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Agronomy
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Model Application for Sustainable Agricultural Water
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Model Application for Sustainable Agricultural Water

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Water Use and Irrigation".

Deadline for manuscript submissions: closed (30 September 2019) | Viewed by 48415

Special Issue Editors

Dr. Jaehak Jeong

E-Mail Website
Guest Editor
BREC, Texas A&M AgriLife Research, Department of Biological and Agricultural Engineering, Texas A&M University, Temple, TX, USA
Interests: crop model; water; irrigation; drainage; agricultural water management; salinity; sustainable water use; water efficiency; water productivity; water reuse; climate change; water quality
Dr. Xuesong Zhang

E-Mail Website
Guest Editor
1. Joint Global Change Research Institute, Pacific Northwest National Laboratory, 5825 University Research Court, Suite 3500, College Park, MD 20740, USA
2. Earth System Sciences Interdisciplinary Center, University of Maryland, 5825 University Research Court, Suite 4001, College Park, MD 20740, USA
Interests: crop model; water; irrigation; drainage; agricultural water management; salinity; sustainable water use; water efficiency; water productivity; water reuse; climate change; water quality
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear colleagues,

Since mid-20th century, crop models have been developed and enhanced for whole systems-level quantitative assessment of cropping systems. These state-of-the-art simulation models synthesize the knowledge base obtained from field experiments and numerous disciplines encompassing agronomy, crop physiology, soil, water, climate, and economy. With a growing population, increasing demands for water intensify competition between agriculture and urban needs and transboundary issues for water resources. The natural-resources management aspect of cropping systems is a driving factor that complicates agricultural production with ongoing climate change and water contamination. This Special Issue offers the opportunity for crop scientists and modelers to publish research on “Model Application for Sustainable Agricultural Water”. Contributions are sought from agricultural modeling communities across the world that deal with agricultural systems in various respects. The primary topics of the Special Issue include, but are not limited to, the following:

  • Field-scale to regional-scale management of agricultural water-resources
  • Sustainable agricultural water-management under climate change
  • The water productivity of food crops and bioenergy crops
  • The management of salinity in irrigated agriculture and the use of saline water for crop growth
  • Water reuse and wastewater for irrigation
  • The environmental effects of agricultural water use and management

Dr. Jaehak Jeong
Dr. Xuesong Zhang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agronomy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • crop model
  • water
  • irrigation
  • drainage
  • agricultural water management
  • salinity
  • sustainable water use
  • water efficiency
  • water productivity
  • water reuse
  • climate change
  • water quality

Published Papers (11 papers)

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Editorial

Jump to: Research

3 pages, 173 KiB  
Editorial
Model Application for Sustainable Agricultural Water Use
by Jaehak Jeong and Xuesong Zhang
Agronomy 2020, 10(3), 396; https://doi.org/10.3390/agronomy10030396 - 14 Mar 2020
Cited by 6 | Viewed by 2138
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Model Application for Sustainable Agricultural Water)

