Irrigation Decisions Research Articles

Grapevine stem water potential estimation based on sensor fusion

Estimating vine water status is essential for achieving the desired balance between wine grape quality and yield in viticulture management and irrigation planning. The growing demands of smart farming for extracting meaningful information from field data to support irrigation decisions may be facilitated using field monitoring technology along with advanced modeling algorithms. This research took place in a Vitis Vinifera cv. “Sauvignon Blanc” experimental plot within a commercial vineyard. The main objective was to generate a water stress estimation model for wine grapevines based on data fusion from multiple sensors. Data were collected using sensors from five monitored grapevines, each treated with a different water stress regime. The sensors provided measurements of trunk and fruit growth, leaf temperature, and soil water content (SWC) at depths of 20 and 40 cm. Additionally, meteorological station in the vineyard recorded the temperature, relative humidity, wind speed, and solar radiation. A daily-scale multivariate time series was composed using the data derived from the sensors. Stem water potential (SWP) values were measured for the monitored vines and analyzed against the multivariate time series of factors to define their interrelations and interactions. Finally, a prediction model for SWP estimation was defined using boosted regression trees (BRT) algorithm, with an optimized set of factors. Validation was conducted using comparative statistics between a randomly selected test set and a predicted set of SWP values achieved using the trained BRT model. After processing the data and eliminating most of the factors due to multicollinearity, the model was composed of daily maximum leaf temperature (LT), SWC at noon at 40 cm, tree water deficit (TWD), SWC daily amplitude at 20 cm, water input, vapor pressure deficit, and phenological stages. The highest contributor to the BRT model was maximum LT (44.5%), followed by SWC at noon at 40 cm (16.9%) and TWD (16%). The model performance was estimated using various measures and resulted in a correlation of 0.9 between the estimated SWP values and the test set. No significant differences were found between their means (t = -0.31, p-value = 0.76) and distributions (D = 0.13, p-value = 0.77). The RMSE was 0.16 MPa which corresponds to a 12% error when normalized to the range of the test set. The suggested sensor fusion approach for estimating SWP is a promising technique that integrates representative factors of the soil–water-plant-atmosphere continuum; it should be further examined in different climate regimes and vine varieties.

Simulation modeling for effective management of irrigation water for winter wheat

Lack of water resources, low irrigation efficiency, and inappropriate irrigation decisions severely restrict agricultural production in arid and semi-arid regions. Therefore, rapid and accurate decision-making regarding crop irrigation in real time is necessary. This study optimized irrigation scheduling by using information on different meteorological years and obtained the average soil water content (0–60 cm) before each irrigation, the corresponding irrigation time, and the water available for irrigation. The relative development speed of winter wheat and the amount of water available for irrigation were considered, and a dynamic irrigation water limit model was constructed. Winter wheat field experiments over 3 years (2016–2019) were followed by an evaluation of the regional applicability of the decision support system for the agrotechnology transfer model. A long short-term memory network effectively predicted air temperature and solar radiation; the R2 and root mean square error values were 0.802–0.964% and 12.53–23.9%, respectively. Public weather forecasts can be used to accurately predict rainfall, with 87.3% and 57% accuracy rates for forecasts of no rain and rain, respectively. Compared with traditional irrigation, the use of this dynamic irrigation lower limit for irrigation forecasting can increase yield and attendant net benefits. When two and three irrigation treatments were applied during the winter wheat growth period, the 3-year average yield increased by 10.3% and 4.4%, respectively, and the net benefit increased by 19.1% and 7.4%, respectively. The proposed method avoids relying on only field experiments to determine the irrigation lower limit and allows for the effective implementation of optimized irrigation schedules and the dynamic correction of irrigation plans in arid and semi-arid areas.

