Improved Irrigation Management Research Articles

Irrigation management or climate change ? Which is more important to cope with water shortage in the production of table grape in a Mediterranean context

Table grape production requires large amount of water, which can be problematic in semi-arid Mediterranean regions, where climate change projections anticipated reductions in water availability associated to decreases in precipitation and increases in temperature. In this context, this study aims to evaluate the effect of contrasting irrigation strategies and climate change scenarios on key water balance variables using a Chilean Table grape crop as case study. A standard and an improved irrigation management treatments were implemented in situ during the 2015/2016 and the 2016/2017–2017/2018 observed growing seasons, respectively. Then, the HYDRUS-1D water transfer model was run to simulate the three observed growing seasons and 27 near future growing seasons (2019/2020–2044/2015) under climate change conditions. Satisfactory calibration and validation results against soil moisture and water storage measurements were obtained within the first and the second observed growing seasons respectively (RRMSE values below 5%). Results during the observed seasons showed that by changing the standard irrigation by the improved irrigation management, the water use efficiency (WUEi) increases from 49.5% to 55.7%. For the near future, the calibrated model shows that under all the tested climate change scenarios, irrigation strategies based on supplying 80% and 50% of the crop evapotranspiration (ETc) (deficit irrigation scenarios) have larger efficiencies compared to the standard irrigation management (presenting a higher actual basal crop coefficient and lower percolation). Similar results were obtained under future extreme climate change years, defined as the ratio between model-based projections of reference evapotranspiration (ET0) and precipitation, with the deficit irrigation scenarios having larger efficiencies than the standard irrigation management. Based on these results, it is concluded that by mid- century, the irrigation management has more relevance than climate change impacts for tables grapes growing under a Mediterranean climate in central Chile.

CROP COEFFICIENT OF MARANDU PALISADE GRASS: AN APPROACH INVOLVING LEAF AREA INDEX AND CANOPY HEIGHT

Determining the evapotranspiration rate enhances our understanding of plant water needs, potentially improving irrigation management. This study aimed to investigate the water consumption of the Marandu palisade grass (Urochloa brizantha) cultivar ‘Marandu’, single cropped and intercropped with black oat and ryegrass, by determining the crop coefficient (Kc) and their correlation with canopy height (CH) and leaf area index (LAI). The experiment was carried out at ESALQ/USP from April 2016 to January 2017. Two weighing lysimeters were used for calculated the Crop evapotranspiration (ETc): one with single-cropped Marandu palisade grass throughout the year and the other intercropped with black oat and ryegrass during autumn and winter (from April to September 2016). Estimated Kc, CH, and LAI were measured every 4-day. Regression equations were generated to determine the correlation between Kc and CH and Kc and LAI in each season. Minimum and maximum Kc values for single cultivation in each season were: 0.61 and 0.97 for spring, 0.45 and 1.10 for summer, 0.47 and 0.74 for autumn, and 0.52 and 0.94 for winter. In the intercropped cultivation, Kc values ranged from 0.62 to 1.04.

Estimating and partitioning maize evapotranspiration as affected by salinity using weighing lysimeters and the SIMDualKc model

