Precise Irrigation Scheduling Research Articles

Advanced evapotranspiration forecasting in Central Italy: Stacked MLP-RF algorithm and correlated Nystrom views with feature selection strategies

Evapotranspiration is a key parameter in hydrology, particularly in the field of water resources management. Reference evapotranspiration (ETo) stands as a crucial metric, embodying the influence of climate on water loss from both soil and plant surfaces. The accurate forecasting of future ETo values is paramount for informed decision-making in agricultural practices and water supply planning. The anticipation of evapotranspiration variations supports optimized irrigation, drought assessment, and efficient water allocation. Employing innovative algorithms, specifically the Multilayer Perceptron-Random Forest (MLP-RF) Stacked Model and the Correlated Nystrom Views (XNV), this study focuses on predicting ETo up to 60 days ahead in Agro Pontino, an area in Central Italy known for its flourishing agricultural production in the Mediterranean Europe. A Radial Basis Function (RBF) Neural Network serves as a benchmark. Taking into account firstly all available weather-climatic variables and subsequently adopting two different strategies for reducing the variables, based respectively on Principal Component Analysis and feature selection using Particle Swarm Optimization, three distinct sets of input variables were considered. The models based on the complete set of exogenous climatic variables demonstrated superior accuracy. However, even models relying on only mean temperature, maximum relative humidity, and shortwave solar radiation as inputs produced good results, also for the 60-day forecasting horizon, with Kling-Gupta Efficiency (KGE) and Mean Absolute Percentage Error (MAPE) equal to 0.98 and 8.356 %, respectively, in the case of MLP-RF Stacked Model. The latter consistently outperformed XNV and RBF across various combinations of input variables and forecasting horizons. Notably, the reduction in accuracy with extended forecasting horizons was mild, suggesting the potential for accurate results over significantly more extended horizons. These forecasting models facilitate precise irrigation scheduling, minimizing water wastage and conserving resources. This targeted approach enhances crop yields, quality, and environmental sustainability, rendering agriculture economically viable and adaptable to climate variability.

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.

A review on IoT based precision irrigation planning and scheduling

Global warming and climate change are warnings showcasing water crisis. At the same time ever growing population is ultimatum to the food security. In span of such times, world has to be made a sustainable habitat. It is only possible when each ounce of resources is being measured and used judiciously. Maximum responsibility is on farmers and researchers of the world. In times of advanced technologies, Internet of Things (IoT) has surfaced as a saviour. IoT based systems have been stated as success in monitoring and control mechanisms. Thus, this paper was intended to review the control strategies and monitoring systems based on IoT. The literature incorporates basic information as well as recent trends in the field of irrigation management based on IoT.

Assessing capacitance soil moisture sensor probes’ ability to sense nitrogen, phosphorus, and potassium using volumetric ion content

Accurate and near real-time volumetric soil water and volumetric ion content (VIC) measurements can both inform precise irrigation scheduling and aid in fertilizer management applications in cropping systems. To assist in the monitoring of these measurements, capacitance-based soil moisture probes are used in agricultural best management practice (BMP) programs. However, the ability of these sensors to detect nutrients in the soil sourced from fertilizers is not well understood. The objective of this study was to evaluate the sensitivity of a capacitance-based soil moisture probe in detecting Nitrogen (N), Phosphorous (P), and Potassium (K) movement in the soil. To achieve this, a laboratory-based setup was established using pure sand soil cores. Raw soil moisture and VIC probe readings from the cores were contrasted across multiple N, P, and K rates. The N treatments applied were rates of 0, 112, 168, and 224 kg/ha; for P, were 0, 3.76, and 37.6 kg/ha, and for K were 0, 1.02, 1.53, and 2.04 kg/ha. Each nutrient was evaluated separately using a randomized complete block design experiment with three replications for N and K, and 5 replications for P. The impact of each nutrient rate on the sensitivity of VIC readings was determined by evaluating differences in three points of the time series, including the observed maximum point, inflection point, and convergence value as well as the time of occurrence of those points over a 24-hour period. These points were assessed at depths 5, 15, 25, 35, 45, and 55 cm. The findings of this study highlight the capacitance-based soil moisture probes’ responsiveness to changes in all K rates at most depths. However, its sensitivity to changes in N and P rates is comparatively lower. The results obtained in this study can be used to develop fertilizer management protocols that utilize K movement as the baseline to indirectly assess N and P, while helping to inform those who currently use the probe which nutrients the probe may be detecting. The probes’ readings could be incorporated into decision support systems for irrigation and nutrient management and improve control systems for precision water and nutrient management.