The effect of smart fertigation systems on chilli grown in a greenhouse for urban farming

AbstractReasonable fertigation methods are essential for plant growth, as excessive or inadequate water and fertilizer supply can lead to plant diseases, resource consumption, or even crop production reduction. These problems are common to local farmers in Malaysia, especially for large‐scale chilli cultivation using traditional irrigation approaches in rural areas. These problems need to be addressed, as chilli are one of Malaysia's high‐cash crops with massive local demands. Realizing an efficient irrigation control approach using an innovative fertigation method is a potential solution to tackle such problems. This paper proposes a smart fertigation system for chilli cultivation in greenhouses in urban areas to increase chilli production and improve irrigation management. The proposed system utilized the fuzzy logic method for intelligent irrigation decisions, where the decision is based on the input data provided by soil moisture, temperature, and humidity sensors. In addition, the proposed system can monitor and control irrigation in the greenhouse remotely through a website that was developed using the Hostinger platform. The results showed that the fuzzy‐based fertigation approach produced approximately 50% irrigation application efficiency and a higher growth gradient and chilli yield than the traditional approach in both cycles.

An IoT Application for Effective Management of Agriculture Resources

Several issues related to traditional agriculture have hindered the growth of nation. Thus, solution to this mode of agriculture is adoption of smart agriculture. Therefore, the aim of this research is to make the agriculture smart by adopting innovative and advanced technologies such as Internet of things (IoT) and Wireless Sensor Network (WSN) enabled automation in agriculture. In recent times, IoT and WSN based technologies can be applied in several research fields. IoT enables various applications employed in crop growth monitoring and selection of irrigation decision support system. The technology like Raspberry Pi used recently in irrigation system of agriculture for improvement of crop productivity. This research paper focuses on various parameters of IoT related to agriculture such as soil moisture sensor, floodwater detector, temperature tracking sensor, humidity and light intensity sensor, etc. The sensors are associated to web server through raspberry pi controller. It detects the each activity of field and transfer the information to web server. The result outcomes assist the former to know about the actual scenario of field in real time through buzzer and can smartly do watering and showering fertilizer to the field in very efficient manner without trying too hard and quality of the crops will be get improved.

Statistical verification of 16-day rainfall forecast for a farmers advisory service in Pakistan

Rainfall forecast is useful for farmers to avoid expensive irrigation decisions both in rain-fed and irrigated agricultural areas. In developing countries, farmers have limited knowledge of weather forecast information sources and access to technology such as the internet and smartphones to make use of these forecasts. This paper presents a case of developing Farmers Advisory Service (FAS) in Pakistan that is based on rainfall forecast data. The analysis emphasizes on statistical verification of 16-day rainfall forecast data from a global weather forecast model (Global Forecast System). In-situ data from 15 observatories maintained by Pakistan Meteorological Department in Khyber Pakhtunkhwa province has been considered for verification. Scores of various indicators are calculated for the rainfall forecast ranging from simple forecasts of dichotomous outcomes to forecasts of a continuous variable. A sensitivity analysis is also performed to understand how scores of dichotomous indicators vary by changing the threshold to define a rainfall event and forecast lead time interval. The quality of forecast varies across the stations based on the selected skill scores. The findings of verification, sensitivity analysis, and attributes of FAS provide insight into the process of developing a decision support service for the farmers based on the global weather forecast data.

Precision Irrigation Management Using Machine Learning and Digital Farming Solutions

Freshwater is essential for irrigation and the supply of nutrients for plant growth, in order to compensate for the inadequacies of rainfall. Agricultural activities utilize around 70% of the available freshwater. This underscores the importance of responsible management, using smart agricultural water technologies. The focus of this paper is to investigate research regarding the integration of different machine learning models that can provide optimal irrigation decision management. This article reviews the research trend and applicability of machine learning techniques, as well as the deployment of developed machine learning models for use by farmers toward sustainable irrigation management. It further discusses how digital farming solutions, such as mobile and web frameworks, can enable the management of smart irrigation processes, with the aim of reducing the stress faced by farmers and researchers due to the opportunity for remote monitoring and control. The challenges, as well as the future direction of research, are also discussed.

