart irrigation has a potential of improving water use efficiency in precision agriculture especially when efficient irrigation control strategies are adopted. Conventionally, irrigation systems rely on heuristic methods to schedule irrigation which either leads to over-irrigation or under-irrigation. This influences the crop physiological characteristics as well as the water use efficiency. To tackle this menace, model-based irrigation management has been overemphasized. A closed-loop irrigation control strategy relies on a mathematical model of the system for irrigation scheduling decisions. In this study, a data-driven approach was used to learn soil moisture dynamics from a drip irrigated tomato in an open field agricultural system. A total number of 9674 data samples were collected using an ATMOS41 weather station, TERROS 12 soil moisture sensor and a YFS-201 flow sensor for crop evapotranspiration and precipitation, soil moisture and irrigation volumes respectively. Data driven modelling was then performed using the system identification toolbox in a MATLAB environment. The model formulation was a multi-input single-output (MISO) system with reference evapotranspiration, irrigation and rainfall as inputs and soil moisture as the output. Different model structures including transfer functions, state space models, polynomial models and ARX models were evaluated. Model performance was evaluated using the mean square error (MSE), final prediction error (FPE) and estimated fit of the model approaches. Simulation results indicate that the soil moisture dynamics model provides a satisfactory approximation of the process dynamics with a state space model giving an estimated fit of 97.04 %, MSE and FPE of 1.74×10−7 and 1.75×10−7 respectively. This model will be used to design a model predictive controller for smart irrigation scheduling in open field environmental conditions.

Daily reference evapotranspiration prediction for irrigation scheduling decisions based on the hybrid PSO-LSTM model.

The shortage of available water resources and climate change are major factors affecting agricultural irrigation. In order to improve the irrigation water use efficiency, it is necessary to predict the water requirements for crops in advance. Reference evapotranspiration (ETo) is a hypothetical standard reference crop evapotranspiration, many types of artificial intelligence models have been applied to predict ETo; However, there are still few in the literature regarding the application of hybrid models for deep learning model parameters optimization. This paper proposes two hybrid models based on particle swarm optimization (PSO) and long-short-term memory (LSTM) neural network, used to predict ETo at the four climate stations, Shaanxi province, China. These two hybrid models were trained using 40 years of historical data, and the PSO was used to optimize the hyperparameters in the LSTM network. We applied the optimized model to predict the daily ETo in 2019 under different datasets, the result showed that the optimized model has good prediction accuracy. The optimized hybrid models can help farmers and irrigation planners to make plan earlier and precisely, and can provide valuable information to improve tasks such as irrigation planning.

A crop water stress index based internet of things decision support system for precision irrigation of wine grape

The goal of irrigation for wine grape grown in arid or semiarid regions is to sustain vine survival and to optimize berry attributes for quality wine production. Precision irrigation of wine grape is impeded by the lack of a smart, decision support system (DSS) to remotely monitor vine water status. The objectives of this study were to: develop and field test an Internet of Things (IoT) DSS system for precision irrigation of wine grape. The IoT system was comprised of a suite of in situ sensors used to monitor real-time weather conditions, grapevine canopy temperature, soil moisture, and irrigation amount. Sensor data were collected and stored on a field deployed data logger that calculated a daily thermal Crop Water Stress Index (CWSI) for grapevine using a neural network model with real-time sensor data model inputs. The data logger also hosted, via a cellular modem, webpages showing a running, 12-day history of daily CWSI, fraction of available soil moisture (fASW), irrigation amount, and other sensor data. The webpages were accessible to vineyard managers via cell phone or computer. The CWSI based IoT DDS system was installed at two small acreage, commercial estate vineyards in southwestern Idaho USA over four growing seasons. At each vineyard site, the DSS was used daily by the vineyard irrigation manager to schedule irrigation events. Neither vineyard manager used any other quantitative vine water status monitoring tool for irrigation management decisions. The midday leaf water potential (LWP) of grapevines was routinely measured by research project personnel. Data collected over the study period at each vineyard showed a significant (p < 0.001) correlation with LWP and fASW, providing evidence that, under the conditions of this study, the daily CWSI based IoT provided automated, remote monitoring of vine water status. Both vineyard managers reported daily use of the DSS for irrigation scheduling decisions. Over the four-year study, each vineyard manager was able to maintain consistent seasonal average CWSI daily values and irrigation application amounts, despite yearly differences in climatic conditions. The results of this study demonstrate that a CWSI based IoT DSS can be used for precision irrigation of wine grape in a commercial vineyard under semiarid growing conditions. The CWSI based IoT DSS used a unique combination of neural network modeling, edge computing, and IoT for real-time vine water stress monitoring for precision irrigation.

