Agricultural Water Management最新文献

{"title":"Optimizing sowing date to mitigate loss of growing degree days and enhance crop water productivity of groundwater-irrigated spring maize","authors":"Lifeng Zhou ,&nbsp;Xinlong Han ,&nbsp;Qiliang Yang ,&nbsp;Hao Feng ,&nbsp;Kadambot H.M. Siddique","doi":"10.1016/j.agwat.2024.109097","DOIUrl":"10.1016/j.agwat.2024.109097","url":null,"abstract":"<div><div>Groundwater irrigation (GWI) decreases soil temperature and increases crop growth duration and water consumption. Optimizing sowing dates offers a cost-effective solution to mitigate these effects. This study evaluated five sowing date treatments for spring maize: GWI on April 20 (GW420), April 25 (GW425), April 30 (GW430), May 5 (GW505), and May 10 (GW510), with surface water irrigation (SWI) on April 20 (SW420) as the control. The evaluated parameters included soil temperature at 5 cm depth (T₅), soil-temperature-calculated growing degree days (GDD_s), actual crop evapotranspiration (ET_c-act), leaf area index (LAI), grain filling, grain yield, and crop water productivity (WP_c). GW420 decreased daily maximum T₅ by 1.8°C (P&lt;0.05) and daily average GDD_s accumulation by 5.9 % and increased the growth duration by 7.8 d and ET_c-act by 33.2 mm compared to SW420. GW420 also delayed LAI growth and decreased the weight of maximum grain filling rate (W_max) and maximum grain filling rate (G_max), reducing mean LAI (LAI_ave) by 8.7 %, grain yield by 6.7 %, and WP_c by 10.2 % (P&lt;0.05). Late sowing compensated for GDD_s loss in the GWI treatments, with the highest daily average GDD_s accumulation observed in GW505 and GW510 (21.3°C d^–1), followed by SW420 and GW430 (20.2–20.3°C d^–1), and the lowest in GW420 and GW425 (19.1–19.4°C d^–1). Late sowing also shortened growth duration and decreased ET_c-act, with GW510 showing a 13.9 d shorter growth duration and GW425, GW430, GW505, and GW510 exhibiting 30.6, 36.0, 57.6, and 70.2 mm lower ET_c-act, respectively, than GW420. Moderately late sowing (GW430) enhanced G_max and maintained the active grain filling period (T_agp). Late sowing increased WP_c by 7.9 %, 16.8 %, 17.4 %, and 17.2 % in GW425, GW430, GW505, and GW510 (P&lt;0.05), respectively, compared to GW420. While the grain yields of GW430 and SW420 did not significantly differ, GW430 had a higher WP_c than SW420, indicating that moderately late sowing fully compensated for the decline in grain yield and WP_c of groundwater-irrigated maize. Entropy-TOPSIS analysis revealed that GW430 is the optimal sowing date for groundwater-irrigated maize in arid regions of northwest China, offering a cost-effective method to mitigate GWI-induced GDD_s loss and enhance WP_c.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"305 ","pages":"Article 109097"},"PeriodicalIF":5.9,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142538244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Maximizing crop yield and water productivity through biochar application: A global synthesis of field experiments","authors":"Liangang Xiao ,&nbsp;Yi Lin ,&nbsp;Deliang Chen ,&nbsp;Kebing Zhao ,&nbsp;Yudi Wang ,&nbsp;Zengtao You ,&nbsp;Rongqin Zhao ,&nbsp;Zhixiang Xie ,&nbsp;Junguo Liu","doi":"10.1016/j.agwat.2024.109134","DOIUrl":"10.1016/j.agwat.2024.109134","url":null,"abstract":"<div><div>Thus far, a series of field experiments have been conducted across the globe to investigate the effects of biochar on crop productivity. However, a comprehensive evaluation of the improvement potential of crop yield, water use, and relevant underlying drivers after adding biochar remains lacking. A synthesis based on global field experiments was conducted herein to investigate the efficacy of biochar in crop-yield and water-use improvement, taking a range of potential impacting factors into account. The results showed that biochar significantly increased crop yield and crop water productivity (WP_c), by 11.2 % and 14.8 %, respectively, but caused a significant decline (1.8 %) in crop evapotranspiration (ET_c). The highest crop-yield improvement was reached at an application rate of &gt; 20 t ha⁻¹ in the initial year after adding biochar. Low C/N, high pyrolysis temperature, low pH, and wood-based raw materials were found to be beneficial biochar properties for increasing crop production. Biochar generally performed better in soils of low pH and low fertility, especially in hot and humid climates. There was a higher increase