Sustainable agricultural water management incorporating inexact programming and salinization-related grey water footprint

In arid and semi-arid regions, improper irrigation activities not only exacerbate water shortages, but also lead to environmental pollution such as soil salinization that hinders crop growth and agricultural sustainability. There has been a lack of agricultural water management tools that could support agricultural water management with salinization-related grey water footprints being considered and associated uncertainties being addressed. In this study, salinization-related grey water footprints were measured through accounting for relationships among irrigation, soil salinity, evapotranspiration and crop yield, and then incorporated into an agricultural water management model for supporting environmentally sound irrigation decisions. Such an agricultural water management model was also characterized by a newly proposed generalized fuzzy interval fractional programming (GFIFP) method that could address ratio problems of two objectives and dual uncertainties. The developed methodology coupling the GFIFP method and grey water footprints was applied to an irrigation region in northwestern China where water scarcity and soil salinization hindered local development. Five credibility levels corresponding to decision makers' varied satisfactory degrees over water availability, and nine weight coefficients of possibility and necessity measures were considered. Results showed that, in order to reduce negative environmental impacts while increasing economic benefits, more irrigation water should be allocated to wheat and sunflower rather than corn. When the credibility level is 0.5 and weight coefficient is 0.9, the system efficiency would be the highest. Compared to the benchmark year of 2018, this scenario would generate [0.330, 6.647] billion yuan more benefits and [16.0, 133.9] million m3 less grey water footprints. Compared to three conventional approaches, GFIFP could provide decisions with more flexibility and less environmental impacts. The developed approach could also be applied to agricultural water management problems in other areas aiming at reducing grey water footprints and enhancing environmental benefits under uncertainty.

Deficit irrigation-based growth control of maize plants using hybrid model predictive / trellis decoding algorithm

Deficit irrigation approaches have been applied in mitigation of pressure on global freshwater availability. Data from growth of plants under different water stress conditions allows the development of growth models taking into account irrigation scheduling. In this work, a model predictive control approach combined with a modified trellis decoding algorithm is applied to control growth of maize plants under deficit irrigation conditions by specifying the irrigation schedule required to achieve specific total leaf length at the end of a specified growth period. The brute force-based algorithm allows the control of growth to within 8.5 % of the desired final total leaf length over a growth period of seven days. The designed control algorithm is an adaptable solution that can be applied to different optimization goals related to plant growth and water consumption, and allows the user to evaluate best-case and worst-case scenarios in advance, which supports decision making with regard to irrigation decisions.

Field Data Forecasting Using LSTM and Bi-LSTM Approaches

Water, an essential resource for crop production, is becoming increasingly scarce, while cropland continues to expand due to the world’s population growth. Proper irrigation scheduling has been shown to help farmers improve crop yield and quality, resulting in more sustainable water consumption. Soil Moisture (SM), which indicates the amount of water in the soil, is one of the most important crop irrigation parameters. In terms of water usage optimization and crop yield, estimating future soil moisture (forecasting) is an essentially valuable task for crop irrigation. As a result, farmers can base crop irrigation decisions on this parameter. Sensors can be used to estimate this value in real time, which may assist farmers in deciding whether or not to irrigate. The soil moisture value provided by the sensors, on the other hand, is instantaneous and cannot be used to directly compute irrigation parameters such as the best timing or the required water quantity to irrigate. The soil moisture value can, in fact, vary greatly depending on factors such as humidity, weather, and time. Using machine learning methods, these parameters can be used to predict soil moisture levels in the near future. This paper proposes a new Long-Short Term Memory (LSTM)-based model to forecast soil moisture values in the future based on parameters collected from various sensors as a potential solution. To train and validate this model, a real-world dataset containing a set of parameters related to weather forecasting, soil moisture, and other related parameters was collected using smart sensors installed in a greenhouse in Chiang Mai province, Thailand. Preliminary results show that our LSTM-based model performs well in predicting soil moisture with a 0.72% RMSE error and a 0.52% cross-validation error (LSTM), and our Bi-LSTM model with a 0.76% RMSE error and a 0.57% cross-validation error. In the future, we aim to test and validate this model on other similar datasets.

A study of the conversion of different evaporation pans in South China based on the extreme learning machine model

ABSTRACT Evaporation is important basic information for irrigation decision making in water resources management. Developing countries usually use a small pan to observe surface evaporation, but the relationship between evaporation in different small pans is not sufficiently clear. In this paper, we use an extreme learning machine (ELM) model to predict and convert E20 (diameter 0.20 m) and E601 (diameter 0.62 m) pan data for 38 meteorological stations in southern China. Firstly, we obtained the best combination of meteorological parameters for forecasting E20 and E601, respectively, and we also found that the accuracy of the model can be significantly improved by adding pan data. Secondly, we found that during the conversion between E20 and E601, the model performance when using E601 data to predict the E20 evaporation is better than that when using E20 data to predict the E601 evaporation. Finally, the geographical factors were analysed, and the model performance was found to be relatively poor in the coastal area and the North–South junction.