Assessing Suitability of Irrigation Scheduling Decision Support Systems for Lowland Rice Farmers in Sub-Saharan Africa—A Review

Assessing Suitability of Irrigation Scheduling Decision Support Systems for Lowland Rice Farmers in Sub-Saharan Africa—A Review

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.

Two source energy balance maize evapotranspiration estimates using close-canopy mobile infrared sensors and upscaling methods under variable water stress conditions

Mobile infrared thermometers (IRTs) mounted on moving platforms provide one-time-of-day radiometric measurements (Tr), which can be used to calculate instantaneous actual evapotranspiration (ETa) using the two-source energy balance (TSEB) model. However, irrigation scheduling decisions utilize daily ETa estimates, hence the need for time scaling. This study evaluated different upscaling methods to calculate daily maize ETa using one-time-of day Tr under varying water stress conditions. Mobile IRTs were mounted on a high clearance mobile sensing platform and collected Tr in remote locations under full, deficit and rainfed conditions. Seven scaling methods via two pathways were employed to obtain daily ETa. First pathway was scaling one-time-of-day Tr (SC) whereas the second pathway involved use of six upscaling methods of instantaneous ETa including: original and modified evaporative factor ((EF)o, (EF)m) as well as crop coefficient ((Kc)o, (Kc)m), direct canopy resistance (Direct- rc), and solar radiation ratio (Rn/Rs); and all were compared to a neutron-based soil water balance (SWB) determined ETa. From the results, SC outperformed other methods in comparison to SWB ETa across all the selected treatments with smaller discrepancies and lower RMSE (0.9–1.7 mm d−1 vs. 0.7–4.3 mm d−1 for other methods). Furthermore, methods including SC, (EF)o, (EF)m, and Rn/Rs had their daily average ETa values in close agreement to SWB ETa with mean ETa differences ranging between 0.2 and 1.6 mm d−1. Overall, SC method performed better in fully irrigated maize (r2 = 0.52, RMSE = 0.9 mm d−1) than in deficit irrigated maize ( r2 = 0.48, RMSE = 1.4 mm d−1) but worst in rainfed maize (r2 = 0.16, RMSE = 1.7 mm d−1). This implies that SC is more suited for irrigated rather than rainfed settings. Importantly, the choice of any method depends on data requirements, irrigation water management strategy, and ETa estimation accuracy.

Concurrent data assimilation and model-based optimization of irrigation scheduling

Crop models can be combined with optimization algorithms in order to develop management tools. However, such model-based tools are inherently affected by the imperfectness of the model on which they are based. In this paper we describe a procedure in which data assimilation and partial re-parametrization of the model embedded in the optimization procedure used to determine irrigation scheduling are performed before each optimization run. Furthermore, sensitivity analysis is performed before performing data assimilation, which ensures that only influential parameters are adjusted. The procedure was tested via simulation with DSSAT-CROPGRO for a hypothetical processing tomato crop in Davis, CA, in 2010–2019. Several scenarios that differed in terms of the measurements assumed to be available for assimilation (leaf area index, biomass and/or soil water content) were simulated. The results were compared to a benchmark scenario involving a perfect model, as well as a scenario in which data assimilation was not performed. The analysis focused on the overall performance of the irrigation schedule (yield vs. irrigation amount) derived using the model rather on the accuracy of the estimated model parameters. Assimilating weekly measurements of leaf area index led to overall performance that was within 3% of the benchmark performance. Adding weekly measurements of biomass or daily measurements of soil water content did not improve the performance. On the other hand, assimilating only daily soil water content measurements led to poorer results (5% decrease compared to benchmark) and also affected the repeatability of the results. Defining dynamically the subset of parameters for calibration via sensitivity analysis rather than calibrating a fixed subset of parameters or all parameters was beneficial, both in terms of overall performance and repeatability of the results. Overall, concurrent data assimilation and model-based optimization has potential to enhance irrigation scheduling decision making, particularly in water limited environments.