Water scarcity and saline stress are primary threats for water use and agricultural production in arid and semi-arid regions, such as the Hetao Irrigation District, China. The current study, using irrigation with saline water, developed through three seasons (2017–2019) of field experimental surveillance of mulched maize water use in two cropped weighing lysimeters. Measurements included crop characteristics (height, LAI, dates of development stages), soil and water salinity, and daily actual crop evapotranspiration (ETc act). Deficit surface irrigation was scheduled in 2017 and 2018 seasons, while in 2019 drip irrigation was used aimed at satisfying crop water requirements. Both approaches aimed at improving irrigation management while controlling impacts of salinity. The collected information was used to parameterize and calibrate the soil water balance model SIMDualKc. The model uses the FAO56 dual crop coefficient approach and considers the combined effect of water and salinity stresses due to salinity of both soil and irrigation water. Results show that the SIMDualKc model adequately simulates the dynamics of the observed ETc act throughout the three growing seasons. The goodness-of-fit indicators show highly appropriate model fitting of observed ETc act with low RMSE of 0.42 mm d-1 for the calibration and 0.53 mm d-1 for validation. The calibrated standard basal crop coefficients (Kcb) of maize for the initial, mid, and end stages were respectively 0.15, 1.15 and 0.25, which agree with those recently proposed by Pereira et al. (2021). Due to the impacts of both water and salt stress in 2017 and 2018, ETc act was much below the potential value ETc, ranging 64–83%, while in 2019 that percentage increased to 92% due to avoiding water stress by then. The average Kcb mid act range 0.58–1.06, therefore lower than Kcb mid, with the lower values occuring in the lysimeter that was irrigated with a large deficit. The irrigation scheduling practiced was assessed using water use and productivity indicators, which have shown the advantage in using drip irrigation with small time intervals between irrigations. Moreover, the current study and the calibrated model provide for adopting irrigation management practices that may save water and control salinity.

High precision zero-friction magnetic dendrometer

Increasing agricultural demand for freshwater in the face of a changing climate requires improved irrigation management to maximize resource efficiency. Soil water deficits can significantly reduce plant growth and development, directly impacting crop quantity and quality. Dendrometers are a plant-based tool that have shown potential to improve irrigation management in high-value woody perennial crops (e.g., trees and vines). A dendrometer continuously measures small fluctuations in stem diameter; this has been directly correlated to water stress measurements using traditional methods. While plant-based measures of water deficits are considered to be the best measures of water stress, current dendrometer methods are imprecise due to mechanical hysteresis and thermal expansion. The high-precision dendrometer created at the OPEnS Lab alleviates these key failure points using zero-thermal expansion carbon fiber, zero friction via a spring tensioning approach, and a linear magnetic encoder. In-lab tests and field deployments have validated device measurements and the execution of these pivotal qualities. Mass deployment of these automated dendrometers has the potential to provide a continuous record of water stress, providing valuable decision support for irrigation management.

The Development of Smart Irrigation System With IoT, Cloud, and Big Data

Irrigation management in several developed countries now uses modern technology by utilizing the capacity of the Internet of Things and Big Data for leak detection, water measurement, planning, monitoring and distribution. The Internet of Things provides new solutions for the irrigation sector and helps improve irrigation management and reduce operational costs associated with maintaining infrastructure. One of the biggest problems facing smart irrigation systems in Indonesia is the inability of stakeholders to convert available data into in-depth and accurate information that can be used in decision making. The purpose of writing this article is to prepare the concept of building a smart irrigation system for the sustainable use of water for stakeholders. Analysis and approaches are carried out by reviewing the development of smart irrigation systems from the perspective of Industry 4.0 (big data and internet of things), identifying the use of IoT-based technology, innovation and data-science best practices that can transform organizations and institutions, irrigation users become more data-centered centric artificial intelligence. The results of this study propose innovative simple semantic solutions to help irrigation users or farmers to measure humidity, rainfall, wind speed, soil temperature and solar radiation in real-time through mobile application information to the water user community.

An Evaluation of a New Scheme for Determination of Irrigation Depths in the Egyptian Nile Delta

Innovative irrigation techniques should be implemented to improve irrigation management in dryland countries. In this regard, a new scheme, that uses three sets of irrigation depth and numerically simulated cumulative transpiration, was evaluated in the Egyptian Nile delta in 2020. Presuming that water is volumetrically priced, the proposed scheme can maximize net incomes at optimum irrigation depths considering quantitative weather forecasts. A field experiment was carried out with a randomized complete block design using a major crop, maize, to assess the feasibility of the proposed scheme in comparison to a sensor-based irrigation method under conditions of dry climate and clay loamy soil. The proposed scheme could increase the gross net income of farmers and conserve irrigation by 21% and 35%, respectively, compared to a sensor-based irrigation method, although the yield and its components were almost the same with no significant statistical differences. The model could accurately simulate soil water content in the topsoil layers with a RMSE of 0.02 cm3 cm−3. The proposed scheme could be a useful tool to spare the costs of expensive soil monitoring sensors while saving water and improving net income.