Simulating Canopy Temperature Using a Random Forest Model to Calculate the Crop Water Stress Index of Chinese Brassica

The determination of crop water status has positive effects on the Chinese Brassica industry and irrigation decisions. Drought can decrease the production of Chinese Brassica, whereas over-irrigation can waste water. It is desirable to schedule irrigation when the crop suffers from water stress. In this study, a random forest model was developed using sample data derived from meteorological measurements including air temperature (Ta), relative humidity (RH), wind speed (WS), and photosynthetic active radiation (Par) to predict the lower baseline (Twet) and upper baseline (Tdry) canopy temperatures for Chinese Brassica from 27 November to 31 December 2020 (E1) and from 25 May to 20 June 2021 (E2). Crop water stress index (CWSI) values were determined based on the predicted canopy temperature and used to assess the crop water status. The study demonstrated the viability of using a random forest model to forecast Twet and Tdry. The coefficients of determination (R2) in E1 were 0.90 and 0.88 for development and 0.80 and 0.77 for validation, respectively. The R2 values in E2 were 0.91 and 0.89 for development and 0.83 and 0.80 for validation, respectively. Our results reveal that the measured and predicted CWSI values had similar R2 values related to stomatal conductance (~0.5 in E1, ~0.6 in E2), whereas the CWSI showed a poor correlation with transpiration rate (~0.25 in E1, ~0.2 in E2). Finally, the methodology used to calculate the daily CWSI for Chinese Brassica in this study showed that both Twet and Tdry, which require frequent measuring and design experiment due to the trial site and condition changes, have the potential to simulate environmental parameters and can therefore be applied to conveniently calculate the CWSI.

"Smart Crop-Field Monitoring and Irrigation System Based On IoT"

Agriculture is critical to the growth of agrarian countries like India. Agriculture-related issues have long been a roadblock to the country's growth. Smart agriculture, which involves updating existing agricultural techniques, is the only answer to this issue. As a result, the suggested approach seeks to use automation and IoT technology to make agriculture smart. Crop growth monitoring and selection, irrigation decision assistance, and other applications are enabled by the Internet of Things (IoT). To modernize and increase agricultural production, a Microcontroller Pi-based IoT watering solution is recommended. The major purpose of this research is to grow crops with the least amount of water possible. Most farmers spend hours throughout the fields in order to focus on groundwater resources to plants at the proper time. Groundwater contamination should be enhanced, and the complexity of the system circuit should be reduced. The protocol determines the quantity of water necessary analysis of the results obtained from the sensors. Six sensors detect the temperature of the food of the soil, but also the precipitation, temperatures, and duration of daylight each day, and send the data to the mobile node. Dependent on these characteristics, the proposed systems must calculate the quantity of water needed for irrigation. The usage of Crop Monitoring (PA) with virtualization is the system's key advantage, as it will evaluate and use of water nitrates while boosting crop yields and aid in analyzing field condition.

IRRIGATION DECISIONS AND USE OF GROUNDWATER IN BANGLADESH: PERSPECTIVES ON SOME EVOLVING CRISIS

Groundwater is one of the vital rechargeable natural capitals, which serves as the main water source around the globe. In Bangladesh, about 79% of the cultivated area relies on groundwater irrigation. The perspective piece is intended to scrutinize the irrigation decisions and groundwater use in Bangladesh from published sources. The paper points that the reason behind the excessive groundwater mining is imbedded in the heavy dependence of Bangladesh on irrigated dry season rice to feed its huge populaces. Bangladesh has obtained rice self-sufficiency by foregoing environmental costs. The farming communities as a whole rarely adopted water-saving modern technologies and keep paddy fields abundantly irrigated as the marginal cost of irrigation is near zero. The irrigation water price is commonly pre-negotiated on the basis of per unit area for the whole crop season without considering the volume of water. The perspective suggests that the ignorance about the importance of groundwater and the consequent over-extraction cannot be stopped without thorough policy reforms. Bangladesh should move toward institutionalizing irrigation, design a participatory framework involving all the stakeholders in a water management network and piloting efficient water models across the country.