An analysis framework to evaluate irrigation decisions using short-term ensemble weather forecasts

Irrigation water is an expensive and limited resource and optimal scheduling can boost water efficiency. Scheduling decisions often need to be made several days prior to an irrigation event, so a key aspect of irrigation scheduling is the accurate prediction of crop water use and soil water status ahead of time. This prediction relies on several key inputs including initial soil water status, crop conditions and weather. Since each input is subject to uncertainty, it is important to understand how these uncertainties impact soil water prediction and subsequent irrigation scheduling decisions. This study aims to develop an uncertainty-based analysis framework for evaluating irrigation scheduling decisions under uncertainty, with a focus on the uncertainty arising from short-term rainfall forecasts. To achieve this, a biophysical process-based crop model, APSIM (The Agricultural Production Systems sIMulator), was used to simulate root-zone soil water content for a study field in south-eastern Australia. Through the simulation, we evaluated different irrigation scheduling decisions using ensemble short-term rainfall forecasts. This modelling produced an ensemble of simulations of soil water content, as well as ensemble simulations of irrigation runoff and drainage. This enabled quantification of risks of over- and under-irrigation. These ensemble estimates were interpreted to inform the timing of the next irrigation event to minimize both the risks of stressing the crop and/or wasting water under uncertain future weather. With extension to include other sources of uncertainty (e.g., evapotranspiration forecasts, crop coefficient), we plan to build a comprehensive uncertainty framework to support on-farm irrigation decision-making.

Determining transpiration coefficients of ‘Rojo Brillante’ persimmon trees under Mediterranean climatic conditions

There is scarce knowledge on what are the actual water requirements of persimmon trees (Diospyros kaki). Research on this topic is required to better adjust irrigation water supplies to the actual crop water needs. This study aimed to determine the transpiration coefficients (Kp) of persimmon ‘Rojo Brillante’ trees and assess how transpiration and Kp are influenced by environmental factors and the atmospheric demand under Mediterranean climatic conditions. This was done over the course of three growing seasons by means of sap flow measurements calibrated with whole canopy gas exchange chambers. Irrigation was applied aiming to avoid water limiting conditions for trees. This was verified by monitoring the trees’ water status across the growing seasons. Sap flow measurements were linearly and highly correlated (p < 0.001) with canopy transpiration (Ep) obtained with the whole canopy chambers, but either overestimated or underestimated it. Ep increased from April until June-July in response to canopy development and increased midday canopy light interception. There was a positive linear relationship between daily Ep, reference evapotranspiration (ETo) and solar radiation. However, a non-linear relationship was obtained between Ep and vapor pressure deficit (VPD). A positive non-linear relationship (p < 0.01) was observed between Kp (Ep / ETo in non-limited water conditions) and VPD, illustrating the low stomatal regulation that persimmon trees exhibit in response to air dryness. Kp varied over the growing seasons following the ETo pattern although showing comparatively a slighter decrease during the last part of the growing season. From June to September, Kp of persimmon ‘Rojo Brillante’ trees with a midday photosynthetically active radiation (PAR) intercepted of ~30 % ranged from 0.45 to 0.56. A strong relationship was obtained between Kp and percentage of midday intercepted PAR that can be used to estimate Kp of persimmon ‘Rojo Brillante’ trees with a range of ground canopy covers. This information is useful for deriving specific water use models for persimmon trees to be implemented in decision support systems. The monthly transpiration coefficients here provided will be useful for optimizing irrigation scheduling decisions in persimmon orchards and therefore irrigation water management.

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.

