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A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water, Agriculture and Aquaculture".

Deadline for manuscript submissions: 10 September 2024 | Viewed by 15837

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Dr. Peter Waller

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Guest Editor

College of Agriculture & Life Sciences / College of Engineering, The University of Arizona, Tucson, AZ, USA
Interests: evapotranspiration; irrigation strategy evaluation; salinity and water stresses; drip irrigation; GIS/remote sensing; nitrogen management; soil moisture and nutrient monitoring
Special Issues, Collections and Topics in MDPI journals

Dr. Tangzhe Nie

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Guest Editor

School of Water Conservancy and Electric Power, Heilongjiang University, Harbin 150080, China
Interests: crop water requirement; crop modeling; climate change; irrigation management; sprinkling irrigation; fertilizer management; machine learning; yield response to water; water use efficiency; plant physiology; soil carbon and nitrogen cycle; greenhouse gas emission
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the face of increasingly serious water shortages and uncertainties of climate change, improving the efficiency and productivity of crop water use, while reducing negative environmental impacts, is becoming critical to cope with the increasing food demand of the growing world population. To address food security and evaluate crop production affected by irrigation management, numerous irrigation models have been produced and made available to support irrigation decision-making since the 1980s, where the quantitative simulation of irrigation management using different conceptual and process-based models plays a central role, either from a hydrological or an agricultural perspective, or from multiple perspectives.

Advanced technologies, including crop physiology and soil environment monitoring systems, wireless communication, remote sensing, machine learning, the Internet of Things (IoT) and big data, have broadened the application of irrigation models for not only irrigation planning, but also for real-time irrigation scheduling. Model-based irrigation management combining soil-based, plant- and weather-based monitoring methods with appropriate predictive control will significantly improve crop water use efficiency, as well as reducing negative environmental effects.

This Special Issue collects the latest knowledge of model-based irrigation management on both model simulations and field studies, especially including, but not limited to, the following aspects:

Applications of model-based irrigation management in fields;
Newly developed model-based irrigation systems;
Irrigation model calibration and verification;
Evaluation and optimization of model-based irrigation management;
Irrigation management response to climate change;
Influence of irrigation systems on field environment, including farmland carbon and nitrogen cycles.

Dr. Peter Waller
Dr. Tangzhe Nie
Guest Editors

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Keywords

irrigation model
smart irrigation
real-time irrigation scheduling
decision support system
field environment monitoring
model calibration and verification
evaluation and optimization
crop physiology
yield response to water
soil water balance
water use efficiency
carbon and nitrogen cycles
greenhouse gas emission
IoT or big data
remote sensing/GIS
climate change

Published Papers (8 papers)

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Research

22 pages, 9706 KiB

Open AccessArticle

WINDS Model Simulation of Guayule Irrigation

by Matthew E. Katterman, Peter M. Waller, Diaa Eldin M. Elshikha, Gerard W. Wall, Douglas J. Hunsaker, Reid S. Loeffler and Kimberly L. Ogden

Water 2023, 15(19), 3500; https://doi.org/10.3390/w15193500 - 7 Oct 2023

Cited by 1 | Viewed by 1064

Abstract

The WINDS (Water-Use, Irrigation, Nitrogen, Drainage, and Salinity) model uses the FAO56 dual crop coefficient and a daily time-step soil–water balance to simulate evapotranspiration and water content in the soil profile. This research calibrated the WINDS model for simulation of guayule under full irrigation. Using data from a furrow irrigated two-season guayule experiment in Arizona, this research developed segmented curves for guayule basal crop coefficient, canopy cover, crop height and root growth. The two-season guayule basal crop coefficient (K_cb) curve included first and second season development, midseason, late-season and end-season growth stages. For a fully irrigated guayule crop, the year one midseason K_cb was 1.14. The second year K_cb development phase began after the crop was semi-dormant during the first winter. The second year K_cb value was 1.23. The two-season root growth curve included a growth phase during the first season, no growth during winter, and a second growth phase during the second winter. A table allocated fractions of total transpiration to soil layers as a function of root depth. With the calibrated tables and curves, the WINDS model simulated soil moisture content with a root mean squared error (RMSE) of 1- to 3-% volumetric water content in seven soil layers compared with neutron probe water contents during the two-year growth cycle. Thus, this research developed growth curves and accurately simulated evapotranspiration and water content for a two-season guayule crop. Full article