Research

Jump to: Editorial

18 pages, 4724 KiB  
Article
A Model-Based Real-Time Decision Support System for Irrigation Scheduling to Improve Water Productivity
by Xiaoping Chen, Zhiming Qi, Dongwei Gui, Zhe Gu, Liwang Ma, Fanjiang Zeng, Lanhai Li and Matthew W. Sima
Agronomy 2019, 9(11), 686; https://doi.org/10.3390/agronomy9110686 - 27 Oct 2019
Cited by 29 | Viewed by 5045
Abstract
A precisely timed irrigation schedule to match crop water demand is vital to improving water use efficiency in arid farmland. In this study, a real-time irrigation-scheduling infrastructure, Decision Support System for Irrigation Scheduling (DSSIS), based on water stresses predicted by an agro-hydrological model, [...] Read more.
A precisely timed irrigation schedule to match crop water demand is vital to improving water use efficiency in arid farmland. In this study, a real-time irrigation-scheduling infrastructure, Decision Support System for Irrigation Scheduling (DSSIS), based on water stresses predicted by an agro-hydrological model, was constructed and evaluated. The DSSIS employed the Root Zone Water Quality Model (RZWQM2) to predict crop water stresses and soil water content, which were used to trigger irrigation and calculate irrigation amount, respectively, along with forecasted rainfall. The new DSSIS was evaluated through a cotton field experiment in Xinjiang, China in 2016 and 2017. Three irrigation scheduling methods (DSSIS-based (D), soil moisture sensor-based (S), and conventional experience-based (E)), factorially combined with two irrigation rates (full irrigation (FI), and deficit irrigation (DI, 75% of FI)) were compared. The DSSIS significantly increased water productivity (WP) by 26% and 65.7%, compared to sensor-based and experience-based irrigation scheduling methods (p < 0.05), respectively. No significant difference was observed in WP between full and deficit irrigation treatments. In addition, the DSSIS showed economic advantage over sensor- and experience-based methods. Our results suggested that DSSIS is a promising tool for irrigation scheduling. Full article
(This article belongs to the Special Issue Model Application for Sustainable Agricultural Water)
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18 pages, 3120 KiB  
Article
Lessons from Assessing Uncertainty in Agricultural Water Supply Estimation for Sustainable Rice Production
by Jung-Hun Song, Younggu Her, Sang Min Jun, Soonho Hwang, Jihoon Park and Moon-Seong Kang
Agronomy 2019, 9(10), 662; https://doi.org/10.3390/agronomy9100662 - 21 Oct 2019
Cited by 10 | Viewed by 2782
Abstract
Agricultural water supply (AWS) estimation is one of the first and fundamental steps of developing agricultural management plans, and its accuracy must have substantial impacts on the following decision-making processes. In modeling the AWS for paddy fields, it is still common to determine [...] Read more.
Agricultural water supply (AWS) estimation is one of the first and fundamental steps of developing agricultural management plans, and its accuracy must have substantial impacts on the following decision-making processes. In modeling the AWS for paddy fields, it is still common to determine parameter values, such as infiltration rates and irrigation efficiency, solely based on literature and rough assumptions due to data limitations; however, the impact of parameter uncertainty on the estimation has not been fully discussed. In this context, a relative sensitivity index and the generalized likelihood uncertainty estimation (GLUE) method were applied to quantify the parameter sensitivity and uncertainty in an AWS simulation. A general continuity equation was employed to mathematically represent the paddy water balance, and its six parameters were investigated. The results show that the AWS estimates are sensitive to the irrigation efficiency, drainage outlet height, minimum ponding depth, and infiltration, with the irrigation efficiency appearing to be the most important parameter; thus, they should be carefully selected. Multiple combinations of parameter values were observed to provide similarly good predictions, and such equifinality produced the substantial amount of uncertainty in AWS estimates regardless of the modeling approaches, indicating that the uncertainty should be counted when developing water management plans. We also found that agricultural system simulations using only literature-based parameter values provided poor accuracy, which can lead to flawed decisions in the water resources planning processes, and then the inefficient use of public investment and resources. The results indicate that modelers’ careful parameter selection is required to improve the accuracy of modeling results and estimates from using not only information from the past studies but also modeling practices enhanced with local knowledge and experience. Full article