Water shortages, irrigation frequency, and preference for technologies and agricultural services: The case of Jordan*

AbstractThe use of improved technologies has been encouraged to improve irrigation on farms, especially in drought‐prone areas. However, farmers' irrigation decisions may be rather motivated by a desire to reduce risk of crop loss than to reduce water use. Using the case of Jordan, we contribute to the water‐saving debate by examining whether current irrigation frequency is influenced by past experiences of losses due to water shortage and whether preferences for technologies and irrigation advisory services are mediated by water shortage experiences. Our data are based on a survey of 304 fruit farms in the highlands that were all using drip irrigation, a popular way to “save” water globally. We find that farms that faced losses due to water shortages in the past are more likely to irrigate more frequently. More frequent irrigators who have such shortages are more likely to prefer receiving irrigation advisory information rather than upgrading technologies, while more frequent irrigators who have not faced such shortages are more likely to prefer upgrading irrigation technologies. Results suggest that irrigation management is motivated by risk reduction, not just by water conservation. Irrigation advisory services, hitherto neglected, may be an important component of agricultural water management in Jordan.

Irrigation decision model for tomato seedlings based on optimal photosynthetic rate

Soil moisture is a major environmental factor that influences tomato growth and development. Suitable soil moisture not only increases tomato production but also saves irrigation water. This paper developed an irrigation decision model, called soil moisture regulation model, for optimizing growth of tomato seedlings while saving water. The data used to establish models were collected from a multi-gradient nested experiment, in which temperature, photosynthetic photon flux density (PPFD), carbon dioxide (CO2) concentration and soil moisture were variables and the corresponding photosynthetic rate was measured. Subsequently, a prediction model of tomato photosynthetic rate was constructed using support vector regression (SVR) algorithm. With photosynthetic rate prediction model as fitness function, genetic algorithm (GA) was used to find the optimal soil moisture under each combination of the above environmental factors. Finally, back propagation neural network (BPNN) algorithm was used to establish a decision model of tomato irrigation, which could provide the optimal soil moisture under current environment. For the soil moisture regulation model constructed here, the coefficient of determination was 0.9738, the mean square error of the test set was 1.51×10-5, the slope of the verified straight line was 0.9752, and the intercept was 0.00916. This model demonstrated high precision, which thereby provides a theoretical basis for accurate irrigation control in the greenhouse facility environment. Keywords: irrigation, decision model, soil moisture, tomato, photosynthetic rate, machine learning DOI: 10.25165/j.ijabe.20211405.6148 Citation: Wan X B, Li B, Chen D Y, Long X Y, Deng Y F, Wu H R, et al. Irrigation decision model for tomato seedlings based on optimal photosynthetic rate. Int J Agric & Biol Eng, 2021; 14(5): 115–122.

Determination of irrigation scheduling thresholds based on HYDRUS-1D simulations of field capacity for multilayered agronomic soils in Alabama, USA

The use of soil matric potential (ψ) sensors to support irrigation decisions has become common practice among many producers. However, ψ values at which irrigation should be initiated (ψlim) based on a pre-defined irrigation depth is still lacking. The main objectives of this study were: (i) evaluate the impact of different negligible drainage flux on estimated ψ at field capacity (ψfc) using HYDRUS 1D simulations; (ii) identify ψlim values and its corresponded irrigation depth under different soil profile depth at representative soil types in Northwest and Southeast Alabama. The ψ-θ relation at field capacity (ψfc and θfc, respectively) were estimated by a numerical internal drainage flux experiment for multilayered soils using HYDRUS-1D software simulations. Among the different negligible drainage fluxes (qfc) tested, a qfc value of 0.01 and 0.025 cm d−1 yielded the best results for the soil located at Northwest and Southeast Alabama, respectively. For a soil water depletion of 35% and a soil profile depth of 0.6 m, the ψlim ranged from − 103 to − 133 kPa for the soils located at Northwest Alabama and − 38 to − 51 kPa for soils located in Southeast Alabama. It returned an irrigation depth varying from 20 to 24 mm for Northwest Alabama soils and 15–33 mm for Southeast Alabama soils. For a same irrigation depth, it was observed that the ψlim increased (became less negative) as soil profile depth considered for irrigation calculations increased. Additionally, if the same pre-defined irrigation depth is used during the entire growing season, there is a high change that plants could be under stress due to the high level of water deficit. Using the same irrigation depth during the entire crop growth season could be a flawed irrigation management strategy; therefore, irrigation depth should dynamically change over the growth season as the plant roots reach deeper soil layers.

