Irrigation Decisions Research Articles

Evaluation of a new irrigation decision support system in improving cotton yield and water productivity in an arid climate

Optimizing irrigation scheduling through a Decision Support System has shown promise to improve crop yield and water productivity in irrigated agriculture in an arid climate. The effects of an irrigation scheduling method on cotton (Gossypium hirsutum L.) yield and water productivity were investigated in Qira Oasis, China from 2016–2018. The Decision Support System for Irrigation Scheduling (DSSIS) was based on forecasted rainfall and water stress index simulated by the Root Zone Water Quality Model (RZWQM2). A field experiment was conducted to test the viability of the DSSIS in 2016. The design of the experiment was a randomized complete that included two factors and two levels for each factor: (i) irrigation scheduling method—DSSIS–based (DSS) and soil moisture sensor–based (SMS), and (ii) irrigation level—full irrigation (FI) and deficit irrigation (DI, 75 % of FI). Implementation of the DSS led to significant increases in seed cotton yield [1.05 Mg ha–1 (32 %)] and water productivity [1.64 kg ha–1 mm–1 (20 %)] compared to the SMS. Compared to DI, FI significantly increased cotton yield [0.69 Mg ha–1 (20 %)] but had no significant effect on water productivity. In general, the higher water productivity under DSS (vs. SMS) was attributed to the reduced water stress and increased seed cotton yield. While the DSS–FI treatment provided the greatest seed cotton yield (4.55 Mg ha–1) and net income (US $3427 ha–1), the highest water productivity (10.09 kg ha–1 mm–1) was achieved under the DSS–DI treatment. Water use under DSS–DI treatment significantly decreased by 51 mm (10 %) and 23 mm (5 %), respectively, compared to DSS–FI and SMS–FI treatments. Therefore, our results demonstrated that the DSS with deficit irrigation could maintain cotton yield and improve water productivity under an arid desert climate.

Model Application for Sustainable Agricultural Water Use

With the growing population and climate change, increasing demands for water are intensifying competition between agricultural stakeholders. Since the mid-20th century, numerous crop models and modeling techniques have emerged for the quantitative assessment of cropping systems. This article introduces a collection of articles that explore current research in model applications for sustainable agricultural water use. The collection includes articles from model development to regional and field-scale applications addressing management effects, model uncertainty, irrigation decision support systems, and new methods for simulating salt balances. Further work is needed to integrate data science, modern sensor systems, and remote sensing technologies with the models in order to investigate the sustainability of agricultural systems in regions affected by land-use change and climate change.

Water use indicators and economic analysis for on-farm irrigation decision: A case study of a super high density olive tree orchard

Increasing the efficiency of on-farm water use requires wise decisions on the irrigation system, the irrigation strategy and the method to schedule irrigation, among other factors related to water management. Since the early 2000s, the water productivity approach has been widely used to address this issue. It provides useful indicators to both the biophysical water productivity and the economic performance of irrigation. Analysis of the literature, however, shows both confusion on the use of terms and lack of agreement on the equations. We have addressed the rational use of the water productivity approach for the irrigator to improve both biophysical and economic water productivity at the field scale. We also addressed the increasing use of the water footprint approach at the field scale. The literature shows a lack of consensus on the reliability of the conceptual framework behind that approach. We focused on its potential for irrigation decision making, and concluded that it is not advantageous, as compared to the water productivity approach, for assessing on-farm water use. In addition, we show a case study of a super high density olive orchard which analyses the joint use of economic water productivity indicators and both production and profit functions to improve decision making.

Development Experience of a Context-Aware System for Smart Irrigation Using CASO and IRRIG Ontologies

The rapid development of information and communication technologies and wireless sensor networks has transformed agriculture practices. New tools and methods are used to support farmers in their activities. This paper presents a context-aware system that automates irrigation decisions based on sensor measurements. Automatic irrigation overcomes the water shortage problem, and automatic sensor measurements reduce the observational work of farmers. This paper focuses on a method for developing context-aware systems using ontologies. Ontologies are used to solve heterogeneity issues in sensor measurements. Their main goal is to propose a shared data schema that precisely describes measurements to ease their interpretations. These descriptions are reusable by any machine and understandable by humans. The context-aware system also contains a decision support system based on a rules inference engine. We propose two new ontologies: The Context-Aware System Ontology addresses the development of the context-aware system in general. The Irrigation ontology automates a manual irrigation method named IRRINOV®. These ontologies reuse well-known ontologies such as the Semantic Sensor Network (SSN) and Smart Appliance REFerence (SAREF). The decision support system uses a set of rules with ontologies to infer daily irrigation decisions for farmers. This project uses real experimental data to evaluate the implementation of the decision support system.