(This article belongs to the Special Issue Model Application for Sustainable Agricultural Water)
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14 pages, 4040 KiB  
Article
Effects of Irrigation Amount and Irrigation Frequency on Flue-Cured Tobacco Evapotranspiration and Water Use Efficiency Based on Three-Year Field Drip-Irrigated Experiments
by Jianfang Guang, Xiaohou Shao, Qisong Miao, Xu Yang, Chao Gao, Fuzhang Ding and Youbo Yuan
Agronomy 2019, 9(10), 624; https://doi.org/10.3390/agronomy9100624 - 10 Oct 2019
Cited by 9 | Viewed by 3302
Abstract
This study aimed to determine the effect of irrigation amount and irrigation frequency on drip-irrigated flue-cured tobacco evapotranspiration (ETa), yield, and water use efficiency. Four irrigation treatment levels were imposed: 100% IRT (fully irrigated treatment, no stress), 85% IRT, 70% IRT, RFT (rainfed [...] Read more.
This study aimed to determine the effect of irrigation amount and irrigation frequency on drip-irrigated flue-cured tobacco evapotranspiration (ETa), yield, and water use efficiency. Four irrigation treatment levels were imposed: 100% IRT (fully irrigated treatment, no stress), 85% IRT, 70% IRT, RFT (rainfed treatment), and high, medium, and low irrigation frequencies were set. The relationship between irrigation volume and yield is a quadratic curve. The evapotranspiration had a positive relationship with the irrigation amount. The yield of flue-cured tobacco was the highest in 2016 (wet year), and the corresponding ETa was the smallest. The irrigation water use efficiency (IWUE) in the driest year, 2017, was lower than IWUE in the wet years 2015 and 2016, and the crop water use efficiency (CWUE) had similar results for the three years. IWUE increased with irrigation amount. The effect of irrigation frequency on CWUE was not significant. The CWUE had a positive relationship with yield. No significant differences due to irrigation frequency were found for yield. Full article
(This article belongs to the Special Issue Model Application for Sustainable Agricultural Water)
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16 pages, 1382 KiB  
Article
Improving Modeling of Quinoa Growth under Saline Conditions Using the Enhanced Agricultural Policy Environmental eXtender Model
by Nicole Goehring, Paul Verburg, Laurel Saito, Jaehak Jeong and Manyowa N. Meki
Agronomy 2019, 9(10), 592; https://doi.org/10.3390/agronomy9100592 - 27 Sep 2019
Cited by 7 | Viewed by 3367
Abstract
Cultivation of highly salt-tolerant plants (i.e., halophytes), may provide a viable alternative to increase productivity compared to conventional salt-sensitive crops, increasing the economic potential of salt-affected lands that comprise ~20% of irrigated lands worldwide. In this study the Agricultural Policy/Environmental eXtender (APEX) model [...] Read more.
Cultivation of highly salt-tolerant plants (i.e., halophytes), may provide a viable alternative to increase productivity compared to conventional salt-sensitive crops, increasing the economic potential of salt-affected lands that comprise ~20% of irrigated lands worldwide. In this study the Agricultural Policy/Environmental eXtender (APEX) model was adapted to simulate growth of the halophyte quinoa, along with salt dynamics in the plant-soil-water system. Model modifications included salt uptake and salt stress functions formulated using greenhouse data. Data from a field site were used to further parameterize and calibrate the model. Initial simulation results were promising, but differences between simulated and observed soil salinity and plant salt values during the growing season in the calibration suggest that additional improvements to salt uptake and soil salinity algorithms are needed. To demonstrate utility of the modified APEX model, six scenarios were run to estimate quinoa biomass production and soil salinity with different irrigation managements and salinities. Simulated annual biomass was sensitive to soil moisture, and root zone salinity increased in all scenarios. Further experiments are needed to improve understanding of crop salt uptake dynamics and stress sensitivities so that future model updates and simulations better represent salt dynamics in plants and soils in agricultural settings. Full article
(This article belongs to the Special Issue Model Application for Sustainable Agricultural Water)
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15 pages, 3248 KiB  
Article
