Optimal irrigation water allocation using a genetic algorithm under various weather conditions

Growing water scarcity, due to growing populations and varying natural conditions, puts pressure on irrigation systems, which often are the main consumptive water users. Therefore, water resources management to improve the allocation of limited water supplies is essential. In this study, a non-linear programming optimization model with an integrated soil/water balance is developed to determine the optimal reservoir release policies and the optimal cropping pattern around Doroudzan Dam in the South-West of Iran. The proposed model was solved using a genetic algorithm (GA). Four weather conditions were identified by combining the probability levels of rainfall, evapotranspiration and inflow. Moreover, two irrigation strategies, full irrigation and deficit irrigation were modeled under each weather condition. The results indicate that for all weather conditions the total farm income and the total cropped area under deficit irrigation were larger than those under full irrigation. In addition, our results show that when the weather conditions and the availability of water changes the optimal area under corn and sugar beet decreases sharply. In contrast, the change in area cropped with wheat is small. It is concluded that the optimization approach has been successfully applied to Doroudzan Dam region. Thus, decision makers and water authorities can use it as an effective tool for such large and complex irrigation planning problems

Development of soft computing and applications in agricultural and biological engineering

Soft computing is a set of “inexact” computing techniques, which are able to model and analyze very complex problems. For these complex problems, more conventional methods have not been able to produce cost-effective, analytical, or complete solutions. Soft computing has been extensively studied and applied in the last three decades for scientific research and engineering computing. In agricultural and biological engineering, researchers and engineers have developed methods of fuzzy logic, artificial neural networks, genetic algorithms, decision trees, and support vector machines to study soil and water regimes related to crop growth, analyze the operation of food processing, and support decision-making in precision farming. This paper reviews the development of soft computing techniques. With the concepts and methods, applications of soft computing in the field of agricultural and biological engineering are presented, especially in the soil and water context for crop management and decision support in precision agriculture. The future of development and application of soft computing in agricultural and biological engineering is discussed

Optimal reservoir operation using Nash bargaining solution and evolutionary algorithms

Optimizing reservoir operation is critical to ongoing sustainable water resources management. However, different stakeholders in reservoir management often have different interests and resource competition may provoke conflicts. Resource competition warrants the use of bargaining solution approaches to develop an optimal operational scheme. In this study, the Nash bargaining solution method was used to formulate an objective function for water allocation in a reservoir. Additionally, the genetic and ant colony optimization algorithms were used to achieve optimal solutions of the objective function. The Mahabad Dam in West Azerbaijan, Iran, was used as a case study site due to its complex water allocation requirements for multiple stakeholders, including agricultural, domestic, industrial, and environmental sectors. The relative weights of different sectors in the objective function were determined using a discrete kernel based on the priorities stipulated by the government (the Lake Urmia National Restoration Program). According to the policies for the agricultural sector, water allocation optimization for different sectors was carried out using three scenarios: (1) the current situation, (2) optimization of the cultivation pattern, and (3) changes to the irrigation system. The results showed that the objective function and the Nash bargaining solution method led to a water utility for all stakeholders of 98%. Furthermore, the two optimization algorithms were used to achieve the global optimal solution of the objective function, and reduced the failure of the domestic sector by 10% while meeting the required objective in water-limited periods. As the conflicts among stakeholders may become more common with a changing climate and an increase in water demand, these results have implications for reservoir operation and associated policies

Identifying efficient Nitrate reduction strategies in the Upper Danube

Nitrogen losses in the form of Nitrate (N-NO3) from point and diffuse sources of pollution are recognized to be the leading cause of water body impairment throughout Europe. Implementation of conservation programs is perceived as being crucial for restoring and protecting the good ecological status of freshwater bodies. The success of conservation programs depends on the efficient identification of management solutions with respect to the envisaged environmental and economic objectives. This is a complex task, especially considering that costs and effectiveness of conservation strategies depend on their locations. We applied a multi-objective, spatially explicit analysis tool, the R-SWAT-DM framework, to search for efficient, spatially-targeted solution of Nitrate abatement in the Upper Danube Basin. The Soil Water Assessment Tool (SWAT) model served as the nonpoint source pollution estimator for current conditions as well as for scenarios with modified agricultural practices and waste water treatment upgrading. A spatially explicit optimization analysis that considered point and diffuse sources of Nitrate was performed to search for strategies that could achieve largest pollution abatement at minimum cost. The set of optimal spatial conservation strategies identified in the Basin indicated that it could be possible to reduce Nitrate loads by more than 50% while simultaneously provide a higher income