Irrigation Scheduling and Cultivar Management for Increasing Water Productivity under Dryland Condition: A Review

In Dryland environments with limited water resources, irrigation scheduling and cultivar management are play pivotal roles in enhancing water productivity. Effective irrigation scheduling involves the judicious timing and amount of water application, considering factors such as soil moisture levels and crop growth stages. Utilizing advanced technologies, such as soil moisture sensors and remote sensing, facilitates precise irrigation management. This approach not only conserves water but also prevents waterlogging and salinity issues, promoting optimal plant growth. Cultivar selection is another critical aspect, focusing on identifying and cultivating crops that are well-adapted to arid conditions. Drought-resistant cultivars with traits like deep root systems and efficient water use contribute significantly to water productivity. Integrating modern breeding techniques and genetic engineering enhances the development of cultivars with improved drought tolerance, ensuring resilience in dryland agriculture. Furthermore, the implementation of agro ecological practices, such as conservation tillage and organic farming, complements irrigation scheduling and cultivar management. These practices enhance soil structure, water retention, and overall ecosystem health, fostering a more sustainable and resilient agricultural system. Adopting a comprehensive approach that combines precise irrigation scheduling, resilient cultivar selection and agro ecological practices holds great potential for increasing water productivity in dryland conditions. This integrated strategy not only addresses the challenges posed by water scarcity but also contributes to the long-term viability of agriculture in arid regions.

Testing a Soil Moisture Sensor Based Irrigation Controller in both Laboratory and Field Conditions for Fargo Silty Clay Soils

Highlights A soil matric potential sensor-based irrigation system was tested in lab and field experiments. Soil matric potential and soil moisture levels for each threshold were determined for Fargo silty clay soil through lab experiment. Thresholds 5, 6, 7, and 8 at -35, -45, -55, and -70 kPa were tested through the field experiment for watermelon production. Abstract. Irrigation scheduling plays a pivotal role in water management. Conventional irrigation methods often struggle to maintain consistent moisture across fields due to uniform water application irrespective of field conditions and soil moisture levels. Automatic soil moisture sensors offer a solution by tailoring irrigation based on field conditions. These systems trigger irrigation when soil is too dry or skip it when moisture levels are sufficient. Hence, setting the right threshold value is key for sensor-based irrigation. This study involved laboratory testing of an irrigation controller, and determining soil matric potential and volumetric water content at various thresholds (1 to 9) for Fargo Silty Clay soil. Subsequently, a field experiment assessed its performance for a drip irrigation system at different thresholds (5, 6, 7, and 8) for watermelon cultivation, both with and without clear plastic mulch, in a randomized layout. Thresholds 5, 6, 7, and 8 (-35, -45, -55, and -70 kPa), along with no irrigation, were considered with one irrigation event daily. Results from laboratory and field tests underscore the controller’s efficacy in managing drip irrigation timing and duration. Notably, threshold 7 (-55 kPa) combined with clear plastic mulch emerged as the most optimal treatment, yielding the highest watermelon production of 129.6 tons/ha. Moreover, this approach delivered premium produce quality, but utilizing only 142.2 mm of irrigation water. This study emphasizes the role of precise irrigation scheduling. By integrating smart technology and thoughtful threshold setting, agricultural practices can be elevated, yielding abundant crops while conserving water and promoting sustainability. Keywords: Controller, Drip irrigation, Mulch, Sensor, Watermelon.