Assessing the Impact of Best Management Practices in a Highly Anthropogenic and Ungauged Watershed Using the SWAT Model: A Case Study in the El Beal Watershed (Southeast Spain)
by Adrián López-Ballesteros, Javier Senent-Aparicio, Raghavan Srinivasan and Julio Pérez-Sánchez
Agronomy 2019, 9(10), 576; https://doi.org/10.3390/agronomy9100576 - 24 Sep 2019
Cited by 39 | Viewed by 3755
Abstract
Best management practices (BMPs) provide a feasible solution for non-point source pollution problems. High sediment and nutrient yields without retention control result in environmental deterioration of surrounding areas. In the present study, the soil and water assessment tool (SWAT) model was developed for [...] Read more.
Best management practices (BMPs) provide a feasible solution for non-point source pollution problems. High sediment and nutrient yields without retention control result in environmental deterioration of surrounding areas. In the present study, the soil and water assessment tool (SWAT) model was developed for El Beal watershed, an anthropogenic and ungauged basin located in the southeast of Spain that drains into a coastal lagoon of high environmental value. The effectiveness of five BMPs (contour planting, filter strips, reforestation, fertilizer application and check dam restoration) was quantified, both individually and in combination, to test their impact on sediment and nutrient reduction. For calibration and validation processes, actual evapotranspiration (AET) data obtained from a remote sensing dataset called Global Land Evaporation Amsterdam Model (GLEAM) were used. The SWAT model achieved good performance in the calibration period, with statistical values of 0.78 for Kling–Gupta efficiency (KGE), 0.81 for coefficient of determination (R2), 0.58 for Nash–Sutcliffe efficiency (NSE) and 3.9% for percent bias (PBIAS), as well as in the validation period (KGE = 0.67, R2 = 0.83, NS = 0.53 and PBIAS = −25.3%). The results show that check dam restoration is the most effective BMP with a reduction of 90% in sediment yield (S), 15% in total nitrogen (TN) and 22% in total phosphorus (TP) at the watershed scale, followed by reforestation (S = 27%, TN = 16% and TP = 20%). All effectiveness values improved when BMPs were assessed in combination. The outcome of this study could provide guidance for decision makers in developing possible solutions for environmental problems in a coastal lagoon. Full article
(This article belongs to the Special Issue Model Application for Sustainable Agricultural Water)
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21 pages, 5170 KiB  
Article
Agricultural Policy Environmental eXtender (APEX) Simulation of Spring Peanut Management in the North China Plain
by Jie Zhao, Qingquan Chu, Mengjie Shang, Manyowa N. Meki, Nicole Norelli, Yao Jiang, Yadong Yang, Huadong Zang, Zhaohai Zeng and Jaehak Jeong
Agronomy 2019, 9(8), 443; https://doi.org/10.3390/agronomy9080443 - 10 Aug 2019
Cited by 17 | Viewed by 4165
Abstract
Spring peanut is a valuable alternative crop to mitigate water scarcity caused by excessive water use in conventional cropping systems in the North China Plain (NCP). In the present study, we evaluated the capability of the Agricultural Policy Environmental eXtender (APEX) model to [...] Read more.
Spring peanut is a valuable alternative crop to mitigate water scarcity caused by excessive water use in conventional cropping systems in the North China Plain (NCP). In the present study, we evaluated the capability of the Agricultural Policy Environmental eXtender (APEX) model to predict spring peanut response to sowing dates and seeding rates in order to optimize sowing dates, seeding rates, and irrigation regimes. Data used for calibration and validation of the model included leaf area index (LAI), aboveground biomass (ABIOM), and pod yield data collected from a field experiment of nine sowing dates and seeding rate combinations conducted from 2017 to 2018. The calibrated model was then used to simulate peanut yield responses to extended sowing dates (5 April to 4 June with a 5-day interval) and seeding rates (15 plants m−2 to 50 plants m−2 with a 5 plants m−2 interval) using 38 years of weather data as well as yield, evapotranspiration (ET), and water stress days under different irrigation regimes (rainfed, one irrigation before planting (60 mm) or at flowering (60 mm), and two irrigation with one time before planting and one time at flowering (60 mm each time) or at pod set (60 mm each time)). Results show that the model satisfactorily simulates pod yield of peanut based on R2 = 0.70, index of agreement (d value) being 0.80 and percent bias (PBIAS) values ≤4%. Moreover, the model performed reasonably well in predicting the emergence, LAI and