A Game Theory Approach for Conjunctive Use Optimization Model Based on Virtual Water Concept

In this study to allocate the agricultural and environmental water, considering virtual water concept, a multi-objective optimization model based on NSGA-II is developed. The objectives consist of equity maximization, agricultural benefit maximization for each region, maximization of green water utilization and finally minimization of environmental shortage. Then a cooperative game (Grand Coalition) model is presented by forming all possible coalitions. By the game model including Nucleolus, Proportional Nucleolus, Normal Nucleolus and Shapley methods, the benefit is reallocated based on all Pareto optimal solutions obtained from multi-objective optimization model. Then using two famous fallback bargaining methods, Unanimity and q-Approval, preferable alternative (solution) for each of the cooperative games is determined. Finally, based on the obtained benefit for each selected alternatives, the two most beneficial alternatives are chosen. The proposed methodology applied for water allocation of Minoo-Dasht, Azad-Shahr and Gonbad-Kavoos cities in Golestan province, Iran for a 3-year period as a case study. Also, eight crops including Wheat, Alfalfa, Barley, Bean, Rice, Corn, Soya, and Cotton are selected based on local experts’ recommendations. The models’ results indicated no significant difference between the grand coalition model and the multi-objective optimization model in terms of the average cultivation area (a relative change of 2.1%), while lower agricultural water allocation occurred for the grand coalition model (about 10.35 percent average) compared with the multi-objective optimization model. It is also observed that more agricultural benefit gained by the grand coalition model (32 percent average). Finally, it is found that Wheat and Corn hold the most rates of import and export, respectively, and Rice was the crop which has the least shortage of production to supply food demand

Predictive fertilization models for potato crops using machine learning techniques in Moroccan Gharb region

Given the influence of several factors, including weather, soils, land management, genotypes, and the severity of pests and diseases, prescribing adequate nutrient levels is difficult. A potato’s performance can be predicted using machine learning techniques in cases when there is enough data. This study aimed to develop a highly precise model for determining the optimal levels of nitrogen, phosphorus, and potassium required to achieve both high-quality and high-yield potato crops, taking into account the impact of various environmental factors such as weather, soil type, and land management practices. We used 900 field experiments from Kaggle as part of a data set. We developed, evaluated, and compared prediction models of k-nearest neighbor (KNN), linear support vector machine (SVM), naive Bayes (NB) classifier, decision tree (DT) regressor, random forest (RF) regressor, and eXtreme gradient boosting (XGBoost). We used measures such as mean average error (MAE), mean squared error (MSE), R-Squared (RS), and R2Root mean squared error (RMSE) to describe the model’s mistakes and prediction capacity. It turned out that the XGBoost model has the greatest R2, MSE and MAE values. Overall, the XGBoost model outperforms the other machine learning models. In the end, we suggested a hardware implementation to help farmers in the field

Estimating Effective Soil Hydraulic Properties Using Spatially Distributed Soil Moisture and Evapotranspiration

With the development of many earth-observing remote sensing (RS) platforms, spatially distributed remote sensing products are becoming critical inputs to many hydrologic and meteorological models. Remotely sensed soil moisture (SM) and evapotranspiration (ET) including ground-based data have the potential to be used for estimating pixel-scale soil hydraulic parameters. However, only a few studies have been conducted to better understand the impact of assimilating both SM and ET in estimating soil hydraulic properties of the root zone. In this study, we used inverse modeling based on the Noisy Monte Carlo Genetic Algorithm by linking RS SM and ET derived from the Surface Energy Balance Algorithm for Land for estimating pixel-scale effective soil hydraulic properties. Walnut Creek (Iowa), Brown (Illinois), and Lubbock (Texas) test sites were selected to assess the performance of this approach from point to satellite scales using synthetic and validation experiments. For comparison purposes, inverse modeling results were analyzed under three scenarios (ET only, SM only, and SM + ET in the optimization criteria). These results showed that considering both SM and ET components improved the estimations of effective soil hydraulic properties and reduced their uncertainties better than SM or ET only. Overall, although uncertainty exists, our proposed SM + ET based scheme performed well in estimating effective soil hydraulic properties at multiple spatial scales (point, airborne, and satellite footprints) under various hydroclimatic conditions