Efficient IoT-Based Control for a Smart Subsurface Irrigation System to Enhance Irrigation Management of Date Palm.

Drought is the most severe problem for agricultural production, and the intensity of this problem is increasing in most cultivated areas around the world. Hence improving water productivity is the primary purpose of sustainable agriculture. This study aimed to use cloud IoT solutions to control a modern subsurface irrigation system for improving irrigation management of date palms in arid regions. To achieve this goal, we designed, constructed, and validated the performance of a fully automated controlled subsurface irrigation system (CSIS) to monitor and control the irrigation water amount remotely. The CSIS is based on an autonomous sensors network to instantly collect the climatic parameters and volumetric soil water content in the study area. Therefore, we employed the ThingSpeak cloud platform to host sensor readings, perform algorithmic analysis, instant visualize the live data, create event-based alerts to the user, and send instructions to the IoT devices. The validation of the CSIS proved that automatically irrigating date palm trees controlled by the sensor-based irrigation scheduling (S-BIS) is more efficient than the time-based irrigation scheduling (T-BIS). The S-BIS provided the date palm with the optimum irrigation water amount at the opportune time directly in the functional root zone. Generally, the S-BIS and T-BIS of CSIS reduced the applied irrigation water amount by 64.1% and 61.2%, respectively, compared with traditional surface irrigation (TSI). The total annual amount of applied irrigation water for CSIS with S-BIS method, CSIS with T-BIS method, and TSI was 21.04, 22.76, and 58.71 m3 palm−1, respectively. The water productivity at the CSIS with S-BIS (1.783 kg m−3) and T-BIS (1.44 kg m−3) methods was significantly higher compared to the TSI (0.531 kg m−3). The CSIS with the S-BIS method kept the volumetric water content in the functional root zone next to the field capacity compared to the T-BIS method. The deigned CSIS with the S-BIS method characterized by the positive impact on the irrigation water management and enhancement on fruit yield of the date palm is quite proper for date palm irrigation in the arid regions.

Managing Water and Salt for Sustainable Agriculture in the Indus Basin of Pakistan

The Indus basin of Pakistan occupies about 16 million ha (Mha) of land. The Indus River and its tributaries are the primary sources of surface water. An estimated 122 km3 of surface water is diverted annually through an extensive canal system to irrigate this land. These surface water supplies are insufficient to meet the crop water requirements for the intensive cropping system practiced in the Indus basin. The shortfall in surface water is met by exploiting groundwater. Currently, about 62 km3 of groundwater is pumped annually by 1.36 million private and public tube wells. About 1.0 million tubewells are working only in the Punjab province. Small private tubewells account for about 80% of the pumped volume. Inadequate water allocation along the irrigation canals allows excessive water use by head-end farmers, resulting in waterlogging. In contrast, the less productive use of erratic supplies by tail-end farmers often results in soil salinity. The major issues faced by irrigated agriculture in Pakistan are low crop yields and water use efficiency, increasing soil salinization, water quality deterioration, and inefficient drainage effluent disposal. Currently, 4.5 Mha (about 30% of the total irrigated area) suffers from adverse salinity levels. Critical governance issues include inequitable water distribution, minimizing the extent to which salt is mobilized, controlling excessive groundwater pumping, and immediate repair and maintenance of the infrastructure. This paper suggests several options to improve governance, water and salt management to support sustainable irrigated agriculture in Pakistan. In saline groundwater areas, the rotational priorities should be reorganized to match the delivery schedules as closely as possible to crop demand, while emphasizing the reliability of irrigation schedules. Wherever possible, public tubewells should pump fresh groundwater into distributaries to increase water availability at the tail ends. Any substantial reform to make water delivery more flexible and responsive would require an amendment to the existing law and reconfiguration of the entire infrastructure, including thousands of kilometers of channels and almost 60,000 outlets to farmer groups. Within the existing political economy of Pakistan, changing the current water allocation and distribution laws without modernizing the infrastructure would be complicated. A realistic reform program should prioritize interventions that do not require amendment of the Acts or reconstruction of the entire system and are relatively inexpensive. If successful, such interventions may provide the basis for further, more substantial reforms. The present rotational water supply system should continue, with investments focusing on lining channels to ensure equitable water distribution and reduce waterlogging at the head ends. Besides that, the reuse of drainage water should be encouraged to minimize disposal volumes. The timely availability of farm inputs can improve individual farmers’ productivity. Farmers will need to have access to new information on improved irrigation management and soil reclamation approaches. Simultaneously, the government should focus more on the management of drainage and salinity.