Detecting tree water stress using a trunk relative water content measurement sensor

Irrigation water management in tree crops requires timely and accurate determination of crop water status for making irrigation scheduling decisions. However, commonly used techniques for measuring tree water statuses, such as stem water potential measured with pressure chambers or leaf stomatal conductance measured with porometers, are manually operated and lack the timeliness for making irrigation decisions in real-time. The objective of this research was to develop and field test a new electronic sensor system capable of continuously monitoring tree trunk relative water content. Replicated sensors were installed in experimental orchards planted with pomegranate and nectarine trees. Data collected from the sensor readings were compared with periodic trunk diameter, stem water potential, and leaf stomatal conductance measurements. Strong linear correlations were observed between sensor readings and tree trunk diameter variations but with a 3–4-hour phase lag. Sensor readings were also strongly correlated to stem water potential (R2 = 0.58-0.85) and leaf stomatal conductance (R2 = 0.60-0.84) for both pomegranate and nectarine trees. The next step is to further validate the sensors in commercial orchards and apply the sensors for making real-time irrigation water management decisions.

Transpiration and Water Use of an Irrigated Traditional Olive Grove with Sap-Flow Observations and the FAO56 Dual Crop Coefficient Approach

The SIMDualKc model was applied to evaluate the crop water use and the crop coefficient (Kc) of an irrigated olive grove (Olea europaea L.) located in Sicily, Italy, using experimental data collected from two crop seasons. The model applies the FAO56 dual Kc approach to compute the actual crop evapotranspiration (ETc act) and its components, i.e., the actual tree transpiration (Tc act), obtained through the basal crop coefficient (Kcb), and soil evaporation according to an evaporation coefficient (Ke). Model calibration was performed by minimizing the difference between the predicted Tc act and the observed daily tree transpiration measured with sap flow instrumentation (TSF field) acquired in 2009. The validation was performed using the independent data set of sap flow measurements from 2011. The calibrated Kcb was equal to 0.30 for the initial and non-growing season stages, 0.42 for the mid-season, and 0.37 for the end season. For both seasons, the goodness-of-fit indicators relative to comparing TSF field with the simulated Tc act resulted in root mean square errors (RMSE) lower than 0.27 mm d−1 and a slope of the linear regression close to 1.0 (0.94 ≤ b0 ≤ 1.00). The olive grove water balance simulated with SIMDualKc produced a ratio between soil evaporation (Es) and ETc act that averaged 39%. The ratio between actual (ETc act) and potential crop evapotranspiration (ETc) varied from 84% to about 99% in the mid-season, indicating that the values of ETc act are close to ETc, i.e., the adopted deficit irrigation led to limited water stress. The results confirm the suitability of the SIMDualKc model to apply the FAO56 dual Kc approach to tree crops, thus assessing the water use of olives and supporting the development of appropriate irrigation management tools that are usable by farmers. A different way to estimate Kcb is based on the approach suggested in 2009 by Allen and Pereira (A&amp;P), which involves the measured fraction of ground covered (shaded) by the crop and the height of the trees. Its application to the studied grove produced the mid-season Kcb values ranging from 0.40–0.45 and end-season Kcb values ranging from 0.35–0.40. The comparison between the A&amp;P-computed Tc act A&amp;P and TSF field shows RMSE values ranging from 0.27 to 0.43 mm d−1, which demonstrates the adequacy of the latter approach for parameterizing water balance models and for irrigation scheduling decision making.

Evaluating the role of remote sensing-based energy balance models in improving site-specific irrigation management for young walnut orchards