by integrating deep learning, mechanistic model and field observations

학위논문(박사) -- 서울대학교대학원 : 농업생명과학대학 협동과정 농림기상학, 2022. 8. Youngryel Ryu.Rice (Oryza sativa) is a vital cereal crop that feeds more than 50% of the world population. However, the traditional anaerobic management leads rice production to consume ~40% of the irrigation water and emit ~10% of the global anthropogenic methane. A new paradigm for sustainable rice farming is urgently required amid challenges from increasing food demand, water scarcity, and reducing greenhouse gases emissions. Rice plants transpire considerable water overnight. Saving nighttime water loss is desirable but first need to understand the underlying mechanism of nocturnal stomatal opening. Apart from the night, optimizing daytime management is pivotal for designing an environmentally sustainable rice farming system. In a long-term strategy, detailed and reliable crop type map is compulsory to upscale new leaf level findings and site level methods to regional or global scale. Therefore, in this dissertation, we improved mechanistic understanding of nocturnal stomatal conductance in rice plants (Chapter II); provided an interdisciplinary and heuristic approach for designing an environmentally sustainable rice farming system with a case study in South Korea (Chapter III); and developed a new crop type referencing method by mining off-the-shelf Google Street View images to map crop types (Chapter IV). In chapter II, we proposed a “coordinated leaf trait” hypothesis to explain the ecological mechanism of nocturnal stomatal conductance (gsn) in rice. We conducted an open-field experiment by applying drought, nutrient deficiency, and the combined drought-nutrient deficiency stress. We found that gsn was neither strongly reduced by drought nor consistently increased by nutrient deficiency. With abiotic stress as a random effect, gsn was strongly positively correlated with nocturnal respiration (Rn). Notably, gsn primed early morning photosynthesis, as follows: Rn (↑) → gsn (↑) → gsd (daytime stomatal conductance) (↑) → A (assimilation) (↑). This photosynthesis priming effect diminished after mid-morning. Leaves were cooled by gsn as follows: gsn (↑) → E (transpiration) (↑) → Tleaf (leaf temperature) (↓). However, our results clearly suggest that evaporative cooling did not reduce Rn cost. Our results indicate that gsn is more closely related to carbon respiration and assimilation than water and nutrient availability, and that leaf trait coordination (Rn − gsn − gsd − A) is likely the primary mechanism controlling gsn. In chapter III, we aimed to increase current crop yield, reduce irrigation water consumption, and tackle the dilemma to simultaneously reducing CH4 and N2O emissions in a flooded rice production system. By proposing a heuristic and holistic method, we optimized farm management beyond previous most emphasized irrigation regimes while also exploring niches from other pivotal options regarding sowing window, fertilization rate, tillage depth, and their interactions. Specifically, we calibrated and validated the process-based DNDC model with five years of eddy covariance observations. The DNDC model later was integrated with the non-dominated sorting genetic algorithm (NSGA-III) to solve the multi-objective optimization problem. We found that the optimized management would maintain or even increase current crop yield to its potential (~10 t/ha) while reducing more than 50% irrigation