Estimating crop coefficients for apple orchards with varying canopy cover using measured data from twelve orchards in the Western Cape Province, South Africa

The FAO-56 crop coefficient (Kc) approach is widely used for making irrigation decisions. Allen and Pereira (2009) extended this approach by developing a method for estimating Kc using a density coefficient (Kd), which is estimated from the fraction of ground covered by vegetation and plant height. In this study we evaluated this method using detailed measurements of transpiration (T), evapotranspiration (ET), soil attributes, weather, and tree physiological variables in 12 apple (Malus domestica Borkh.) orchards in the Western Cape Province of South Africa. Mid-summer canopy cover of the orchards was less than 20 % in young non-bearing and exceeded 60 % in mature full-bearing orchards. Data were collected over three growing seasons (October 2014 to May 2017) in orchards planted to the Golden Delicious/Reinders®, Cripps Pink, Cripps Red, and Rosy Glow apple cultivars. The original Allen and Pereira (A&P) method significantly overestimated the basal crop coefficients (Kcb) by on average 47 % in mature and 103 % in young orchards, respectively. However, improved Kcb estimates were obtained by adjusting the ratio of the resistances (i.e. rl /100) in the A&P method, where rl is the mean leaf resistance and 100 s/m is the typical resistance for annual crops. We defined a resistance parameter “α” for apple orchards which is equivalent to the bulk canopy resistance of a well-watered tree. Replacing rl /100 with rl /α, and using the measured mean rl and other biophysical measurements to solve the A&P equation for α gave a value ∼ 37 s/m. The improved Kcb values were used to derive the orchard Kc taking into account the contribution of cover crops whose transpiration was measured using miniature stem heat balance sap flow gauges. The seasonal total transpiration (T) estimated as T = Kcb x ETo, where ETo is the reference ET closely matched the measured values with a RMSE (root of the mean square error) of ∼ ±16 mm. Therefore, using the mean canopy resistance which is representative of apple trees in the A&P method has the potential to accurately predict both the crop coefficients and water use of apple orchards from planting until full bearing age.

Water Irrigation Decision Support System for Practical Weir Adjustment Using Artificial Intelligence and Machine Learning Techniques

This research proposes a decision support system for weir sluice gate level adjusting. The proposed system, named AWARD (Appropriate Weir Adjustment with Water Requirement Deliberation), is composed of three modules, which are (1) water level prediction, (2) sluice gates setting period estimation, and (3) sluice gates level adjusting calculation. The AWARD system applies an artificial neural network technique for water level prediction, a fuzzy logic control algorithm for sluice gate setting period estimation, and hydraulics equations for sluice gate level adjusting. The water requirements and supplies are deducted from the field-survey and telemetry stations in Chiang Rai Province, Thailand. The results show that the proposed system can accurately estimate the water volume. Water level prediction shows high accuracy. The standard error of prediction (SEP) is 2.58 cm and the mean absolute percentage error (MAPE) is 7.38%. The sluice gate setting period is practically adjusted. The sluice gate level is adjusted according to the water requirement.

A Decision Support System for Irrigation Management: Analysis and Implementation of Different Learning Techniques

Automatic irrigation scheduling systems are highly demanded in the agricultural sector due to their ability to both save water and manage deficit irrigation strategies. Elaborating a functional and efficient automatic irrigation system is a very complex task due to the high number of factors that the technician considers when managing irrigation in an optimal way. Automatic learning systems propose an alternative to traditional irrigation management by means of the automatic elaboration of predictions based on the learning of an agronomist (DSS). The aim of this paper is the study of several learning techniques in order to determine the goodness and error relative to expert decision. Nine orchards were tested during 2018 using linear regression (LR), random forest regression (RFR), and support vector regression (SVR) methods as engines of the irrigation decision support system (IDSS) proposed. The results obtained by the learning methods in three of these orchards have been compared with the decisions made by the agronomist over an entire year. The prediction model errors determined the best fitting regression model. The results obtained lead to the conclusion that these methods are valid engines to develop automatic irrigation scheduling systems.