SALEACH: A new web-based soil salinity leaching model for improved irrigation management

Leaching is essential in irrigated croplands where natural precipitation is insufficient to control salinity buildup. Several useful models exist for salinity management; however, leaching requirement (LR) calculations are based on steady-state approaches that only consider salinity tolerance of crops and irrigation water salinity to estimate the LR. In this study, a web-based soil salinity leaching management model (SALEACH) was developed as an online tool to assist growers for better and easier management of soil salinity to sustain agricultural production in irrigated croplands. SALEACH employs the traditional steady-state approach to estimate LRs but improves outputs by not only considering irrigation water salinity (ECiw) and salinity tolerance of specific crops (ECt), but also root water uptake patterns to account for irrigation system differences, and soil types for differences in hydraulic characteristics, as well as water stress and rainfall input. The SALEACH model can calculate the required irrigation water depth by using the estimated LR or any user-specified leaching fraction (LF) values; it can predict the drainage water salinity and soil salinity in the rootzone based on the applied leaching; and it can estimate relative crop yield for a given LF. SALEACH-estimated LRs were assessed in different soil types and irrigation systems by comparing them with LRs, soil water and drainage water salinity values obtained from an existing steady-state model (WATSUIT) and a transient-state model (HYDRUS-1D). Statistical analyses showed that SALEACH-estimated LRs, soil salinity, and drainage water salinity were all in the acceptable ranges of the corresponding values derived from other models. Thus, we conclude SALEACH is reliable and can be employed by practitioners to produce satisfactory estimations of LRs and soil salinity by considering the soil, crop, water quality, and irrigation system. Adoption of the model can improve water use efficiency and reduce groundwater pollution.

Maize Seedling Establishment, Grain Yield and Crop Water Productivity Response to Seed Priming and Irrigation Management in a Mediterranean Arid Environment

Water shortage is a major environmental stress that destructively impacts maize production, particularly in arid regions. Therefore, improving irrigation management and increasing productivity per unit of water applied are needed, especially under the rising temperature and precipitation fluctuations induced by climate change. Laboratory and field trials were carried out in the present study, which were aimed at assessing the possibility of promoting maize germination, growth, grain yield and crop water productivity (CWP) using seed priming under different irrigation regimes. Two seed priming treatments, i.e., hydro-priming and hardening versus unprimed seeds, were applied under four irrigation regimes, i.e., 120, 100, 80 and 60% of estimated crop evapotranspiration (ETc). The obtained results indicated that increasing irrigation water from 100% up to 120% ETc did not significantly increase grain yield or contributing traits, while it decreased CWP. Deficit irrigation of 80 and 60% ETc gradually decreased grain yield and all attributed traits. Seed priming significantly ameliorated seedlings’ vigor as indicated by earlier germination, higher germination percentage, longer roots and shoots, and heavier fresh and dry weight than unprimed seeds with the superiority of hardening treatment. Additionally, under field conditions, seed priming significantly increased grain yield, yield contributing traits and CWP compared with unprimed treatment. Interestingly, the results reflect the role of seed priming, particularly hardening, in mitigating negative impacts of drought stress and enhancing maize growth, grain yield and attributed traits as well as CWP under deficit irrigation conditions. This was demonstrated by a significant increase in grain yield and CWP under moderate drought and severe drought conditions compared with unprimed treatment. These results highlight that efficient irrigation management and seed priming can increase maize yield and water productivity in arid environments.