in crop yield for corn compared with those for wheat and rice. In addition, changes in WP_c were generally commensurate with those of crop yield in most scenarios. Conditions beneficial for crop-yield improvement tend to result in a higher increase in WP_c through their effects on ET_c. Overall, this study illustrates that crop yield improvement is closely related to improvements in both soil fertility and water use. The latter represents an important factor related to crop growth and productivity through the regulation of evaporation and transpiration after biochar amendment. Despite the promising performance of biochar in promoting crop yield and WP_c, a further challenge involves ways to maximize the effects of biochar across global croplands by properly considering the impacting factors during the process of policy design and implementation.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"305 ","pages":"Article 109134"},"PeriodicalIF":5.9,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142538245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating multi-source remote sensing and machine learning for root-zone soil moisture and yield prediction of winter oilseed rape (Brassica napus L.): A new perspective from the temperature-vegetation index feature space","authors":"Hongzhao Shi ,&nbsp;Zhijun Li ,&nbsp;Youzhen Xiang ,&nbsp;Zijun Tang ,&nbsp;Tao Sun ,&nbsp;Ruiqi Du ,&nbsp;Wangyang Li ,&nbsp;Xiaochi Liu ,&nbsp;Xiangyang Huang ,&nbsp;Yulin Liu ,&nbsp;Naining Zhong ,&nbsp;Fucang Zhang","doi":"10.1016/j.agwat.2024.109129","DOIUrl":"10.1016/j.agwat.2024.109129","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Accurately assessing root-zone soil moisture is crucial for precision irrigation, as it directly influences crop yield. The Temperature-Vegetation Index (Ts-VI) Feature Space, which combines land surface temperature (Ts) and vegetation index (VI), is widely used to evaluate root-zone soil moisture in vegetated areas. However, its effectiveness in estimating crop yield remains unclear. Therefore, the objectives of this study are: (1) to collect multispectral and thermal infrared remote sensing data from a two-year (2021–2023) field experiment on winter oilseed rape &lt;em&gt;(Brassica napus&lt;/em&gt; L.), and to optimize and evaluate the fitting methods of the dry and wet edges of the Ts-VI feature space based on the selected vegetation indices; (2) to analyze the spatiotemporal patterns of the Temperature Vegetation Dryness Index (TVDI) derived from the optimized Ts-VI feature space and estimate root-zone soil moisture (SM) and crop yield; and (3) to precisely invert the SM and yield of winter oilseed rape in the 0–60 cm root-zone using three machine learning algorithms—Support Vector Regression (SVR), Extreme Gradient Boosting Regression (XGBR), and Random Forest Regression (RFR)—based on the optimized TVDI. Results indicate that, among the various fitting methods, the polynomial fitting method shows the best performance. The performance of the root-zone soil moisture prediction models across different growth stages follows the order of budding stage &gt; seedling stage &gt; flowering stage, and with the increase of soil depth, the performance of the model gradually deteriorates.In the yield inversion of winter oilseed rape, TVDI effectively predicts yield, with the coefficient of determination (R&lt;sup&gt;2&lt;/sup&gt;) ranging from 0.430 to 0.480 and RMSE ranging from 213.399 to 267.212 kg ha&lt;sup&gt;−1&lt;/sup&gt; during the seedling stage, R&lt;sup&gt;2&lt;/sup&gt; ranging from 0.640 to 0.747 and RMSE ranging from 110.712 to 178.133 kg ha&lt;sup&gt;−1&lt;/sup&gt; during the budding stage, and R&lt;sup&gt;2&lt;/sup&gt; ranging from 0.680 to 0.773 and RMSE ranging from 83.815 to 147.301 kg ha&lt;sup&gt;−1&lt;/sup&gt; during the flowering stage. The flowering stage effectively reflects crop yield trends and allows for accurate yield prediction of winter oilseed rape up to two months in advance. A comparison of the modeling results from XGBR, SVR, and RFR shows that XGBR provides the best fit for both root-zone soil moisture and yield predictions. Compared to linear regression models, the three machine learning models significantly improve accuracy and fit, providing more precise evaluations of root-zone soil moisture and yield. In addition, through the comparison and verification of this method in other regions, it shows that the results also