ABIOM, with a R2 = 0.86, d = 0.95 and PBIAS = 8% for LAI and R2 = 0.90, d = 0.97 and PBIAS = 1% for ABIOM, respectively. Simulation results indicate that the best combination of sowing dates and seeding rates is a density of 35–40 plants m−2 and dates during early-May to mid-May due to the influence of local climate and canopy structure to the growth and yield of peanut. Under the optimal sowing date and plant density, an irrigation depth of 60 mm during flowering gave a pod yield (5.6 t ha−1) and ET (464 mm), which resulted in the highest water use efficiency (12.1 kg ha−1 mm−1). The APEX model is capable of assessing the effects of management practices on the growth and yield of peanut. Sowing 35–40 plants m−2 during early-May to mid-May with 60 mm irrigation depth is the recommended agronomic practice for peanut production in the water-constrained NCP. Full article
(This article belongs to the Special Issue Model Application for Sustainable Agricultural Water)
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10 pages, 585 KiB  
Communication
AquaCropR: Crop Growth Model for R
by Anyela Valentina Camargo Rodriguez and Eric S. Ober
Agronomy 2019, 9(7), 378; https://doi.org/10.3390/agronomy9070378 - 14 Jul 2019
Cited by 16 | Viewed by 9553
Abstract
The Food and Agriculture Organization (FAO) AquaCrop model, run either via a standalone graphical user interface (GUI) or via a matlab application programming interface (API) (AquaCrop-OS), has been successfully tested on many crop species and under multiple scenarios. However, with these current versions, [...] Read more.
The Food and Agriculture Organization (FAO) AquaCrop model, run either via a standalone graphical user interface (GUI) or via a matlab application programming interface (API) (AquaCrop-OS), has been successfully tested on many crop species and under multiple scenarios. However, with these current versions, it is difficult for users to adapt formulae, add functionality or incorporate the model into other applications such as decision support tools. Here, we report on the release of a version of AquaCrop written in R. Performance of the model was tested using published datasets of wheat (Triticum aestivum L.) and maize (Zea mays L.), comparing output from AquaCropR with these other versions of AquaCrop. Our goal in developing this version was to widen the use and improvement of AquaCrop through open access. Full article
(This article belongs to the Special Issue Model Application for Sustainable Agricultural Water)
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20 pages, 5128 KiB  
Article
AquaCrop Calibration and Validation for Faba Bean (Vicia faba L.) under Different Agronomic Managements
by Ketema Tilahun Zeleke
Agronomy 2019, 9(6), 320; https://doi.org/10.3390/agronomy9060320 - 18 Jun 2019
Cited by 12 | Viewed by 4721
Abstract
Faba bean (Vicia faba L.) is an important pulse crop known for its nitrogen-fixing characteristics and as a disease-break crop in crop rotations. Sowing time, scheduling of supplemental irrigation, and sowing rate are some of the agronomic managements which affect faba bean [...] Read more.
Faba bean (Vicia faba L.) is an important pulse crop known for its nitrogen-fixing characteristics and as a disease-break crop in crop rotations. Sowing time, scheduling of supplemental irrigation, and sowing rate are some of the agronomic managements which affect faba bean growth and yield. The effect of these on faba bean yield can be evaluated using calibrated models. The Food and Agriculture Organization (FAO) AquaCrop model was calibrated and tested using two-year experimental data of different watering regimes, sowing dates, and sowing rates in a semiarid environment of South-Eastern Australia. AquaCrop adequately simulated the green canopy cover (CC), biomass development, grain yield, and soil water dynamics under different agronomic management conditions. AquaCrop simulated faba bean yield with 3% deviation, root mean square error (RMSE) of 0.49 t ha−1, normalised root mean square error (NRMSE) of 12.4%, index of agreement (d) of 0.95, and R2 of 0.86. The CC was simulated with RMSE of 14.1%, R2 of 0.85, and d of 0.90. The above-ground dry matter was predicted with RMSE of 2.6 t ha−1, R2 of 0.95, and d of 0.93. Except for end-of-season values, the total soil water was also adequately simulated at RMSE of 21 mm, R2 of 0.89, and d of 0.87. The response of faba bean to supplemental irrigation, sowing time, and sowing rate was adequately simulated by the calibrated model. AquaCrop is a valuable decision support tool for predicting faba bean growth, yield, and soil water dynamics under different agronomic managements. Full article