demand and GHGs (CH4 & N2O) emissions. Our results indicate that earlier sowing window and improvements on irrigation practice together would be pivotal to maximizing crop yield while sustaining environmental benefits. We found that the optimal fraction of non-flooded days was around 54% of growing season length and its optimal temporal distributions were primarily in vegetative stages. Our study shows that the present farm yield (8.3-8.9 t/ha) in study site not only has not achieved its potential level but also comes at a great environmental cost to water resources (604-810 mm/yr) and GHGs emissions (CH4: 186-220 kg C/ha/yr; N2O: 0.3-1.6 kg C/ha/yr). Furthermore, this simple method could further be applied to evaluate the environmental sustainability of a farming system under various climate and local conditions and to guide policymakers and farming practices with comprehensive solutions. In chapter IV, we apply a convolutional neural network (CNN) model to explore the efficacy of automatic ground truthing via Google Street View (GSV) images in two distinct farming regions: Illinois and the Central Valley in California. Ground reference data are an essential prerequisite for supervised crop mapping. The lack of a low-cost and efficient ground referencing method results in pervasively limited reference data and hinders crop classification. In this study, we demonstrate the feasibility and reliability of our new ground referencing technique by performing pixel-based crop mapping at the state level using the cloud-based Google Earth Engine platform. The mapping results are evaluated using the United States Department of Agriculture (USDA) crop data layer (CDL) products. From ~130,000 GSV images, the CNN model identified ~9,400 target crop images. These images are well classified into crop types, including alfalfa, almond, corn, cotton, grape, rice, soybean, and pistachio. The overall GSV image classification accuracy is 92% for the Central Valley and 97% for Illinois. Subsequently, we shifted the image geographical coordinates 2–3 times in a certain direction to produce 31,829 crop reference points: 17,358 in Illinois, and 14,471 in the Central Valley. Evaluation of the mapping results with CDL products revealed satisfactory coherence. GSV-derived mapping results capture the general pattern of crop type distributions for 2011–2019. The overall agreement between CDL products and our mapping results is indicated by R2 values of 0.44–0.99 for the Central Valley and 0.81–0.98 for Illinois. To show the applicational value of the proposed method in other countries, we further mapped rice paddy (2014–2018) in South Korea which yielded fairly well outcomes (R2=0.91). These results indicate that GSV images used with a deep learning model offer an efficient and cost-effective alternative method for ground referencing, in many regions of the world.쌀(오리자 사티바)은 세계 인구의 50% 이상을 먹여 살리는 중요한 곡물 작물이다. 그러나 전통적인 혐기성 관리는 쌀 생산으로 관개수의 40%를 소비하고 전 세계 인공 메탄의 10%를 배출한다. 식량 수요 증가, 물 부족, 온실가스 배출 감소 등의 과제 속에서 지속 가능한 벼농사를 위한 새로운 패러다임이 시급하다. 벼는 하룻밤 사이에 상당한 양의 물을 내뿜는다. 야간 수분 손실을 줄이는 것은 바람직하지만, 먼저 야간 기공 개방의 기본 메커니즘을 이해할 필요가 있다. 야간과 별도로 주간 경영의 최적화는 환경적으로 지속 가능한 벼농사 시스템을 설계하는 데 매우 중요하다. 장기 전략에서, 새로운 잎 수준 발견과 현장 수준 방법을 지역적 또는 전역적 규모로 상향 조정하려면 상세하고 신뢰할 수 있는 작물 유형 맵이 필수적이다. 따라서, 본 논문에서 우리는 벼농사의 야간 기공 전도도에 대한 기계적 이해를 향상시켰다(제2장). 환경적으로 지속 가능한 벼농사 시스템을 설계하기 위한 학제 간 및 휴리스틱 접근법 제공(제3장). 그리고 새로운 작물 유형 참조 방법을 개발했다. 기성품인 Google Street View 이미지를 마이닝하여 자르기 유형을 매핑합니다. 