have certain reference value. The combination of the Ts-VI feature space and machine learning algorithms not only enables precise monitoring of root-zone soil moisture conditions but also predicts future crop yield trends, offering valuable insights for water resource manage","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"305 ","pages":"Article 109129"},"PeriodicalIF":5.9,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142538327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and validation of a soil moisture-based wireless sensors network for the smart irrigation of a pear orchard","authors":"Fatma Hamouda,&nbsp;Àngela Puig-Sirera,&nbsp;Lorenzo Bonzi,&nbsp;Damiano Remorini,&nbsp;Rossano Massai,&nbsp;Giovanni Rallo","doi":"10.1016/j.agwat.2024.109138","DOIUrl":"10.1016/j.agwat.2024.109138","url":null,"abstract":"<div><div>In this study, a soil moisture-based wireless sensor network (SM-WSN) was transferred to support irrigation management at field scale. This smart irrigation service comes from a necessity and willingness to upgrade the regional weather-based decision support system of the Tuscany region (Italy). The sensor network was designed, hydrologically, and agronomically validated in a commercial pear orchard during four growing seasons (2019–2022). Initially, the micro irrigation system was assessed based on its water distribution uniformity (DU) performance. Then, a zoning analysis was carried out to delineate homogeneous areas according to the normalized difference vegetation index (NDVI) and soil bulk electrical conductivity (ECb). Unlike the ordinary irrigation scheduling applied in the farm, the smart system allowed maintaining the soil water content within a pre-defined optimal range, in which the upper and lower limits corresponded respectively to the soil field capacity and the threshold below which water stress occurs. Based on the smart irrigation management, a water-saving up to 50 % of the total water supplied with the ordinary scheduling was achieved during the investigated growing seasons. Moreover, the quality of the productions (i.e., °Brix, fruit size and flesh firmness) was in line with the standard market reference values. Consequently, the adoption of the new technology, which aims to identify the most appropriate irrigation management, has the potential to generate positive economic returns and reduce environmental impacts.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"305 ","pages":"Article 109138"},"PeriodicalIF":5.9,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142538276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Supplementary irrigation and reduced nitrogen application improve the productivity, water and nitrogen use efficiency of maize-soybean intercropping system in the semi-humid drought-prone region of China","authors":"Zhengxin Zhao ,&nbsp;Zongyang Li ,&nbsp;Yao Li ,&nbsp;Lianyu Yu ,&nbsp;Xiaobo Gu ,&nbsp;Huanjie Cai","doi":"10.1016/j.agwat.2024.109126","DOIUrl":"10.1016/j.agwat.2024.109126","url":null,"abstract":"<div><div>Maize-soybean intercropping systems are widespread in North China. However, the combined effects of supplementary irrigation and different nitrogen (N) application rates on the productivity, water use efficiency (WUE), and N use efficiency (NUE) of such systems remain unclear. A field experiment was conducted in a semi-humid drought-prone region in Northwest China in 2022 and 2023 to assess the interaction effects of supplemental irrigation and different N application rates on the crop yields, WUE, and NUE of a maize-soybean intercropping system and a monoculture system. Three cropping systems were used: maize-soybean intercropping, maize monoculture, and soybean monoculture, with two irrigation treatment scenarios (rainfed and supplementary irrigation at 30 mm) and three N fertilizer rates for maize (240, 180, and 120 kgN ha⁻¹). The land equivalent ratio (LER), <span><math><mrow><mo>∆</mo><mtext>water productivity</mtext></mrow></math></span> (<span><math><mtext>WP</mtext></math></span>), <span><math><mrow><mo>∆</mo><mtext>N harvest index</mtext></mrow></math></span> (<span><math><mtext>NHI</mtext></math></span>), and <span><math><mrow><mo>∆</mo><mtext>N partial factor productivity</mtext></mrow></math></span> (<span><math><mtext>NPFP</mtext></math></span>) of the maize-soybean intercropping system ranged from 1.06 to 1.11, 1.03–1.11, 1.17–1.34, and 1.16–1.28, respectively, demonstrating