(This article belongs to the Special Issue Model-Based Irrigation Management)

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18 pages, 3003 KiB

Open AccessArticle

Simulation Study of CH₄ and N₂O Emission Fluxes from Rice Fields in Northeast China under Different Straw-Returning and Irrigation Methods Based on the DNDC Model

by Dan Xu, Zhongxue Zhang, Tangzhe Nie, Yanyu Lin and Tiecheng Li

Water 2023, 15(14), 2633; https://doi.org/10.3390/w15142633 - 20 Jul 2023

Cited by 1 | Viewed by 1440

Abstract

In order to explore the long-term variation law of methane (CH₄) and nitrous oxide (N₂O) emissions from rice fields in cold regions under different straw-returning and irrigation methods, this study set up two irrigation methods, namely, conventional flooding and controlled irrigation, and two straw-returning quantities (0 t·hm⁻² and 6 t·hm⁻²). Based on the field in situ test data, a sensitivity analysis of the main factors of the DNDC model affecting the emissions of CH₄ and N₂O from rice fields was conducted, and the emission fluxes of CH₄ and N₂O were calibrated and validated. Under different future climate scenarios (RCP4.5 and RCP8.5), greenhouse gas emissions from rice fields were simulated on a 60-year scale under different straw-returning and irrigation methods using the DNDC model. The results indicate that the DNDC model can effectively simulate the seasonal emission laws of CH₄ and N₂O from rice fields in cold regions under different straw-returning and irrigation methods. The simulated values have a significant correlation with the measured values (R² ≥ 0.794, p < 0.05), and the consistency is controlled within 30%. The soil texture, soil organic carbon (SOC) content, annual average temperature, and straw-returning amount are sensitive factors for CH₄ emissions from rice fields. The total nitrogen fertilizer application amount and SOC content are sensitive factors for N₂O emissions from rice fields. Over the next 60 years, under the two different emission scenarios of RCP4.5 and RCP8.5, straw returning combined with control irrigation has a good coupling effect on the GWP of rice fields, and compared with conventional flooding without straw returning, the GWP of rice fields is reduced by 31.41% and 34.13%, respectively, and the SOC content in 0–20 cm soil layer is increased by 54.69% and 52.80%, respectively. Thus, it can be used as a long-term carbon sequestration and emission reduction tillage model for rice fields in Northeast China. The results of this study can provide a reference for a further regional estimation of greenhouse gas emissions from rice fields using models. Full article

(This article belongs to the Special Issue Model-Based Irrigation Management)

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12 pages, 648 KiB

Open AccessArticle

Assessment of the Midseason Crop Coefficient for the Evaluation of the Water Demand of Young, Grafted Hazelnut Trees in High-Density Orchards

by Alessandra Vinci, Chiara Traini, Silvia Portarena and Daniela Farinelli

Water 2023, 15(9), 1683; https://doi.org/10.3390/w15091683 - 26 Apr 2023

Cited by 3 | Viewed by 1416

Abstract

Knowledge of crop water requirements is important in supporting irrigation management. Evapotranspiration (ET) is commonly measured with a variety of instruments and field procedures, but it is also typically computed or modeled using the FAO56 or FAO66 methods. The adoption of this approach requires the assessment of the crop coefficients. Some data are available for own-rooted hazelnut trees, but no data have been reported for young and grafted hazelnut trees. There is a need to update nut–tree crop coefficients, especially considering modern cultivars and production systems, such as those with a high tree density per ha⁻¹. In this paper, the FAO66 crop transpiration coefficient K_c,Tr and the FAO56 dual crop coefficients K_cb were assessed for the mid-growing season of a young grafted hazelnut orchard. The field data were acquired manually and using UAV. The coefficients were determined for three tree densities and for two growing seasons. The crop coefficients, obtained using the FAO66 method, agreed with the literature data referring to low densities, while the FAO56 method could allow us to better define the crop coefficients for high-density hazelnut orchards. Full article

(This article belongs to the Special Issue Model-Based Irrigation Management)

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20 pages, 6292 KiB

Open AccessArticle

WINDS Model Demonstration with Field Data from a Furrow-Irrigated Cotton Experiment

by Hadiqa Maqsood, Douglas J. Hunsaker, Peter Waller, Kelly R. Thorp, Andrew French, Diaa Eldin Elshikha and Reid Loeffler