Evaluation of Canopy Temperature Based Crop Water Stress Index for Deficit Irrigation Management of Sugar Beet in Semi-Arid Climate

Highlights Sugar beet irrigation scheduling was based on daily average crop water stress index between 13:00 and 16:00 hours. Three crop water stress index thresholds, 0.2, 0.35, and 0.55, were evaluated for irrigation scheduling. Season evapotranspiration decreased and soil water extraction increased as crop water stress threshold increased. There was no significant difference in root or sucrose yield between full irrigation and 0.2 crop water stress index, while seasonal irrigation depths were reduced from133 to 185 mm. Abstract. Sugar beet is an economically important crop in the semi-arid Intermountain Western U.S., with seasonal water use ranging from 500 to 900 mm. Sugar beet is a deep-rooted crop (1.5-2 m) in unrestricted soil profiles that can utilize stored soil water to reduce seasonal irrigation requirements. Effective use of stored soil water below 0.6 m requires precise irrigation scheduling and knowledge of soil water availability below 0.6 m, which is usually unknown due to the labor and expense of soil water monitoring at deeper depths and uncertainty in effective rooting depth and soil water holding capacity. Deficit irrigation (DI) management of sugar beet using a thermal-based crop water stress index (CWSI) has the potential to overcome soil water monitoring limitations and facilitate the utilization of stored soil water to reduce seasonal irrigation requirements. The objective of the research summarized in this paper was to implement and evaluate the effect of automated DI scheduling of sugar beet using three daily average CWSI thresholds (0.2, 0.35, and 0.55) on seasonal irrigation requirement, crop evapotranspiration, seasonal soil water depletion, root yield, estimated recoverable sugar (ERS) yield, and water use efficiency compared to full irrigation. There were no significant differences in root and ERS yield between full irrigation and 0.2 CWSI DI treatment, while seasonal ET was significantly decreased, seasonal soil water extraction was significantly increased, and seasonal irrigation depths were reduced from 133 to 185 mm. Root and ERS yield water production functions were curvilinear with a downward concave. Root and ERS yield water use efficiencies were constant or increased slightly for crop evapotranspiration reductions up to 85% of full irrigation evapotranspiration. The results indicate that irrigating when the average daily CWSI sugar beet exceeds 0.2 is an effective means for mild deficit irrigation scheduling to reduce seasonal irrigation requirements with no significant effect on root and ERS yield. Keywords: Crop water stress index, Evapotranspiration, Irrigation, Irrigation scheduling, Root yield, Sucrose yield, Sugar beet.

Determination of Irrigation water requirement and Scheduling of onion at Low land area of Wag-himra, Northern Ethiopia

Irrigation that saves water is important for satisfying the economic and environmental sustainability of commercial agriculture and improving the living standard of the people. Precision irrigation scheduling is critical to improving irrigation efficiency. A field experiment was conducted in two consecutive years at Abergelle irrigation schemes. The aim of the study was to determine crop water requirement and irrigation schedule of onion (when and how much to irrigate) for most market-oriented crops. The treatments were arranged with a factorial arrangement randomized complete block design with three replications. Three Levels of CROPWAT, fixed application depth (125%, 100%, and 75%ETc) and, three irrigation intervals (3, 4, and 5 days) and 1-farmer practice were used as control. The study result showed that 75% CROPWAT fixed depth at 3-day intervals achieved high water productivity, and saved 2873m3ha-1 of irrigation water compared with farmers’ method of irrigation. It provides additional irrigated land of 0.84 ha with a yield gain of 10.44 t ha-1. Therefore, a 3-day irrigation interval with 75% CROPWAT irrigation depth was recommended for the optimum yield and water productivity of the crop.

Food-energy‑carbon nexus of Himalayan okra–pea cropping system: Impacts of AM-fungi, precision phosphorus and irrigation regimes in an acid Alfisol