higher yields and resource of the intercropping system Supplementary irrigation significantly improved yield and resource use by improving the N complementarity effect and increased the economic by 17.24–31.16 %. A 25 % reduction in the N application rate (180 kgN ha⁻¹) for maize increased the NPFP without decreasing the crop yield and WP whereas, a 50 % reduction (120 kgN ha⁻¹) significantly decreased the crop yield and the economic benefits. In summary, supplementary irrigation can improve the productivity and resource use efficiency, and appropriate reduction of N fertilizer will not reduce the yield of intercropping system. This study provides practical insights for enhancing sustainable agriculture by improving water and N use efficiency in maize-soybean intercropping systems in the semi-humid arid-prone regions of China.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"305 ","pages":"Article 109126"},"PeriodicalIF":5.9,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142538329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of transformer, LSTM and coupled algorithms for soil moisture prediction in shallow-groundwater-level areas with interpretability analysis","authors":"Yue Wang,&nbsp;Yuanyuan Zha","doi":"10.1016/j.agwat.2024.109120","DOIUrl":"10.1016/j.agwat.2024.109120","url":null,"abstract":"<div><div>Accurate quantification of soil moisture is essential for understanding water and energy exchanges between the atmosphere and the Earth’s surface, as well as for agricultural applications. Predicting soil moisture content is vital for efficient water management, irrigation scheduling, and drought monitoring. Traditional forecasting methods, such as numerical regression models, often struggle due to various influencing factors and poor observation data quality. In contrast, deep learning algorithms, particularly recurrent and convolutional neural networks, show promise in predicting nonlinear data like soil moisture. This study focuses on shallow groundwater regions, using groundwater levels and meteorological data as features while coupling the Transformer model with other neural network structures. We investigate the potential of attention-based neural networks for soil moisture time series prediction. Our findings demonstrate that the Transformer model achieves an average R² of 0.523 across different time lags, outperforming the LSTM model with an R² of 0.485. The introduction of LSTM enhances the Transformer’s stability in handling temporal changes. Additionally, we verified the importance of groundwater for soil moisture prediction. This study introduces new methods for soil moisture prediction and offers new insights and recommendations for the development of artificial intelligence technology for soil moisture prediction.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"305 ","pages":"Article 109120"},"PeriodicalIF":5.9,"publicationDate":"2024-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Irrigation of rangeland soils with coal seam water - A lysimeter study on soil physico-chemical properties","authors":"J. Bernhard Wehr ,&nbsp;Scott A. Dalzell ,&nbsp;David C. Macfarlane ,&nbsp;Neal W. Menzies ,&nbsp;Peter M. Kopittke","doi":"10.1016/j.agwat.2024.109135","DOIUrl":"10.1016/j.agwat.2024.109135","url":null,"abstract":"<div><div>Groundwater extracted from coal seams may be a resource for irrigation of land in areas with low rainfall, but the effect of this water on soil properties needs to be established. A lysimeter study was conducted using intact soil cores (0.75 m diameter, 1.4 m deep) of four different soil types (Sodic Vertisol, Calcic Solonetz, Haplic Solonetz and Xanthic Lixisol) from southern Queensland, Australia, to study changes in soil physical and chemical properties under accelerated rates of irrigation with coal seam (CS) water (electrical conductivity (ECw) of 3 dS/m, pH of 8.8, and a sodium adsorption ratio (SAR) of 100). Cores were also alternately irrigated with deionised water to simulate rainfall, and either lucerne (<em>Medicago sativa</em> L) or Rhodes grass (<em>Chloris gayana</em> Kunth.) where grown in the lysimeters. The soil surface was treated with stoichiometric rates of elemental sulfur (1.4 t/ha) and gypsum (2.5 t/ha) prior to every 450 mm CS water irrigation to minimise changes in SAR and pH. Three of the soils (Vertisol, both Solonetz) had low leaching fractions (≤ 0.1 %) due to their clay texture