Water 2023, 15(8), 1544; https://doi.org/10.3390/w15081544 - 14 Apr 2023

Cited by 1 | Viewed by 1391

Abstract

The WINDS (Water-Use, Irrigation, Nitrogen, Drainage, and Salinity) model was developed to provide decision support for irrigated-crop management in the U.S. Southwest. The model uses a daily time-step soil water balance (SWB) to simulate the dynamics of water content in the soil profile and evapotranspiration. The model employs a tipping bucket approach during infiltration events and Richards’ equation between infiltration events. This research demonstrates WINDS simulation of a furrow-irrigated cotton experiment, conducted in 2007 in central Arizona, U.S. Calibration procedures for WINDS include the crop coefficient curve or segmented crop coefficient curve, rate of root growth, and root activity during the growing season. In this research, field capacity and wilting point were measured in the laboratory at each location and in each layer. Field measurements included water contents in layers by neutron moisture meter (NMM), irrigation, crop growth, final yield, and actual ET_c derived by SWB. The calibrated WINDS model was compared to the neutron probe moisture contents. The average coefficient of determination was 0.92, and average root mean squared error (RMSE) was 0.027 m³ m⁻³. The study also demonstrated WINDS ability to reproduce measured crop evapotranspiration (ET_c actual) during the growing season. This paper introduces the online WINDS model. Full article

(This article belongs to the Special Issue Model-Based Irrigation Management)

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17 pages, 958 KiB

Open AccessArticle

Appropriate Water-Nitrogen Regulation Mode to Improve Productivity of Mixed-Sowing Grassland of Bromus inermis and Alfalfa

by Yanxia Kang, Guangping Qi, Qiong Jia, Aixia Wang, Minhua Yin, Yanlin Ma, Jinghai Wang, Yuanbo Jiang and Zhongxia Tang

Water 2023, 15(6), 1124; https://doi.org/10.3390/w15061124 - 15 Mar 2023

Cited by 2 | Viewed by 1256

Abstract

Scientific selection of appropriate herbage planting management mode is an important guarantee to promote artificial grassland development and grassland productivity. In this study, three-year-old alfalfa (Medicago sativa L.) and bromus inermis were applied to analyze the effects of planting patterns (bromus inermis and alfalfa mixed-sowing D1, bromus inermis mono-sowing D2), nitrogen application (pure nitrogen) level (N1: 60 kg·ha⁻¹, N2: 120 kg·ha⁻¹), and water regulation (upper and lower limits of irrigation are calculated as a percentage of field capacity θ_f, W1: slight water deficit 65~85% θ_f, W2: moderate water deficit 55~85% θ_f, W3: serious water deficit 45~85% θ_f) on herbage growth and water-nitrogen use efficiency. This research applied the principal component analysis, the TOPSIS model, and the combination evaluation to evaluate each treatment. Results demonstrated that (1) the plant height, leaf area index, and yield of mixed-sowing herbage were 81.63%, 119.52%, and 111.51%, higher than the mono-sowing herbage. Increasing the amount of irrigation and nitrogen application could enhance herbage yield. The herbage yield with the W1N2 treatment was the highest. In this treatment, the mixed-sowing herbage yield was 26,050.73 kg·ha⁻¹, and the mono-sowing herbage yield was 12,186.10 kg·ha⁻¹. (2) The crude protein content of mixed-sowing herbage increased by 41.44%, higher than mono-sowing herbage, and the relative feeding value decreased by 16.34%. Increasing irrigation and nitrogen application could improve the quality of herbage. Meanwhile, the quality of herbage treated with W1N2 was the best. (3) The water use efficiency (WUE), irrigation water use efficiency (IWUE), partial factor productivity of nitrogen (PFP_N), and crude protein water use efficiency (CP_WUE) of mixed-sowing herbage were significantly higher than mono-sowing herbage. The PFP_N and the CP_WUE of herbage improved with increasing irrigation amount. Meanwhile, the WUE, the IWUE, and the CP_WUE of herbage also improved with increasing nitrogen application amount. The results showed that mixed-sowing of alfalfa and bromus inermis with slight water deficit (upper and lower limit of irrigation was 65~85% θ_f) and nitrogen application (120 kg·ha⁻¹) could have the best comprehensive production effect. At the same time, it was a planting and management mode of high yield, high quality, and high efficiency of artificial herbage in the oasis-desert interlacing area of Hexi, Gansu Province, China, and areas with similar climates. Full article