Assessing Seasonal Methane and Nitrous Oxide Emissions from Furrow-Irrigated Rice with Cover Crops

Improved irrigation management is identified as a potential mitigation option for methane (CH4) emissions from rice (Oryza sativa). Furrow-irrigated rice (FR), an alternative method to grow rice, is increasingly adopted in the Mid-South U.S. However, FR may provide a potential risk to yield performance and higher emissions of nitrous oxide (N2O). This study quantified the grain yields, CH4 and N2O emissions from three different water management practices in rice: multiple-inlet rice irrigation (MIRI), FR, and FR with cereal rye (Secale cereale) and barley (Hordeum vulgare) as preceding winter cover crops (FRCC). CH4 and N2O fluxes were measured from May to September 2019 using a static chamber technique. Grain yield from FR (11.8 Mg ha−1) and MIRI (12.0 Mg ha−1) was similar, and significantly higher than FRCC (8.5 Mg ha−1). FR and FRCC drastically reduced CH4 emissions compared to MIRI. Total seasonal CH4 emissions decreased in the order of 44 > 11 > 3 kg CH4-C ha−1 from MIRI, FR, and FRCC, respectively. Cumulative seasonal N2O emissions were low from MIRI (0.1 kg N2O-N ha−1) but significantly higher from FR (4.4 kg N2O-N ha−1) and FRCC (3.0 kg N2O-N ha−1). However, there was no net difference in global warming potential among FR, FRCC and MIRI. These results suggest that the increased N2O flux from furrow-irrigated rice may not greatly detract from the potential benefits that furrow-irrigation offers rice producers.

Field Scale Assessment of the TsHARP Technique for Thermal Sharpening of MODIS Satellite Images Using VENµS and Sentinel-2-Derived NDVI

Remotely sensed-based surface temperature is an important tool for crop monitoring and has great potential for improving irrigation management. However, current thermal satellite platforms do not display the fine spatial resolution required for identifying crop water status patterns at the field scale. The thermal sharpening (TsHARP) utility provides a technique for downscaling coarse thermal images to match the finer resolution of images acquired in the visible and near infrared bandwidths. This sharpening method is based on the inverse linear relationship between vegetation fraction calculated from the normalized difference vegetation index (NDVI) and land surface temperature (LST). The current study used the TsHARP method to sharpen low-resolution thermal data from the Moderate Resolution Imaging Spectrometer MODIS (1 km) to the finer resolution of Sentinel-2 (10 m) and Vegetation and Environment New micro-Spacecraft (VENµS) (5 m) visible-near infrared images. The sharpening methodology was evaluated at scene and field scales in southern Georgia and northern Mississippi, USA. A comparison of sharpened temperature was made with reference temperatures from Landsat-8 Operational Land Imager (OLI) in four different spatial resolutions (30, 60, 120, and 240 m) for method validation. Coarse resolution comparison on the dates in which imagery from both sensors were acquired on the same day resulted in average observed mean absolute error (MAE) of 1.63 °C, and R2 variation from 0.34 to 0.74. Temperature errors at the field scale ranged from 0.25 to 3.11 °C using both Sentinel-2 and VENµS. Sharpened maps at 120 and 60 m resolution showed the highest consistency for all fields and dates. Maps sharpened using VENµS images showed comparable or higher accuracy than maps sharpened using Sentinel-2. The superior performance coupled with the better revisit time indicates that the VENµS platform has high potential for frequent in-season crop monitoring. Further research with ground data collection is needed to explore field use limitations of this methodology, but these results give useful insights of potential benefits of implementing the TsHARP technique as a tool for crop stress monitoring.