Editorial—Managing and Planning Water Resources for Irrigation: Smart-Irrigation Systems for Providing Sustainable Agriculture and Maintaining Ecosystem Services

Smart-irrigation systems are a hot topic in irrigation management. Satellite imaging, sensors and controls, communication technologies and irrigation decision models are readily available. The price of the required technology is being reduced year after year, and its implementation in agriculture gives real-time information that allows for more accurate management of water resources. Even so, the adaptation of existing technologies to particular situations that the irrigation management is facing in different agro-environmental contexts is needed. This Special Issue addresses the application of different smart-irrigation technologies in four different research areas: (1) remote sensing-based estimates of crop evapotranspiration, (2) Information and Communication Technologies (ICTs) for smart-irrigation, (3) precision irrigation models and controls, and (4) the price of natural resources. The nine papers presented in this special issue cover a wide range of practical applications, and this editorial summarizes each of them.

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.

IoT-based Irrigation Management for Smallholder Farmers in Rural Sub-Saharan Africa

Ensuring food security has become a challenge in Sub-Saharan Africa (SSA) due to combined effects of climate change, high population growth, and relying on rainfed farming. Governments are establishing shared irrigation infrastructure for smallholder farmers as part of the solutions for food security. However, the irrigated farms often failed to achieve the expected crop yield. This is partly due to lack of water management system in the irrigation infrastructure. In this work, IoT-based irrigation management system is proposed after investigating problems of irrigated farmlands in three SSA countries, Ethiopia, Kenya, and South Africa as case studies. Resource-efficient IoT architecture is developed that monitors soil, microclimate and water parameters and performs appropriate irrigation management. Indigenous farming and expert knowledge, regional weather information, crop and soil specific characteristics are also provided to the system for informed-decision making and efficient operation of the irrigation management system. In SSA, broadband connectivity and cloud services are either unavailable or expensive. To tackle these limitations, data processing, network management, irrigation decisions and communication to the farmers are carried out locally, without the involvement of any back-end servers. Furthermore, the use of green energy sources and resource-aware intelligent data analysis algorithm is studied. It is expected that an intelligent data analysis will help to discover new knowledge that support further development of agricultural expertise. The proposed IoT-based irrigation management system is expected to contribute towards long term and sustainable high crop yield with minimum resource consumption and impact to the biodiversity around the case farmlands.

Targeting Small-Scale Irrigation Investments using Agent-Based Modeling: Case Studies in Mali and Niger

Small-scale irrigation has been identified as a potential adaptation strategy for climate change and boosting food security and livelihoods in dry regions. This study presents the analysis of the potential adoption of small-scale irrigation in two West African countries (Mali and Niger) by using a spatially explicit analytical framework. It underscores the need for strategically investing in the management of ground and surface water resources for the development of small-scale irrigation systems in the two countries. The study implemented an agent-based modeling technique to simulate small-scale irrigation decisions at the district and national level. The results revealed that, while small-scale irrigation can increase crop productivity in both countries, its adoption may be constrained by water scarcity and tensions in water allocation. Strategic water resource development plans should be established to ensure efficient and sustainable irrigation schemes, especially for areas with high potential profitability.

Using Machine Learning to Integrate On-Farm Sensors and Agro-Meteorology Networks into Site-Specific Decision Support

HighlightsMachine learning can incorporate a variety of data from low-cost sensors and estimate actual ET by comparison with short-term, higher-cost measurements.On-farm weather monitoring can be leveraged to estimate site-specific crop-water requirements.Expanding spatial coverage of weather and actual ET through on-farm monitoring will facilitate localization and leverage publicly available weather data to guide irrigation decisions and improve irrigation water management.Abstract. One of the basic challenges to adopting science-based irrigation scheduling is providing reliable, site-specific estimates of actual crop water demand. While agro-meteorology networks cover most agricultural production areas in the U.S., widely spaced stations represent regionally specific, rather than site-specific, conditions. A variety of low to moderate cost commercial weather stations are available but do not provide directly useful information, such as actual evapotranspiration (ETa), or the ability to incorporate additional sensors. We demonstrate that machine learning methods can provide real-time, site-specific information about ETa and crop water demand using on-farm sensors and public weather information. Two years of field experiments were conducted at four irrigated field sites with crops including snap beans, alfalfa, and pasture. On-farm data were compared to publicly available data originating at nearby agro-meteorology network stations. The machine learning procedure can robustly estimate ETa using data from a few basic sensors, but the resulting estimate is sensitive to the range of conditions that are used as training data. The results demonstrate that machine learning can be used with affordable sensors and publicly available data to improve local estimates of crop water demand when high-quality measurements can be co-located for short periods of time. Supplementary sensors can also be integrated into a tailored monitoring plan to estimate crop stress and other operational considerations. Keywords: Agro-meteorology, Irrigation requirement, Machine learning, Site-specific Irrigation.