and were initially saline in the subsoil (ECse 1.4–4.4 dS/m). Irrigation with CS water resulted in a gradual increase in salt content (EC) and SAR throughout the soil profile, but pH was not increased due to surface-applied elemental sulfur. The Lixisol had a higher hydraulic conductivity and leaching fraction (6.7 %) due to is loamy texture – in this soil, accumulated salts could be leached and no increase in salinity or pH were measured. Despite an increase in SAR for this loamy soil, no structural degradation was observed, and it could be sustainably irrigated with up to 3200 mm CS water (with cumulative irrigation volume of 5400 mm). Hence, leaching fractions rather than soil chemistry are good indicators to identify soils suitable for irrigation with CS water that is saline, alkaline, and sodic.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"305 ","pages":"Article 109135"},"PeriodicalIF":5.9,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characterizing the hysteretic effects of water and salinity stresses on root-water-uptake","authors":"Tianshu Wang ,&nbsp;Lining Liu ,&nbsp;Qiang Zuo ,&nbsp;Xun Wu ,&nbsp;Yanqi Xu ,&nbsp;Jianchu Shi ,&nbsp;Jiandong Sheng ,&nbsp;Pingan Jiang ,&nbsp;Alon Ben-Gal","doi":"10.1016/j.agwat.2024.109121","DOIUrl":"10.1016/j.agwat.2024.109121","url":null,"abstract":"<div><div>Characterizing the effects of previous water and salinity stresses is critical for the evaluation of plant water status, which, in turn, is essential for understanding soil-plant water relations and optimizing irrigation schemes. Recent research has found that hysteresis of plant response following water stress alone can be described by an exponential function of the stress degree on the previous day. To explore and quantify the effects of hysteresis concerning salinity stress and combined water-salinity stress, a hydroponic experiment and a soil column experiment on winter wheat, and a field experiment on cotton were conducted. Like water stress, previous salinity stress and combined water-salinity stress also resulted in hysteretic effects on root-water-uptake. Leaf stomatal conductance and plant transpiration rate of stressed crops could only recover gradually from a previous stressed status after re-watering. When stress was mild, compensatory recovery was found, while incomplete recovery occurred when stress was severe. Although the recovery process was closely related to stress history and type, a recovery coefficient was quantified universally with an exponential function of the stress extent on the previous day (with a coefficient of determination <em>R</em>² ≥ 0.60). Consideration of hysteresis for water and salinity stresses with a mathematical model led to significant improvement in the simulation of both relative transpiration rate (<em>R</em>² = 0.94, root mean squared error <em>RMSE</em> = 0.04, maximal absolute error <em>MAE</em> = 0.12) and soil water content (<em>R</em>² = 0.90, <em>RMSE</em> = 0.01 cm³ cm^–3, <em>MAE</em> = 0.03 cm³ cm^–3), especially during the recovery periods severely affected by historical stress. Consideration of hysteresis is expected to benefit regulation of soil water and salinity and thus enhance water use efficiency. However, the mechanisms underlying hysteresis, especially the compensatory recovery mechanisms, still need to be further investigated.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"305 ","pages":"Article 109121"},"PeriodicalIF":5.9,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantifying the rainfall variability effects on crop growth and production in the intensified annual forage - winter wheat rotation systems in a semiarid region of China","authors":"Xingfa Lai ,&nbsp;Yongliang You ,&nbsp;Xianlong Yang ,&nbsp;Zikui Wang ,&nbsp;Yuying Shen","doi":"10.1016/j.agwat.2024.109131","DOIUrl":"10.1016/j.agwat.2024.109131","url":null,"abstract":"<div><div>Replacing summer fallow period (July to September, SF) with annual short-season forages in the traditional fallow-winter wheat (<em>Triticum aestivum</em> L.) system may maintain grain yield and improve productivity in the semi-arid environments. But the uneven and variability rainfall led to instable productivity of the annual forage–winter wheat cropping system. The aims of this study were to 1) quantifying rainfall variability effects on annual forage–winter wheat system crop growing process and productivity; 2) determine the optimal annual