2장에서 우리는 벼의 야행성 기공 전도도(gsn)의 생태학적 메커니즘을 설명하기 위해 "협동된 잎 형질" 가설을 제안했습니다. 가뭄, 영양 결핍 및 가뭄-영양소 결핍 복합 스트레스를 적용하여 노지 실험을 수행했습니다. 우리는 gsn이 가뭄에 의해 크게 감소하지도 않고 영양 결핍에 의해 지속적으로 증가하지도 않는다는 것을 발견했습니다. 무생물적 스트레스를 무작위 효과로 사용하여 gsn은 야간 호흡(Rn)과 강한 양의 상관관계를 보였습니다. 특히, gsn은 Rn(↑) → gsn(↑) → gsd(주간 기공 전도도)(↑) → A(동화)(↑)와 같이 이른 아침 광합성을 프라이밍했습니다. 이 광합성 프라이밍 효과는 오전 중반 이후에 감소했습니다. 잎은 gsn에 의해 다음과 같이 냉각되었습니다: gsn(↑) → E(증산)(↑) → Tleaf(잎 온도)(↓). 그러나 우리의 결과는 증발 냉각이 Rn 비용을 감소시키지 않았다는 것을 분명히 시사합니다. 우리의 결과는 gsn이 물 및 영양소 가용성보다 탄소 호흡 및 동화와 더 밀접하게 관련되어 있으며 잎 형질 조정(Rn - gsn - gsd - A)이 gsn을 제어하는 주요 메커니즘일 가능성이 있음을 나타냅니다. 제3장에서 우리는 현재의 작물 수확량을 늘리고 관개 용수 소비를 줄이며 침수된 쌀 생산 시스템에서 CH4와 N2O 배출량을 동시에 줄이는 딜레마를 해결하는 것을 목표로 했다. 휴리스틱하고 전체론적 방법을 제안함으로써, 우리는 이전에 가장 강조되었던 관개 체제를 넘어 농장 관리를 최적화함과 동시에 파종 창, 수정률, 경작 깊이 및 이들의 상호 작용과 관련된 다른 중추적 옵션의 틈새를 탐색했다. 구체적으로, 우리는 5년간의 와류 공분산 관찰로 프로세스 기반 DNDC 모델을 교정하고 검증했다. DNDC 모델은 나중에 다중 객관적 최적화 문제를 해결하기 위해 비지배적 정렬 유전 알고리듬(NSGA-III)과 통합되었다. 우리는 최적화된 관리를 통해 50% 이상의 관개 수요와 GHG(CH4 & N2O) 배출량을 줄이면서 현재 농작물 수확량을 잠재력(~10t/ha)까지 유지하거나 증가시킬 수 있다는 것을 발견했습니다. 우리의 결과는 더 이른 파종 기간과 관개 관개 관행의 개선이 환경적 이익을 유지하면서 농작물 수확량을 최대화하는 데 중추적일 것이라는 것을 보여준다. 우리는 홍수 없는 날의 최적 부분이 성장기 길이의 약 54%였고 최적의 시간 분포는 주로 식물 단계에 있다는 것을 발견했다. 우리의 연구는 연구 현장의 현재 농장 수확량(8.3-8.9 t/ha)이 잠재적 수준을 달성했을 뿐만 아니라 수자원(604-810 mm/yr)과 GHGs 배출(CH4: 186-220 kg C/ha/yr; N2O: 0.3-1.6 kg C/ha/yr)에 막대한 환경 비용을 초래한다는 것을 보여준다. 또한, 이 간단한 방법은 다양한 기후 및 지역 조건 하에서 농업 시스템의 환경 지속 가능성을 평가하고 정책 입안자와 농업 관행을 포괄적인 해결책으로 안내하는 데 추가로 적용될 수 있다. 제4장에서는 컨볼루션 신경망(CNN) 모델을 적용하여 두 개의 구별되는 농업 지역에서 구글 스트리트 뷰(GSV) 이미지를 통해 자동 지상 트러싱의 효과를 탐구한다. 일리노이와 캘리포니아의 센트럴 밸리. 지상 참조 데이터는 감독된 작물 매핑을 위한 필수 전제 조건이다. 저렴하고 효율적인 지상 참조 방법이 없기 때문에 참조 데이터가 광범위하게 제한되고 작물 분류를 방해한다. 본 연구에서는 클라우드 기반 Google 어스 엔진 플랫폼을 사용하여 상태 수준에서 픽셀 기반 크롭 매핑을 수행하여 새로운 지상 참조 기술의 실현 가능성과 신뢰성을 입증한다. 매핑 결과는 미국 농무부(USDA) 작물 데이터층(CDL) 제품을 사용하여 평가된다. 약 130,000개의 GSV 이미지에서 CNN 모델은 약 9,400개의 목표 크롭 이미지를 식별했다. 이 이미지들은 알팔파, 아몬드, 옥수수, 면화, 포도, 쌀, 콩, 피스타치오 등의 작물 유형으로 잘 분류된다. 전체 GSV 이미지 분류 정확도는 센트럴 밸리의 경우 92%, 일리노이 주의 경우 97%이다. 그 후 이미지 지리적 좌표를 특정 방향으로 2~3회 이동하여 31,829개의 크롭 기준점을 생성했다. 즉, 일리노이에서 17,358개, 센트럴 밸리에서 14,471개였다. CDL 제품으로 매핑 결과를 평가한 결과 만족스러운 일관성이 나타났다. GSV에서 파생된 매핑 결과는 2011-2019년 작물 유형 분포의 일반적인 패턴을 포착한다. CDL 제품과 우리의 매핑 결과 사이의 전체 합치는 센트럴 밸리의 경우 0.44–0.99의 R2 값과 일리노이 주의 경우 0.81–0.98의 R2 값으로 표시된다. 제안된 방법의 다른 국가에서 적용 가치를 보여주기 위해, 꽤 좋은 결과를 얻은 한국의 논(2014–2018)을 추가로 매핑했다(R2=0.91). 이러한 결과는 딥 러닝 모델과 함께 사용되는 GSV 이미지가 세계의 많은 지역에서 지상 참조를 위한 효율적이고 비용 효율적인 대체 방법을 제공한다는 것을 나타낸다.1. Abstract 3 LIST OF FIGURES 9 LIST OF TABLES 13 ACKNOWLEDGEMENTS 14 Chapter I. Introduction 15 1.1. Study Background 15 1.2. Purpose of Research 15 Chapter II. Nocturnal stomatal conductance in rice: a coordinating bridge between prior respiration and photosynthesis next dawn 17 Abstract 17 1. Introduction 18 2. Materials and Methods 22 2.1 Plants and growth conditions 22 2.2 Leaf physiological traits 22 2.3 Rapid A/Ci response curves 24 2.4 Stomatal anatomy measurements 24 2.5 Statistical analyses 24 3. Results 25 3.1 Effects of abiotic stress on leaf traits 25 3.2 Nighttime leaf physiological traits 26 3.3 Significant priming effects of gsn on early morning photosynthesis (~5:00 – 7:00) 27 3.4 Path analyses only support the leaf trait coordination 28 3.5 Impacts of gsn on gsd and Amax under light-saturated conditions 29 3.6 Photosynthesis priming effects not detected after mid-morning (9:00) 31 4. Discussion 32 4.1 Abiotic stress results: Implications for different hypotheses 33 4.2 Enhanced carbon assimilation