Theory and Development of a VRI Decision Support System: The USDA-ARS ISSCADA Approach

HighlightsMulti-faceted research efforts converged to an automated irrigation decision support system (DSS).Low-cost, solar-powered, wireless plant abiotic and biotic stress sensors were developed to aid the DSS.Low-cost, accurate TDR soil water sensors and a wireless node and gateway system were developed for the DSS.Sensor systems and research-based algorithms were integrated into an automated irrigation DSS and control system.Abstract. Variable-rate irrigation (VRI) is now possible with every new center pivot irrigation system sold, either using sector (speed) control or both sector and zone (radial along the pipeline) control. However, decision support systems able to generate a prescription for spatially varying irrigation based on crop water need have lagged far behind VRI equipment. Irrigation based on crop water need has been shown to increase both crop water productivity and nutrient use efficiency, meaning that an effective VRI decision support system (DSS) could improve profitability while conserving resources. In this article, we report separately on a VRI DSS using sensor-based plant and soil water feedback as implemented in four U.S. states. This article describes the genesis and development of the Irrigation Scheduling Supervisory Control and Data Acquisition (ISSCADA) system, of the integral plant and soil sensors, and of its wireless sensor network subsystems, as well as the role of multi-location research efforts and cooperative research and development agreements in the development of the needed plant and soil sensors and the ISSCADA and wireless sensor network systems. Keywords: Crop water productivity, Decision support system, Product development, Sensors, Variable-rate irrigation, VRI.

A Variable-Rate Irrigation Decision Support System for Corn in the U.S. Eastern Coastal Plain

HighlightsA decision support system using the USDA-ARS Irrigation Scheduling and Supervisory Control and Data Acquisition (ISSCADA) system was evaluated for spatially managing corn irrigation in the U.S. Eastern Coastal Plain.The ISSCADA system was compared to traditional scheduling methods based on measured soil water potentials.The ISSCADA system with feedback on allowable soil water depletion shows potential as a tool for growers for managing variable-rate irrigation systems.Abstract. Variable-rate irrigation (VRI) systems are capable of applying different water depths both in the direction of travel and along the length of the irrigation system. VRI systems maybe useful for improving crop water management and efficiency. Although VRI technology is available and has high grower interest, it has had limited adoption. To address this, researchers have developed a decision support system that uses remote sensing of plant, soil, and microclimate to schedule VRI irrigations. In this research, we evaluated the use of the USDA-ARS Irrigation Scheduling and Supervisory Control and Data Acquisition (ISSCADA) system for spatially managing corn irrigation in the U.S. Eastern Coastal Plain. The ISSCADA system consists of center pivot mounted infrared thermometers (IRT) to measure crop canopy temperatures and in situ soil water sensors. An integrated crop water stress index (iCWSI) was calculated from the canopy temperatures. The ISSCADA system analyzes the iCWSI and soil water measurements to provide an irrigation recommendation. The ISSCADA system was evaluated using (1) iCWSI values and (2) a hybrid ISSCADA system that incorporated both iCWSI values and soil water depletion criteria. These ISSCADA treatments were compared to traditional irrigation management using measured soil water potentials. The ISSCADA system was evaluated for four years. In 2016 and 2017, corn yields and water use efficiency were not significantly different between the irrigation treatments due to adequate rainfall during the growing season. In 2018 and 2019, mid-season drought conditions and sporadic rainfall patterns required frequent irrigations. In both years, the irrigation treatment corn yields were not significantly different from each other but were greater than the rainfed yields. In 2018, the irrigation treatments produced corn yields of 10.7, 10.4, and 10.1 Mg ha-1 for the hybrid, ISSCADA, and SWP treatments, respectively. Over the four-year study, the water use efficiencies of the irrigation treatments were not significantly different from each other or the rainfed treatment and ranged from 16.6 to 22.7 kg ha-1 mm-1. In the two years that the hybrid ISSCADA system was used for managing irrigations, it produced higher corn yields and required less irrigation than the standard ISSCADA treatments. Results from this experiment will help to evaluate and refine the ISSCADA system to provide a tool for growers to use in managing spatial irrigation with VRI systems. Keywords: Crop water stress, Decision support system, Variable rate irrigation.