forage–winter wheat production system that will response better to future climate change. A four-year (2016–2020) field experiment was conducted to investigate the impact of replacing summer fallow period with annual forages including oat (FO, <em>Avena sativa</em> L.), soybean (SB, <em>Glycine max</em> L.), and vetch (FV, <em>Vicia sativa</em> L.) on plant height (H), leaf area index (LAI), and above-ground biomass (AB_yield) growth index dynamics under three different levels of rainfall manipulation i.e. 30 % of ambient rainfall exclusion (R-30 %), natural rainfall (CK), and 30 % of ambient rainfall increase (R+30 %). Additionally, we assessed the correlations between forage and winter wheat production with growing season precipitation across 12 rainfall scenarios. Average forage biomass values of oat, soybean, and vetch were 5.50, 4.29, and 2.82 t ha⁻¹, respectively during summer fallow period. The average winter wheat grain yield values in SF, FO, SB, and FV were 3.78, 3.12, 4.02, and 3.18 t ha⁻¹, respectively. Integrating oat into fallow period had negative effects on wheat growth and production, and the H, LAI, and AB_yield for FO were 63.7 %, 50.9 %, and 29.9 % lower than SF in dry year, but the wheat grain yield in SB were 18.2 % and 24.8 % greater than SF in normal and wet years. Across the four growing seasons, the forage and wheat yields were shown to be strongly related to precipitation, and increasing precipitation significantly enhanced the production. In 2016–2017 growing season, LAI of wheat in SF, FO, SB, and FV with R+30 % scenario was increased by 30.2 %, 21.7 %, 32.7 %, and 19.8 % and that with R-30 % scenario decreased by 23.2 %, 17.8 %, 24.7 %, 16.5 % compared CK, respectively. The traditional summer fallow practice had advantage for maintaining stability in wheat gain production, especially under dry years. In consideration of forage and wheat production to rainfall variability, integrating soybean into fallow season may be an efficient option to maintain wheat yield and produce high forage amount under future climate change on the Loess Plateau and similar semi-arid regions.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"305 ","pages":"Article 109131"},"PeriodicalIF":5.9,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A distributed simulation-optimization framework for many-objective water resources allocation in canal-well combined irrigation district under diverse supply and demand scenarios","authors":"Qianzuo Zhao ,&nbsp;Yanan Jiang ,&nbsp;Qianyu Wang ,&nbsp;Fenfang Xu","doi":"10.1016/j.agwat.2024.109109","DOIUrl":"10.1016/j.agwat.2024.109109","url":null,"abstract":"<div><div>To address the issues of both water resources allocation and sustainable management in agriculture areas with rising food demand, a simulation-optimization framework based on Flopy and Pymoo was proposed and developed for canal-well combined irrigation districts. The proposed framework first solved the many-objective water resources allocation problem which integrates groundwater simulation, crop production, and farmer income modules to quantitatively reveal the various trade-offs and synergies by using NSGA-III algorithm. The Entropy-TOPSIS method was then applied to recommend proper water allocation schemes. The proposed framework was further tested in Baojixia irrigation district considering various water supply and crop demand scenarios based on Copula-based uncertainty analysis. The Key findings are as follows: (1) the proposed framework could effectively optimize conjunctive water resources allocation problems of both surface water and groundwater; (2) low supply combined with high demand (p=0.17) is more likely to occur than high supply with high demand (p=0.02); (3) increased crop demand and restricted surface water negatively impact both water productivity and groundwater sustainability; and (4) the cumulative groundwater drawdown of recommend schemes is 36.9 % and 6.5 % higher under low to medium supply scenarios, while water productivity of recommend schemes decreases 28.2 % and 9.7 % with high and medium demand. This framework could provide useful insights for sustainable agricultural water management in canal-well combined irrigation district with various uncertainties in supply and demand scenarios.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"305 ","pages":"Article 109109"},"PeriodicalIF":5.9,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}