Determining sensor-based field capacity for irrigation scheduling

Irrigated agriculture is a major consumer of freshwater and acreage of irrigated land has increased as producers have become increasingly reliant on irrigation to ensure adequate yields and reduce production risks. Information about the soil’s field capacity, or the water content after a saturated soil has drained for at least 24 h, is needed to determine the soil’s readily available water, or the amount of water that can be safely removed by plants. To address the challenges associated with using soil water sensors in highly variable soils, soil water measurements were included in ongoing cotton irrigation studies during the 2017 through 2019 growing seasons at the University of Missouri Fisher Delta Research Center near Portageville. Sensor-based field capacity values were compared among multiple commercially available sensors. Time-domain reflectometry sensors (Acclima TDR 315) were installed at four depths and five locations each of the three years. In addition, resistance-type sensors (Irrometer Watermark 200SS) were installed at five depths and five locations for two years. The goal was to provide guidance on how best to use soil moisture sensor data for irrigation management. Although water continued to move in the profile for more than 24 h after saturation, the average change between 24 and 48 h at each observed depth was < 0.015 m3 m−3. Observed values for coarser textured soils were much wetter after 24 h than expected based on published field capacity values and in one case, even a small difference in sensor location had a large effect on the observed field capacity. Furthermore, soil apparent electrical conductivity was highly correlated to measured field capacity, which could result in a much faster and less expensive way to estimate field capacity compared to collecting and analyzing soil cores. These findings demonstrate that the application of soil moisture sensors for irrigation management is site specific, and differences can be observed over short distances within a field. The research is continuing to better meet the needs of agricultural producers, consultants, research and extension personnel, and others for information to improve irrigation management.

Study of drought manifestation and its effect on the thermal regime of vegetation surface of crops under irrigation

Relevance of the topic. In recent years, irrigation management practices have faced new challenges related to climate change and the increase in the frequency and intensity of droughts in the southern regions. In addition, the latest technical and technological capabilities have emerged in irrigated agriculture to more effectively manage technological processes. All these processes occurring in real production and in the market of scientific and technical products, have determined the possibility and necessity of studying the influence of natural and agricultural conditions on the processes of energymass transfer in the "soil-plant-atmosphere" environment to improve management methods in modern irrigation conditions. Purpose of research is to investigate the features of formation of crop thermal regime in the conditions of air and soil drought manifestation under irrigation during 2018-2019. Research object. The research was carried out at the production fields of the farm “Freedom Farm International” in the Kakhovsky district of Kherson region during 2018-2019. Two experimental sites were equipped to carry out experimental studies on the crop rotation of "Gornostayevske -2". Methodology and Research methods. The methodology for the conduct of thermal water balance and phenological observations on the growth and development of crops at the experimental sites was applied. Standard field research techniques were used. Analytical, field, simulation modeling, mathematical statistics, geoinformation technologies (GIS), remote sensing of the Earth (RS) methods were used. Research results. As a result of the research in 2018-2019 a database of farm fields (crops, sowing dates, soil conditions, initial moisture reserves, irrigation machines and their characteristics) was created; phenological observations of plant growth and development were performed, heat and water balance calculations were made. It was established that to ensure optimal conditions for the use of thermal energy in different drought conditions, it is necessary to improve models and algorithms for operational planning of crop irrigation. Thus, to mitigate the influence of atmospheric drought, it is recommended to conduct refreshing irrigation, the timing of which can be determined on the basis of monitoring the temperature of the vegetation surface, using the data from the space images or ground-based observations. In addition, under drought conditions, in the case of applying water-balance methods used in irrigation management, it is necessary to adjust the biological coefficients of water consumption by crops, based on modeling the production process using the WOFOST model and identifying its parameters with the help of space images. The obtained new knowledge is aimed at improving irrigation management methods in modern conditions.