Conventional farming practices are energy and carbon-intensive. Low-cost technologies like AM-fungi (AMF) and precision P-management vis-à-vis precision irrigation-scheduling may enhance P-bioavailability, and crop- and water-productivity with reduced energy and carbon-footprints in acid-Alfisol of north-western Himalayas. Hence, an experiment was done in okra (Abelmoschus esculentus)-pea (Pisum sativum) cropping system (OPCS) using AMF (Glomus mosseae) at three inorganic-P levels (50, 75 & 100 % of soil-test based P-dose) and two irrigation-regimes (40 & 80 % AWC). AMF-inoculation significantly enhanced the system-productivity (∼11 %), bio-energy output (∼8 %) and SOC-storage over non-AMF plots in OPCS. Carbon-input use followed the trend of water>diesel>fertilizers> FYM > herbicides> pesticides>AMF in OPCS. AMF-inoculation significantly reduced the carbon-footprints (0.466 kg CO2-e kg−1) by ∼10.2 % over non-AMF plots. Soil-test based 100 % P-dose significantly enhanced the system-productivity (6.3–15.6 %) and bio-energy output (4.7–12.6 %) with lesser carbon-footprints (5.3–15 %) over 50 and 75 % P-dose. Irrigation at 80 % AWC enhanced system-productivity (∼4.1 %), however at 40 % AWC reduced carbon-footprints by ∼11.7 % besides saving irrigation-water by ∼24 % (150 mm ha−1/year) in OPCS. Hence, farmers must necessarily use AMF-inoculation coupled with soil-test based P-fertilization (75–100 %) while preserving a balance in irrigation water-use at 40–80 % AWC depending upon water-availability for higher crop- and water-productivity besides lesser energy and carbon-footprints in OPCS in Himalayan acid Alfisol.

Internet of Things (IoT) for Soil Moisture Tensiometer Automation

Monitoring of water retention behavior in soils is an essential process to schedule irrigation. To this end, soil moisture tensiometers usually equipped with mechanical manometers provide an easy and cost-effective monitoring of tension in unsaturated soils. Yet, periodic manual monitoring of many devices is a tedious task hindering the full exploitation of soil moisture tensiometers. This research develops and lab validates a low cost IoT soil moisture tensiometer. The IoT-prototype is capable of measuring tension up to -80 Kpa with R2 = 0.99 as compared to the same tensiometer equipped with a mechanical manometer. It uses an ESP32 MCU, BMP180 barometric sensor and an SD card module to upload the measured points to a cloud service platform and establishes an online soil water potential curve. Moreover, it stores the reading on a micro-SD card as txt file. Being relatively cheap (76 USD) the prototype allows for more extensive measurements and, thus, for several potential applications such as soil water matric potential mapping, precision irrigation, and smart irrigation scheduling. In terms of energy, the prototype is totally autonomous, using a 2400 mAh Li-ion battery and a solar panel for charging, knowing that it uses deep sleep feature and sends three data points to the cloud each 6 h.

Poyen’s Fuzzy Logic Controlled Automatic Irrigation (FCAI): Precision Irrigation Scheduling Scheme

Water wastage is a grave problem in irrigation due to outdated methods. This work illustrates how a well-structured fuzzy rule base can eliminate the issue and utilize water at an optimized level. Poyen’s fuzzy logic controlled automatic irrigation (Poyen’s FCAI) efficiently manages the water requirement needs in irrigation processes considering the evapotranspiration (ET) loss and cultivating conditions. Poyen’s FCAI essentially comprises two modules, the wireless sensor network (WSN) and information processing module to perform the assigned task effectively and efficiently. The wireless sensor unit (WSU) accumulates the necessary data, determines the ET loss, and runs the decision logic before conveying it to the execution unit, i.e., the wireless information processing unit (WIPU). The WIPU executes the decision by manipulating the water delivery system, ensuring that the field receives the optimum volume of water. This article illustrates the fuzzy model with simulation output. The model is field-tested with and without the control action over two seasons and in adjacent plots. The results show promising improvements in both water management and crop yield due to more accurate conditions. Hence, we obtain a superior automatic irrigation prototype model for all soil types, across different growth stages, and for all crops with an exhaustive lookup table in place.