through coordinated regulation by gsn 34 4.3 Evaporative cooling: Passive thermoregulation via leaf trait coordination 36 References 37 Chapter III. Multi-objective optimization of crop yield, water consumption, and greenhouse gases emissions for sustainable rice production 42 Abstract 42 1. Introduction 43 2. Materials and methods 46 2.1 Study site 46 2.2 DNDC model 46 2.3 In situ data 47 2.4 Multi-objective optimization (MOO) algorithm 48 2.5 DNDC-NSGA-III integration and optimization 48 3. Results 50 3.1 DNDC model validation 50 3.2 The gaps between the current farming outcomes and optimized objectives 53 3.3 Approaching Pareto fronts through the heuristic and holistic management 55 3.4 The gaps between current farming practices to potential crop yield with optimal holistic management 56 4. Discussion 58 4.1 Could heuristic and holistic management increase current rice yield with less irrigation water? 58 4.2 Could heuristic and holistic management simultaneously reduce CH4 and N2O emissions? 59 4.3 Limitations and uncertainties 60 Reference 61 Chapter IV. Exploring Google Street View with Deep Learning for Crop Type Mapping 70 Abstract 70 1. Introduction 71 2. Materials and Methods 74 2.1 Study area 74 2.2 General methodology 75 2.3 Google Street View image collection 76 2.4 CNN model training and validation 77 2.5 Producing ground reference data and quality control 79 2.6 Mapping crop types 80 2.7 Mapping results evaluation 81 2.8 Additional test case 82 3. Results 83 3.1 GSV image classification 83 3.2 Producing ground reference data from classified GSV images 84 3.3 Mapping using the GSV derived ground reference 86 4. Discussion 96 4.1 Can we use GSV images to efficiently produce low-cost, sufficient, and reliable crop type ground reference data covering large areas? 96 4.2 Can we use GSV-derived reference data as “ground truth” to map crop types for large areas spanning many years? 97 Appendix 99 References 105 Chapter V. Conclusions 123 Supplementary Information Chapter II 125 Supplementary Information Chapter III 131 Supplementary Information Chapter IV 135 5. Abstract in Korean 138박

Sustainable Irrigation in Agriculture: An Analysis of Global Research

Irrigated agriculture plays a fundamental role as a supplier of food and raw materials. However, it is also the world’s largest water user. In recent years, there has been an increase in the number of studies analyzing agricultural irrigation from the perspective of sustainability with a focus on its environmental, economic, and social impacts. This study seeks to analyze the dynamics of global research in sustainable irrigation in agriculture between 1999 and 2018, including the main agents promoting it and the topics that have received the most attention. To do this, a review and a bibliometric analysis were carried out on a sample of 713 articles. The results show that sustainability is a line of study that is becoming increasingly more prominent within research in irrigation. The study also reveals the existence of substantial differences and preferred topics in the research undertaken by different countries. The priority issues addressed in the research were climatic change, environmental impact, and natural resources conservation; unconventional water resources; irrigation technology and innovation; and water use efficiency. Finally, the findings indicate a series of areas related to sustainable irrigation in agriculture in which research should be promoted