(This article belongs to the Special Issue Model-Based Irrigation Management)

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19 pages, 4233 KiB

Open AccessArticle

Simulation of Crop Productivity for Guinea Grass (Megathyrsus maximus) Using AquaCrop under Different Water Regimes

by César Augusto Terán-Chaves, José Edwin Mojica-Rodríguez, Alexander Vega-Amante and Sonia Mercedes Polo-Murcia

Water 2023, 15(5), 863; https://doi.org/10.3390/w15050863 - 23 Feb 2023

Cited by 1 | Viewed by 1946

Abstract

The perennial herbaceous forage crops’ (PHFC) biomass as bioindustry feedstock or source of nutrients for ruminants is very important from their final utilization point of view. In 2022, the AquaCrop-FAO version 7.0 model has been opened for PHFC. In this context, this study aimed to test for the first time the ability of the AquaCrop-FAO model to simulate canopy cover (CC), total available soil water (TAW), and biomass (B) of Guinea grass (Megathyrsus maximus cv. Agrosavia sabanera) under different water regimes at the Colombian dry Caribbean, South America. The water regimes included L1—irrigation based on 80% field capacity (FC), L2—irrigation based on 60% FC, L3—irrigation based on 50% FC, L4—irrigation based on 40% FC, L5—irrigation based on 20% FC, and L6—rainfed. The AquaCrop model uses the normalized water productivity—WP* (g m⁻²)—to estimate the attainable rate of crop growth under water limitation. The WP* for Guinea grass was 35.9 ± 0.42 g m⁻² with a high coefficient of determination (R² = 0.94). The model calibration results indicated the simulated CC was good (R² = 0.84, RMSE = 17.4%, NRMSE = 23.2%, EF = 0.63 and d = 0.91). In addition, cumulative biomass simulations were very good (R² = 1.0, RMSE = 5.13 t ha⁻¹, NRMSE = 8.0%, EF = 0.93 and d = 0.98), and TAW was good (R² = 0.85, RMSE = 5.4 mm, NRMSE = 7.0%, EF = 0.56 and d= 0.91). During validation, the CC simulations were moderately good for all water regimes (0.78 < R² < 0.97; 12.0% < RMSE < 17.5%; 15.9% < NRMSE < 28.0%; 0.47 < EF < 0.87; 0.82 < d < 0.97), the cumulative biomass was very good (0.99 < R² < 1.0; 0.77 t ha⁻¹ < RMSE < 3.15 t ha⁻¹; 2.5% < NRMSE < 21.9%; 0.92 < EF < 0.99; 0.97 < d < 1.0), and TAW was acceptable (0.70 < R² < 0.90; 5.8 mm < RMSE < 21.7 mm, 7.6% < NRMSE < 36.7%; 0.15 < EF < 0.58 and 0.79 < d < 0.9). The results of this study provide an important basis for future research, such as estimating biomass production of high-producing grasses in tropical environments, yield effects under scenarios of climate variability, and change based on the presented parameterization and considering a wide range of environments and grazing variations. Full article

(This article belongs to the Special Issue Model-Based Irrigation Management)

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16 pages, 1373 KiB

Open AccessArticle

Effects of Planting and Nitrogen Application Patterns on Alfalfa Yield, Quality, Water–Nitrogen Use Efficiency, and Economic Benefits in the Yellow River Irrigation Region of Gansu Province, China

by Yaru Lv, Jinghai Wang, Minhua Yin, Yanxia Kang, Yanlin Ma, Qiong Jia, Guangping Qi, Yuanbo Jiang, Qiang Lu and Xiaolong Chen