Precision Irrigation Scheduling Based on Wireless Soil Moisture Sensors to Improve Water Use Efficiency and Yield for Winter Wheat in Sub-Saharan Africa

In Sub-Saharan Africa, where most irrigation systems are manually operated, water allocation and irrigation scheduling are often based on uniform application irrespective of crop needs and growth stages, which results in nonoptimal water use. Recently, a lot of research has been carried out to improve irrigation water use efficiency through automation by employing wireless sensor-based monitoring systems. Further to the improvement of water use efficiency and yield, while reducing costs, a field trial was carried out at a farm in Harare, Zimbabwe, during the 2016, 2017, and 2018 winter seasons to test whether a new approach to the automated irrigation systems, one based on IoT and wirelessly connected soil sensors (called hereafter as WCSS), improves water use efficiency without reducing yield. WCSS method was compared with three widely used conventional irrigation methods, that is, manual scheduling, tensiometer-based scheduling, and weather-based scheduling. Impacts on water savings and yield of winter wheat crops under drip irrigation were evaluated. WCSS saved up to 25% more water compared to typical fixed irrigation schedule rates used by wheat growers during the winter season.

Comparison of Shuttleworth–Wallace and Dual Crop Coefficient Method for Estimating Evapotranspiration of a Tea Field in Southeast China

Determination of evaporation (E) and transpiration (T) in tea fields separately is important in developing precise irrigation scheduling and enhancing water use efficiency. In this study, the Shuttleworth–Wallace (S-W) model was applied to simulate the variations of E and T based on the data from 2015 to 2018 in a tea field in southeast China. The dual crop coefficient (D-K) method recommended by FAO-56 was also applied to calculate E and T, using the same data set to compare with the S-W model. The measured crop coefficient (Kc) ranged from 0.43 to 1.44 with the average value was 0.90 during 1–150 DOY (days of year), and the measured Kc tended to be stable with the average value of 0.83 during 151–365 DOY in 2015. The S-W model estimated ETc with root mean square error (RMSE) and R2 of 0.45 mm d−1 and 0.97, while for the D-K method the values were 0.61 mm d−1 and 0.95. Therefore, both approaches could estimate the E and T separately in tea fields in southeast China, however, the D-K method had a slightly poorer accuracy compared to the S-W model in the estimation of ETc.

A pilot study of sensor-based soil moisture assessment for precise irrigation scheduling in apple

A pilot study of sensor-based soil moisture assessment for precise irrigation scheduling in apple

Determination of Growth-Stage-Specific Crop Coefficients (Kc) for Drip Irrigated Wheat (Triticum aestivum L.) under Different Land Configurations

Determination of the actual crop evapotranspiration (ETc) during the crop growth is important for precise irrigation scheduling, sustainable development and environmentally sound water management. Development of a crop coefficient (Kc) can enhance ETc estimations in relation to specific crop phenological development. An experiment was conducted on sandy loam soil at Junagadh Agricultural University, Junagadh to determine growth stage specific Kc of wheat (GW-366) using drip irrigation under different land configurations (L1: broad bed furrow and L2: flat land) at different irrigation levels (I1: 1.0 ETc and I2: 0.8 ETc). Soil moisture sensors were utilized to estimate Actual crop evapotranspiration. Results revealed that adjusted FAO Kc predicts higher value than sensor-based Kc values under both land configurations. Broad bed furrow (BBF) land configuration observed lower Kc values compared to flat land configuration at all growth stages of wheat. Sensor based Kc-ini, Kc-dev, Kc-mid and Kc-end values of BBF observed 0.21 (7.26%), 0.59 (13.78%), 1.00 (7.27%) and 0.29 (9.48%) and 0.20 (8.43%), 0.55 (13.04%), 0.91 (8.18%) and 0.26 (9.48%) lower than flat land configuration. Overestimated adjusted FAO-Kc values caused a loss 106.18 mm and 89.43 mm precious water for wheat under BBF and flat land respectively.

Irrigation water requirements as affected by diverse climate conditions

Determination of crop water requirement is one of the key parameters for precise irrigation scheduling, especially under limited water resources, such as in Egypt. Hence, an accurate estimation of reference evapotranspiration (ETo) is a vital factor for agriculture production, therefore the objectives of the present study were to study the influence of climate change on different seasons and comparing of ETo estimations using Blany- Criddle (BC) and FAO-56 Penman-Monteith (PM) equations under present and future climatic conditions. Data on the present climate have been collected from Wadi El-Natrun meteorological station, Egypt from 1991 to 2020. While the future climate data have been chosen for the concerned RCPs scenarios: RCP2.6, RCP4.5, RCP6.0, and RCP8.5 in 2040, 2060, 2080, and 2100. The results revealed that all months’ reordered Tmean above 20 oC, except December, January, and February where they ranged between 17.68 and 19.44 oC. The highest Tmean values were observed in July (32.3oC), August (31.9 oC), and June (31.8 oC), while February scored the lowest Tmean (17.7 oC).