Precision Agriculture and Irrigation: Current U.S. Perspectives

Highlights.Precision agriculture (PA) applications in irrigation are stymied by lack of decision support systems.Modern PA relies on sensor systems and near real-time feedback for irrigation decision support and control.Sophisticated understanding of biophysics and biological systems now guides site-specific irrigation.The internet of things (IOT) enables new ways to increase yield per unit of water used and nutrient use efficiency. Keywords: Crop water productivity, Decision support system, Internet of things, Remote sensing, SCADA, Soil water content.

Real-time monitoring of water level and storage dynamics of irrigation tank using IoT

Abstract The internet of things (IoT), an emerging technological marvel, consists of a group of physical objects such as vehicles, machines and sensors to monitor and transfer data over the internet with much less human to machine interaction. It relies on a host of technologies like application programming interfaces (API), which in turn, help the devices to get connected with the internet. Efficient irrigation tank management requires a strong database on continuous water level dynamics for irrigation decision-making. Real-time tank water level monitoring is possible through an IoT device by integrating sensors and microcontroller that can send the water level data to the cloud. Google sheet is used to store the water level data that can be viewed using a web application as well as a mobile application. The contour map of the study tank is used to develop the stage (water level) vs volume curve. The volume of water present in the tank at any time can be arrived at for any tank water level using the above curve. The developed device can provide real-time continuous water level data with low cost and simple infrastructure, thus aiding tank water management.

Determining threshold values for root-soil water weighted plant water deficit index based smart irrigation

Plant water deficit index (PWDI) represents the extent of water stress by relating soil moisture to the ability of a plant to take up water including consideration of the relative distribution of soil water to roots. However, for a smart irrigation decision support system, we are challenged in determining reliable thresholds of PWDI to initiate irrigation events to achieve predetermined yield and/or water use efficiency (WUE) targets. Taking drip irrigated maize and sprinkler irrigated alfalfa as examples, field experiments were conducted to investigate the choice and effects of PWDI thresholds. The results indicated that, with increasing PWDI thresholds, irrigation times and quantity of water, as well as crop transpiration, growth, and yield, were all significantly limited while WUE was enhanced except under extremely stressed conditions. To disconnect the unpredictable effects of other factors, yield and WUE were normalized to their corresponding potential values. Within the experimentally determined range of PWDI, relative yield and WUE were described with linear functions for maize, and linear and quadratic functions for alfalfa, allowing identification of the most efficient threshold value according to the objective parameter of choice. The method described can be adopted in smart irrigation decision support systems with consideration of spatial variability and after further verification and improvement under more complicated situations with various crop types and varieties, environmental conditions, cultivation modes, and wider or dynamic PWDI thresholds allowing regulated deficit irrigation.

MONITORING RESEARCH OF FIELD SOIL MOISTURE TO PROVIDE IRRIGATION MANAGEMENT ON THE BASE OF AN EXPERIMENTAL AND CALCULATION METHOD

The article highlights the actual problems of monitoring studies of soil moisture and meteorological indicators for informational support of irrigation management systems. On the basis of literature analysis it is shown that soil moisture control can be carried out both directly on the results of soil moisture measurements and using calculated methods. In the presence of automatically obtained meteorological forecasting data, irrigation decisions are made using an experimental calculation method. Monitoring studies are part of the experiment and are used as feedback in soil moisture control.
 The purpose of the work is to develop an information component of soil moisture monitoring and meteorological indicators in the field to ensure a proper decision-making regarding irrigation by the experimental-calculation method.
 Soil moisture measurement is recommended using a variety of soil moisture sensors by indirect methods of determination. Various tensiometers, dielectric and resistive sensors can be used as sensors. They provide the feedback for irrigation management. The calculation part consists of the decision criterion for the beginning of irrigation, the balance method of calculating soil moisture or moisture reserves in the soil (or a multilayered mathematical model of moisture transfer in the presence of sufficient input parameters for that) using automatic meteorological forecast.
 The article is illustrated by the results of a laboratory model experiment and field research with automated measurement data transmission and feedback implementation in soil moisture control. The model laboratory experiment was used to test the design, technical and technological parameters of the equipment for automatic monitoring and testing of the experimental-calculation method. Practical forecasts, calculations and data acquisition of soil moisture and meteorological indicators for the implementation of feedback during the irrigation management are considered based on the example of a corn field in one of the experimental farms.
 The results of the laboratory experiment and field studies show the effectiveness of predicting soil moisture by this method. Monitoring data of soil moisture and meteorological indicators is the feedback. They are automatically transmitted and improve the accuracy of irrigation recommendations and allow for quick adjustments to forecast calculations. It is recommended to make daily soil moisture correction for direct automated field measurements using ground sensors.
 Further research in this area is to use one-dimensional multilayer models of moisture transfer. They provide accurate results but require more input parameters.