Water 2023, 15(2), 251; https://doi.org/10.3390/w15020251 - 6 Jan 2023

Cited by 3 | Viewed by 1920

Abstract

Appropriate planting and nitrogen application patterns to support high-quality production of cultivated forage in light of issues of water scarcity, extensive field husbandry, and low productivity in cultivated grassland planting areas were investigated in this study. Using Medicago sativa L. (alfalfa) as the research object, this study analyzed the effects of planting patterns (conventional flat planting (FP) and ridge culture with film mulching (RM)) and nitrogen level (N0: 0 kg·ha⁻¹, N1: 80 kg·ha⁻¹, N2: 160 kg·ha⁻¹, N3: 240 kg·ha⁻¹) on the growth, yield, quality (crude protein content (CP), acid detergent fiber content (ADF), neutral detergent fiber content (NDF), and relative feeding value (RFV)), the water–nitrogen use efficiency, and economic benefits (EB) of alfalfa in the year of establishment. Results demonstrated that (1) RM might greatly increase the growth of alfalfa when compared to FP. The plant height, stem diameter, and leaf:stem ratio of alfalfa all increased under the same planting patterns before decreasing as the nitrogen application rate (NAR) increased. (2) Appropriate NAR combined with RM could improve the yield and quality of alfalfa. Compared with other treatments, the yield, CP, and RFV under RMN2 treatment increased by 5.9~84.9%, 4.9~28.6%, and 19.6~49.3%, respectively, and the ADF and NDF decreased by 14.0~27.6% and 13.0~26.1%, respectively. (3) Under the same nitrogen level, RM showed better performance than FP in terms of water use efficiency (WUE), irrigation water use efficiency (IWUE), precipitation use efficiency (PUE), partial factor productivity of nitrogen (PFPN), agronomic nitrogen use efficiency (ANUE), and EB of alfalfa. Under the same planting pattern, PFPN decreased as the NAR increased, while WUE, IWUE, PUE, ANUE, and EB first increased and then decreased as the NAR increased and reached a maximum value under the N2 condition. In conclusion, the RM planting pattern combined with a nitrogen level of 160 kg·ha⁻¹ can significantly promote alfalfa growth as well as the yield, quality, water–nitrogen use efficiency, and EB of alfalfa, making it a suitable planting management mode for alfalfa production in the Yellow River irrigation region in Gansu Province, China and areas with similar climate. Full article

(This article belongs to the Special Issue Model-Based Irrigation Management)

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18 pages, 2708 KiB

Open AccessArticle

Calibration and Validation of the FAO AquaCrop Water Productivity Model for Perennial Ryegrass (Lolium perenne L.)

by César Augusto Terán-Chaves, Alberto García-Prats and Sonia Mercedes Polo-Murcia

Water 2022, 14(23), 3933; https://doi.org/10.3390/w14233933 - 2 Dec 2022

Cited by 4 | Viewed by 3379

Abstract

Crop models that can accurately estimate yield and final biomass have been used for major herbaceous crops and to a lesser extent in forage systems. The AquaCrop version 7.0 contains new modules that have been introduced to simulate the growth and production of perennial herbaceous forage crops. Simulated forage yields as a function of water consumption provide valuable information that allows farmers to make decisions for adapting to both climate variability and change. The study aimed to calibrate and validate the AquaCrop model for perennial ryegrass (Lolium perenne L.) in the high tropics of Colombia (South America). The experiments were conducted during two consecutive seasons, in which perennial ryegrass meadows were subjected to two irrigation regimes: full irrigation and no irrigation. The model was evaluated using precision, accuracy, and simulation error indices. The overall performance of AquaCrop in simulating canopy cover, biomass and soil water content showed a good match between measured and simulated data. The calibration results indicated an acceptable measurement of simulated canopy cover (CC) (R² = 0.95, d-index = 0.41, RMSE = 9.4%, NRMSE = 12.2%, and FE = −21.72). The model satisfactorily simulated cumulative dry mass (R² = 0.95, d-index = 0.98, RMSE = 2. 63 t ha⁻¹, NRMSE = 11.8%, and FE = 0.94). Though the biomass values obtained in the end-of-season cuts were underestimated by the model, soil water content was simulated with reasonable accuracy (R² = 0.82, d-index = 0.84, RMSE = 6.10 mm, NRMSE = 4.80%, and FE = 0.32). During validation, CC simulations were good, except under water deficit conditions, where model performance was poor (R² = 0.42, d-index = 0.01, RMSE = 40.60%, NRMSE = 40.90%, and FE = −25.71); biomass and soil water content simulations were reasonably good. The above results confirmed AquaCrop’s (v 7.0) suitability for simulating responses to water for perennial ryegrass. A single crop file was developed for managing a full season and can be confidently applied to direct future research to improve the understanding of the necessary processes and interactions for the development of perennial herbaceous forage crops. Full article

(This article belongs to the Special Issue Model-Based Irrigation Management)

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