California’s weather is characterized by extreme droughts and floods. This has resulted in overdraft of groundwater aquifers as growers turn to this source of water for irrigation during droughts. California produces 99% of all walnuts in the US, walnut growers are under extreme pressure to optimize crop water use. Water use estimates at the field scale are crucial for growers to refine irrigation scheduling decisions. In this study, two single source remote sensing-based energy balance models (pySEBAL [python-based Surface Energy Balance Algorithm for Land] and SEBS [Surface Energy Balance System algorithm]) for estimating evapotranspiration (ETa) were evaluated against in-situ ETa measurements from surface renewal in two young walnut orchards in California’s Sacramento Valley. Strong correlations were obtained between the RS-based estimates and in-situ measurements for both pySEBAL and SEBS from 2017 to 2020, with R2 above 0.87, RMSE ranging from 0.79 to 1.05 mm, and NSE ranging from 0.79 to 0.88. SEBS out-performed pySEBAL on estimating time-series daily ETa for walnut at the field scale. During the mid-season characterized by high wind speed and high temperatures, pySEBAL and SEBS both underestimated ETa while the two models slightly overestimated ETa during the early growing season and post-harvest period. These results indicate the need for future research to focus on improving the performance of pySEBAL and SEBS when simulating time-series ETa under advection and sparse vegetation conditions, to provide more accurate ETa for both in-season and off-season irrigation water management. Comparisons of historical Kc and RS-based Kc were evaluated, and RS-based Kc matched better with the actual water use requirements of developing 2nd to 4th leaf young walnuts orchards. We observed substantial spatiotemporal variability of RS-based Kc in wo young walnut orchards at the CAPEX and Kauffman sites. For instance, Kc values for the north and south halves of the CAPEX orchard were above 1.2 and 0.9–1.1 respectively. Kc values for the north and south halves at the Kauffman site were in a range of 0.6–0.71 and 0.82–0.94 respectively. In other words, conventional irrigation management based on multiplying historical Kc and reference evapotranspiration is inadequate for site-specific walnut irrigation management due to spatial and temporal variability in Kc. ETa and Kc mapping from two single source models were similar in the spatial patterns and provided an overall visual characterization of the spatial variability in crop water use. Overall, RS-based ETa models using freely accessible satellite images and open-source algorithms could be used as an alternative to expensive in-situ measurements for enhancing site-specific young walnut irrigation management.

Field-Scale Spatial and Temporal Soil Water Variability in Irrigated Croplands

HighlightsThe greatest heterogeneity in soil water was observed at 30-60 cm depth in a corn field and 0-15 cm in a cotton field.Spatiotemporal soil water variability did not increase with increasing soil water in all soil layers in both croplands during the growing season.Water excess and water stress locations were identified in both fields. A single, temporally stable location was identified in each field, which can be used for precise uniform irrigation.Knowledge of variability and stability in soil water can be useful in determining the number and location of sensors to install in crop fields to assist irrigation scheduling decisions.Abstract. This study investigated the spatiotemporal variability and temporal stability of soil water at various depths in two croplands sown in corn and cotton during the 2018 growing season in the Tennessee Valley Region (TVR) of northern Alabama. Classical statistics and relative difference approaches were used to analyze soil water data in this study. In the corn field, the 30-60 cm depth showed the greatest variability, while the cotton field showed the greatest variability at 0-15 cm depth. A decreasing trend was noticed between mean soil water and coefficient of variation for all depths in the cotton field and at 30-60 cm depth in the corn field. However, convex upward, exponential decreasing, or no trends were found between mean soil water and standard deviation at different depths in both fields. The temporal stability analysis showed one representative sensor (S8 in corn and S1 in cotton) for the entire soil profile in both fields. Different statistical tests, i.e., Spearman’s correlation (rs), Nash-Sutcliffe efficiency (NSE), coefficient of determination (R2), etc., were used to reduce uncertainty or increase confidence in the performance of representative sensors. Among various field attributes, topography in corn and soil properties in cotton were determined as significant factors responsible for soil water variability. Crop evapotranspiration (ETc) showed significant negative weak and moderate correlations with soil water in the corn and cotton fields, respectively. However, the mean air temperature showed a significant positive correlation with soil water in the corn field and a significant negative correlation in the cotton field. Solar radiation had a significant negative correlation with soil water in the cotton field and a non-significant correlation in the corn field. Accumulated growing degree days (accGDD) showed a significant negative correlation with soil water in the corn field and a positive correlation in the cotton field. This study gives insights into soil water variability, provides useful information about temporal stability, and identifies significant factors for precision uniform irrigation scheduling. Keywords: Corn, Cotton, Croplands, Growing season, Irrigation, Soil water, Spatial and temporal variability, Temporal stability.