Multimodal neural network with clustering-based drop for estimating plant water stress

Decision-making with low-cost data is an attractive approach in the field of agriculture as it aids to solve the difficulty of inheriting advanced cultivation technologies. To provide expertise in the decision-making process for stress cultivation, precision irrigation based on plant water stress is required to steadily produce high-quality fruits. Single low-cost data namely, single-modal data, is used in the traditional approach. However, for advanced cultivation, multimodal data such as physiological and meteorological data is required. In this study, we propose a multimodal neural network with clustering-based drop (C-Drop) for accurate estimation of plant water stress, as it is an index for irrigation decision-making, using plant image and environmental data. Our proposed method extracts temporal multimodal features from leaf wilting features (physiological data) using environmental features (meteorological data) as an attention mechanism of a multimodal neural network that includes long short-term memory layers. Moreover, the proposed neural network with C-drop realizes a novel end-to-end deep learning architecture in consideration of environmental conditions. On evaluating this method against the existing methods, the proposed method was found to improve the accuracy of plant water stress estimation by 21% for mean absolute error and root-mean-squared error, thereby indicating that this method is precise and stable for the plant water stress estimation. The performance of our proposed method to support precision irrigation will allow new-age farmers to produce high-quality fruits steadily.

Controversies in Midday Water Potential Regulation and Stomatal Behavior Might Result From the Environment, Genotype, and/or Rootstock: Evidence From Carménère and Syrah Grapevine Varieties.

Controversies exist regarding the iso/anisohydric continuum for classifying plant water-use strategies. Isohydricity has been argued to result from plant–environment interaction rather than it being an intrinsic property of the plant itself. Discrepancies remain regarding the degree of isohydricity (σ) of plants and their threshold for physiological responses and resistance to drought. Thus, the aim of this study was to evaluate the isohydricity of the grapevine varieties Syrah and Carménère under a non-lethal water deficit progression from veraison from two different locations, the Cachapoal Valley (CV) and Maipo Valley (MV), in central Chile and with different rootstock only in Syrah. For this purpose, the midday stem water potential (Ψmds) regulation and stomatal responses to drought, leaf traits related to pressure–volume curves, stomatal sensitivity to ABA, cavitation threshold, and photosynthetic responses were assessed. A higher atmospheric water demand was observed in the CV compared to the MV, with lower Ψmds values in the former for both varieties. Also, the σ values in Carménère were 1.11 ± 0.14 MPa MPa-1 and 0.68 ± 0.18 MPa MPa-1 in the CV and MV, respectively, and in Syrah they were 1.10 ± 0.07 MPa MPa-1 in the CV and 0.60 ± 0.10 MPa MPa-1 in the MV. Even though similar variations in σ between locations in both varieties were evident, Carménère plants showed a conserved stomatal response to Ψmds in both study sites, while those of Syrah resulted in a higher stomatal sensitivity to Ψmds in the site of lower σ. Besides the differences in seasonal weather conditions, it is likely that the different rootstock and clonal variability of each season in Syrah were able to induce coordinated changes in σ, Ψgs12, and osmotic potential at full turgor (π0). On the other hand, irrespective of the σ, and given the similarity between the π0 and Ψgs12 in leaves before drought, it seems that π0 could be a convenient tool for assessing the Ψmds threshold values posing a risk to the plants in order to aid the irrigation decision making in grapevines under controlled water deficit. Finally, water deficits in vineyards might irreversibly compromise the photosynthetic capacity of leaves.