Reference Evapotranspiration Prediction for Smart Irrigation

Irrigation is the most critical process for agriculture, but irrigation is the largest consumer of fresh water and causes the loss of large quantities because of the inaccuracy in crop water estimation. Our proposed system aims to improve irrigation management by estimating the amount of water needed by the crop accurately and reduces the number of meteorological parameters needed for such estimation. Detection of the reference crop evapotranspiration (ETo) is the most critical process in crop water estimation, that is considered through our proposed solution by implementing machine learning models using neural networks and linear regression to predict daily ETo using climate data like temperature, humidity, wind speed, and solar radiation. Comparing our system results with FAO-56 Penman-Monteith ET0 and cropwat8.0 software as benchmark, show that our proposed system is better than the linear regression model, in terms of determination coefficient (R^2)=.9677 and root mean square error(RMSE) =.1809, while the multiple linear regression model achieved determination coefficient (R^2)=.68 and root mean square error(RMSE) =3.01. Our system then used the predicted ETo and Crop coefficient (Kc) from FAO, to estimate crop evapotranspiration (ETc) for precision irrigation target.

Special issue: improving irrigation management across the Ogallala aquifer, USA

Special issue: improving irrigation management across the Ogallala aquifer, USA

Parameterization of the AquaCrop model for simulating table grapes growth and water productivity in an arid region of Mexico

Currently, the AquaCrop model has been widely tested for many fruit/grain crops; root and tuber crops; leafy vegetables, or forage crops, but is restricted to annual herbaceous species, while deciduous crops have received less if no attention. In this context, this study aims to test for the first time the ability of the AquaCrop model to simulate canopy cover (CC), actual evapotranspiration (ETa), total soil water content (TWC), biomass (B) and fruit yield (FY) of table grapes vineyards (Vitis vinifera L., cvs. Perlette and Superior) at the Costa de Hermosillo, Sonora in Northwest Mexico. Observed weather and soil physical parameters, with measured crop parameters from an experiment conducted during 2005 were used to develop climate, soil and crop input files for AquaCrop and for calibrating the model. While collected data during the 2006 growing season were used to validate the model. The model adequately simulated CC, ETa and TWC during 2005 and 2006. The Root Mean Square Error (RMSE) between observed and measured CC, ETa and TWC were 5.18%, 0.46 mm/day and 10.11 mm during 2005, and 8.82%, 0.84 mm/day and 9.1 mm during 2006, respectively. The good accuracy of simulations of CC, ETa and TWC by the model have been confirmed by additional statistical parameters like the coefficient of determination (R2), The Mean Bias Error (MBE), the Willmott’s index of agreement (d) and the Nash–Sutcliffe Efficiency (NSE).For the B and FY simulations, the results showed that the model correctly reproduced the B and FY with NRMSE value of 8.8%. The estimated average value of FY (14.56 t/ha) for both seasons are in the range of the potential yield (14–18 t/ha) of table grapes in the irrigated Costa de Hermosillo in northwest Mexico.After the validation of the AquaCrop model, it was used to evaluate the irrigation scheduling by the farmer as well as to assess the water productivity computed as the ratio of crop production to crop water use. The results showed that, the recommended irrigation by the model was about 547 mm and 509 mm, which it is about half of that applied by the farmer (1006 mm and 929 mm) during 2005 and 2006, respectively. This large difference, which represents approximately 54% and 57% of the irrigation supply, is lost through deep percolation and could be saved without vegetation suffering from water stress while maintaining the same yield. The high loss of water by percolation affects significantly the water productivity (WP), which decreases from 3.22 to 1.74 kg/m3 if we consider the transpiration (WPTr), and the sum of ETa and Percolation (WPETa+Pr) for WP computations, respectively. Consequently, the AquaCrop model can be used as an operational tool by decision makers and growers to improve irrigation management. This is of crucial importance in arid and semi-arid regions where water is becoming increasingly scarce.