(This article belongs to the Special Issue Model Application for Sustainable Agricultural Water)
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17 pages, 2748 KiB  
Article
Uncertainty of CERES-Maize Calibration under Different Irrigation Strategies Using PEST Optimization Algorithm
by Quanxiao Fang, L. Ma, R. D. Harmel, Q. Yu, M. W. Sima, P. N. S. Bartling, R. W. Malone, B. T. Nolan and J. Doherty
Agronomy 2019, 9(5), 241; https://doi.org/10.3390/agronomy9050241 - 10 May 2019
Cited by 12 | Viewed by 3563
Abstract
An important but rarely studied aspect of crop modeling is the uncertainty associated with model calibration and its effect on model prediction. Biomass and grain yield data from a four-year maize experiment (2008–2011) with six irrigation treatments were divided into subsets by either [...] Read more.
An important but rarely studied aspect of crop modeling is the uncertainty associated with model calibration and its effect on model prediction. Biomass and grain yield data from a four-year maize experiment (2008–2011) with six irrigation treatments were divided into subsets by either treatments (Calibration-by-Treatment) or years (Calibration-by-Year). These subsets were then used to calibrate crop cultivar parameters in CERES (Crop Environment Resource Synthesis)-Maize implemented within RZWQM2 (Root Zone Water Quality Model 2) using the automatic Parameter ESTimation (PEST) algorithm to explore model calibration uncertainties. After calibration for each subset, PEST also generated 300 cultivar parameter sets by assuming a normal distribution of each parameter within their reported values in the literature, using the Latin hypercube sampling (LHS) method. The parameter sets that produced similar goodness of fit (11–164 depending on subset used for calibration) were then used to predict all the treatments and years of the entire dataset. Our results showed that the selection of calibration datasets greatly affected the calibrated crop parameters and their uncertainty, as well as prediction uncertainty of grain yield and biomass. The high variability in model prediction of grain yield and biomass among the six (Calibration-by-Treatment) or the four (Calibration-by-Year) scenarios indicated that parameter uncertainty should be considered in calibrating CERES-Maize with grain yield and biomass data from different irrigation treatments, and model predictions should be provided with confidence intervals. Full article
(This article belongs to the Special Issue Model Application for Sustainable Agricultural Water)
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22 pages, 2141 KiB  
Article
Water Stress Permanently Alters Shoot Architecture in Common Bean Plants
by Angelica Durigon, Jochem Evers, Klaas Metselaar and Quirijn de Jong van Lier
Agronomy 2019, 9(3), 160; https://doi.org/10.3390/agronomy9030160 - 26 Mar 2019
Cited by 18 | Viewed by 5371
Abstract
The effects of water stress on crop yield through modifications of plant architecture are vital to crop performance such as common bean plants. To assess the extent of this effect, an outdoor experiment was conducted in which common bean plants received five treatments: [...] Read more.
The effects of water stress on crop yield through modifications of plant architecture are vital to crop performance such as common bean plants. To assess the extent of this effect, an outdoor experiment was conducted in which common bean plants received five treatments: fully irrigated, and irrigation deficits of 30% and 50% applied in flowering or pod formation stages onwards. Evapotranspiration, number and length of pods, shoot biomass, grain yield and harvest index were assessed, and architectural traits (length and thickness of internodes, length of petioles and petiolules, length and width of leaflet blades and angles) were recorded and analyzed using regression models. The highest irrigation deficit in the flowering stage had the most pronounced effect on plant architecture. Stressed plants were shorter, leaves were smaller and pointing downward, indicating that plants permanently altered their exposure to sunlight. The combined effect of irrigation deficit and less exposure to light lead to shorter pods, less shoot biomass and lower grain yield. Fitted empirical models between water deficit and plant architecture can be included in architectural simulation models to quantify plant light interception under water stress, which, in turn, can supply crop models adding a second order of water stress effects on crop yield simulation. Full article
(This article belongs to the Special Issue Model Application for Sustainable Agricultural Water)
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