A Decade of Irrigation Water use trends in Southwestern USA: The Role of Irrigation Technology, Best Management Practices, and Outreach Education Programs

Irrigation water is crucial for farm operations in the world, with irrigated lands contributing about 40% to food and fiber production. In semi-arid regions such as the Southwestern United States, the demand for irrigation water has increased due to population growth, rising temperatures, and severe drought events in the region. Irrigation plays a vital role in the economies of southwestern states and requires comparative studies to understand the current situation and to propose possible improvement strategies. This study investigated the trend of irrigated cropland, the quantity of irrigation water use, irrigation technology, scheduling decisions, and irrigation outreach using data from 2007 and 2017 United States Department of Agriculture (USDA) National Agriculture Statistics Service (NASS) census. Harvested cropland in the region remained the same with minimal increase in total irrigated land (1%) and the quantity of irrigated water used (2%). However, gravity irrigation methods reduced significantly by 12%, with a 71% increase in the use of drip irrigation systems. The increase in the adoption of soil moisture sensors (55%), plant sensors (107%), government schedules (29%), and supplier’s schedules (50%) for irrigation scheduling decisions, did not translate to a reduction in irrigation water use at the regional level. However, at the state level within the same period, Arizona recorded an increase in irrigated cropland by 10% and harvested cropland by 9%, with a reduction in the quantity of irrigation water used (-5%). The gains in Arizona could be associated with the combined effects of improved irrigation technologies and the use of best management decisions, which could serve as a model for prudent water use in the southwest. There is a need to increase the effort in science-based education and extension programming on integrated approaches that emphasize both irrigation technology and the best management practices, which include seed selection for drought-tolerant crops.

Trends and Challenges in Irrigation Scheduling in the Semi-Arid Area of Spain

A growing international human population and rising living standards are increasing the demand for agricultural products. Under higher pressure over natural resources, environmental concerns are increasing as well, challenging current water use decision-making processes in irrigated agriculture. Higher agricultural productivity means water should be applied more efficiently, which requires instant information on weather, soil, and plant conditions throughout the growing season. An information-based irrigation scheduling application tightened to the spatiotemporal variability of the fields is critical for enhancing the current irrigation system and making better irrigation scheduling decisions. The aim of this study is to review current irrigation scheduling methodologies based on two case studies (woody and field crops) located in semi-arid areas of Southeast Spain. We realize that optimal irrigation programming requires consistent investment in equipment, expenditure on operation and maintenance, and qualified technical and maintenance services. These technological approaches will be worthwhile in farms with low water availability, high profitability, and significant technical-economic capacity.

Irrigator Pro: Progression of a Peanut Irrigation Scheduling Decision Support System

HighlightsThe logic used in developing a decision support system for irrigating peanut based on max/min soil temperature is describedLogic to transform decision support system from peanut to irrigate corn and cotton with and without soil sensors.Progression of a decision support system from a desktop program to a web/mobile applicationAbstract. Irrigator Pro is a decision support tool for scheduling irrigation events in peanut. It was deployed in 1995 as a rule-based system using crop history, yield potential, soil type, in-season irrigation/rainfall and maximum/minimum soil temperature. As computing platforms have progressed from desktop personal computers to mobile web-based platforms, Irrigator Pro has been updated and is now deployed as a web-based program and an application for mobile devices. Irrigator Pro not only works for peanuts but has been modified to irrigate both corn and cotton. The irrigation decisions are now based on in-field soil water potential measurements in addition to the traditional checkbook with max/min soil temperatures. Users are individual growers, extension agents, and agronomic consultants. The objective of this manuscript is to document the initial development of Irrigator Pro as an expert system combining data and experiential knowledge and the progression from a checkbook-based decision support system to a hybrid system using observed weather data and soil moisture measurement. The background knowledge, equations, and thresholds for triggering irrigation recommendations are included. Keywords: Decision support system, Irrigation scheduling, Irrigator Pro, Mobile app, Peanut, Soil water potential.