Mobile ‘app’ for Precise Irrigation Scheduling in Sugarcane Cultivation under Indian Subtropics

Mobile ‘app’ for Precise Irrigation Scheduling in Sugarcane Cultivation under Indian Subtropics

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.

A Review of Evapotranspiration Measurement Models, Techniques and Methods for Open and Closed Agricultural Field Applications

Detailed knowledge of energy and mass fluxes between land and the atmosphere are necessary to monitor the climate of the land and effectively exploit it in growing agricultural commodities. One of the important surface land fluxes is evapotranspiration, which combines the process of evaporation from the soil and that of transpiration from plants, describing the movement of water vapour from the land to the atmosphere. Accurately estimating evapotranspiration in agricultural systems is of high importance for efficient use of water resources and precise irrigation scheduling operations that will lead to improved water use efficiency. This paper reviews the major mechanistic and empirical models for estimating evapotranspiration including the Penman–Monteith, Stanghellini, Priestly–Taylor, and Hargreaves and Samani models. Moreover, the major differences between the models and their underlined assumptions are discussed. The application of these models is also reviewed for both open and closed field mediums and limitations of each model are highlighted. The main parameters affecting evapotranspiration rates in greenhouse settings including aerodynamic resistance, stomatal resistance and intercepted radiation are thoroughly discussed for accurate measurement and consideration in evapotranspiration models. Moreover, this review discusses direct evapotranspiration measurements systems such as eddy covariance and gas exchange systems. Other direct measurements appertaining to specific parameters such as leaf area index and surface leaf temperature and indirect measurements such as remote sensing are also presented, which can be integrated into evapotranspiration models for adaptation depending on climate and physiological characteristics of the growing medium. This review offers important directions for the estimation of evapotranspiration rates depending on the agricultural setting and the available climatological and physiological data, in addition to experimentally based adaptation processes for ET models. It also discusses how accurate evapotranspiration measurements can optimise the energy, water and food nexus.

Dependence of CWSI-Based Plant Water Stress Estimation with Diurnal Acquisition Times in a Nectarine Orchard

Unmanned aerial vehicle (UAV) remote sensing has become a readily usable tool for agricultural water management with high temporal and spatial resolutions. UAV-borne thermography can monitor crop water status near real-time, which enables precise irrigation scheduling based on an accurate decision-making strategy. The crop water stress index (CWSI) is a widely adopted indicator of plant water stress for irrigation management practices; however, dependence of its efficacy on data acquisition time during the daytime is yet to be investigated rigorously. In this paper, plant water stress captured by a series of UAV remote sensing campaigns at different times of the day (9h, 12h and 15h) in a nectarine orchard were analyzed to examine the diurnal behavior of plant water stress represented by the CWSI against measured plant physiological parameters. CWSI values were derived using a probability modelling, named ‘Adaptive CWSI’, proposed by our earlier research. The plant physiological parameters, such as stem water potential (ψstem) and stomatal conductance (gs), were measured on plants for validation concurrently with the flights under different irrigation regimes (0, 20, 40 and 100 % of ETc). Estimated diurnal CWSIs were compared with plant-based parameters at different data acquisition times of the day. Results showed a strong relationship between ψstem measurements and the CWSIs at midday (12 h) with a high coefficient of determination (R2 = 0.83). Diurnal CWSIs showed a significant R2 to gs over different levels of irrigation at three different times of the day with R2 = 0.92 (9h), 0.77 (12h) and 0.86 (15h), respectively. The adaptive CWSI method used showed a robust capability to estimate plant water stress levels even with the small range of changes presented in the morning. Results of this work indicate that CWSI values collected by UAV-borne thermography between mid-morning and mid-afternoon can be used to map plant water stress with a consistent efficacy. This has important implications for extending the time-window of UAV-borne thermography (and subsequent areal coverage) for accurate plant water stress mapping beyond midday.