FPGA-Embedded Smart Monitoring System for Irrigation Decisions Based on Soil Moisture and Temperature Sensors

The basic need common to all living beings is water. Less than 1% of the water on earth is fresh water and water use is increasing daily. Agricultural practices alone require huge amounts of water. The drip technique improved the efficiency of water use in irrigation and initiated the introduction and development of fertigation, the integrated distribution of water and fertilizer. The past few decades have seen extensive research being carried out in the area of development and evaluation of different technologies available to estimate/measure soil moisture to aid in various applications and to facilitate the use of drip irrigation for users and farmers. In this technology, plant moisture and temperature are accurately monitored and controlled in real time over roots in the form of droplets, by developing smart monitoring system to save water and avoid water waste using drip irrigation technology. Water is delivered to the roots drop by drop, which saves water as well as prevents plants from being flooded and decaying due to excess water released by irrigation methods such as flood irrigation, border irrigation, furrow irrigation, and control basin irrigation. Drip irrigation with an embedded intelligent monitoring system is one of the most valuable techniques used to save water and farmers’ time and energy. In this paper, we design an embedded monitoring system based in the integrated 65 nm CMOS technology in agricultural practices which would facilitate agriculture and enable farmers to monitor crops. Hence, to demonstrate the feasibility, a prototype was constructed and simulated with modelsim and validated with nclaunch the both tools from Cadence, as well as implementation on the FPGA board, was be performed.

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

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

The potential for moral hazard behavior in irrigation decisions under crop insurance

AbstractThe design of federal crop insurance has attracted the interest and attention of many economists and policymakers in the United States. Moral hazard is a frequently cited concern, as insurance may reduce the incentive for farmers to manage properly their agricultural operation. Moral hazard could play a role in irrigation decisions by incentivizing a farmer to choose a riskier irrigation management strategy, potentially affecting the long‐term sustainability of water resources. We use numerical simulation to determine if insurance coverage affects incentives on seasonal irrigation use. Results show that the potential for moral hazard at current costs and policy parameters is low, but that there is potential for moral hazard behavior when the cost of irrigation is high. Hence, conservation policies that increase the cost of irrigation cost (e.g., a water tax) may amplify the potential for moral hazard. In most cases, crop insurance provides a secondary effect that reduces irrigation use in addition to the direct water tax effect, although this reduction comes at increased taxpayer expense. However, it is possible that irrigation use increases under crop insurance. Therefore, there is a need for the explicit consideration of the unintended side effects of crop insurance on farmers’ irrigation water use decisions, particularly in regions experiencing water scarcity and rising marginal costs of irrigation.

Local irrigation response to ethanol expansion in the High Plains Aquifer

Global ethanol production has grown rapidly due to national renewable fuel programs. Concern has grown over impacts that land conversion and crop displacement driven by ethanol feedstock production might have on water resources. In this paper, we examine irrigation decisions of agricultural producers in the Kansas portion of the High Plains Aquifer in response to local ethanol market expansion. To identify the effects of ethanol expansion on irrigation decisions, we examine field-level data on irrigation water use, irrigated acreage, and crop decisions for the years 2003–2017 for nearly 23,000 fields in Kansas. We measure the response of three irrigation decisions, (i) irrigated acreage, (ii) irrigation per acre, and (iii) total water use to the introduction and capacity expansion of an ethanol plant. We find that ethanol market expansion did lead to increases in irrigation water use. Specifically, a 10 % increase in ethanol capacity within 50KM increases annual water use by 0.22 % per field (4.8 acre-inches/field). We predict that ethanol markets accounted for about 4% of total irrigated water use in 2017.