Effects of salinity and sodicity on the seasonal dynamics of actual evapotranspiration and surface energy balance components in mature micro-irrigated pistachio orchards

This article presents findings from a field research study conducted in the San Joaquin Valley of California in 2016–2018 to appraise the effects of soil salinity and sodicity on evapotranspiration and energy balance components of micro-irrigated pistachio orchards. Actual evapotranspiration (ETa) and tree physiologic parameters were measured during consecutive growing seasons in mature orchards grown on non-saline and saline/sodic soils. Salinity and sodicity decreased pistachio water use by about 30%, with ETa reductions varying along the growing season. Accurate information on the dynamics of ETa and energy balance components along the growing season can improve water management for nut orchards exposed to long-term saline-sodic conditions. Results show that the main driver of ETa was the net radiation (Rn), which supplied most of the energy to vaporize water, irrespective of the growth period and level of salinity/sodicity. Field observations revealed that Rn was lower for salt-affected trees due to smaller canopies, which intercepted less light than non-saline trees. Secondarily, the exchange of sensible heat (H) between the ambient air and tree canopies was affected by the interaction between salinity–sodicity and seasonality. Early in the season, salinity and sodicity affected ETa mainly through the reduced canopy growth, which decreased the available energy (Rn–G) for ETa and reduced the water uptake as a result of the lower soil water potential. Late in the season, an increase in H and a decrease in the contribution of the aerodynamic component (β coefficient) to the latent heat flux (LE) occurred, which determined a further reduction of ETa due to a physiological response. The decrease in the β coefficient during the late season was associated with a direct impact of ion accumulation on leaf functionality. Collecting data on the contribution of the aerodynamic component to LE offers a low-cost method to detect and quantify physiological stress, while providing useful information for managing irrigation in salt-affected orchards. The results presented in this article provide insights to improve irrigation management of salt-affected pistachio through integration of weather measurements, energy balance components, and plant-based parameters.

Soil characterization of horticultural greenhouses in northern Uruguay

The crops under cover constitute a very important production mode within the horticulture of northern Uruguay, but there is little information on the current state of these soils. The objective of this study was to characterize some physicochemical properties of soils under greenhouses with tomato cultivation (Solanum lycopersicum) and to relate them to soil management practices. For this, 30 commercial properties were selected from the horticultural area of northern Uruguay near the city of Salto. Soil samples were taken from the most representative greenhouse, at the end of the crop's productive period, from each farm. Additional information was collected such as area by production mode, number of consecutive crops in the same greenhouse, yield obtained in the harvest under study, and soil management. The high values observed for organic soil carbon (OSC), pH, available P, and mineral N suggest the need to decrease the application rates or frequency of organic amendments and/or fertilizers. The high concentrations of Na+ in the surface and NO3- in the subsurface horizons indicate the need to improve irrigation management. This information could be used as a basis to outline aspects to delve into, for rational management of amendments and nutrients, and irrigation water in soils under these production systems.

SUAS Remote Sensing of Vineyard Evapotranspiration Quantifies Spatiotemporal Uncertainty in Satellite-Borne ET Estimates

Small Unmanned Aerial Systems (sUAS) show promise in being able to collect high resolution spatiotemporal data over small extents. Use of such remote sensing platforms also show promise for quantifying uncertainty in more ubiquitous Earth Observation System (EOS) data, such as evapotranspiration and consumptive use of water in agricultural systems. This study compares measurements of evapotranspiration (ET) from a commercial vineyard in California using data collected from sUAS and EOS sources for 10 events over a growing season using multiple ET estimation methods. Results indicate that sUAS ET estimates that include non-canopy pixels are generally lower on average than EOS methods by &gt;0.5 mm day−1. sUAS ET estimates that mask out non-canopy pixels are generally higher than EOS methods by &lt;0.5 mm day−1. Masked sUAS ET estimates are less variable than unmasked sUAS and EOS ET estimates. This study indicates that limited deployment of sUAS can provide important estimates of uncertainty in EOS ET estimations for larger areas and to also improve irrigation management at a local scale.