首页期刊导航|Agricultural Water Management
期刊信息/Journal information
Agricultural Water Management
Elsevier
Agricultural Water Management

Elsevier

0378-3774

Agricultural Water Management/Journal Agricultural Water ManagementSCIISTPEI
正式出版
收录年代

    Data-based groundwater quality estimation and uncertainty analysis for irrigation agriculture

    Yu H.Wen X.Sheng D.Wu M....
    15页
    查看更多>>摘要：? 2021 Elsevier B.V.Accurate groundwater quality estimation is of great significance for effective irrigation management in agricultural areas. However, due to lack of data, the parameterization of the physical models is restricted for large areas with diverse underlying surface conditions. To obtain effective and convenient estimations of groundwater quality with the most accessible data is urgently needed. In this study, the validity of the data-based models in irrigation water quality index estimation was investigated by only using physical groundwater parameters as inputs. 15 combination scenarios of the physical parameters including temperature, pH, electrical conductivity, and dissolved oxygen were examined by support vector machine (SVM), random forests (RF), artificial neural networks (ANN) and extreme learning machine (ELM) models for total dissolved solids (TDS), potential salinity (PS), and sodium adsorption ratio (SAR) estimation for the Zhangye Basin, northwest China. Performance of the artificial intelligence (AI) models was evaluated according to the coefficient of correlation (R), root mean squared error (RMSE), mean absolute percentage error (MAPE), and Nash-Sutcliffe efficiency (NS). The Monte Carlo (MC) approach was performed to assess the uncertainty of the physical groundwater parameters and the sensitivity of the AI models. The results revealed an input pattern, which emphasized the important role of EC and the improving function of pH, in revealing the nature of data-based irrigation water quality index estimation. Results of the uncertainty analysis also confirmed the prominent superiority and robustness of the SVM, RF and ELM models in producing excellent estimations with only physical parameters as inputs. The developed AI models were valuable in estimating irrigation water quality indexes, thus could help decision makers manage irrigation strategies. By using physical groundwater parameters as input variables, the AI models showed prospects in convenient and cost-effective irrigation water quality index estimating.
      原文链接:
    • NSTL
    • Elsevier

    Quantifying the variability in water use efficiency from the canopy to ecosystem scale across main croplands

    Chen Y.Ding Z.Yu P.Song L....
    12页
    查看更多>>摘要：? 2021 Elsevier B.V.Current, how to use limited water resources efficiently and improve agricultural water use efficiency, has become one of the greatest challenges for global food security. In this study, multiple site-years of carbon and water flux data across the major crops including maize, winter wheat and soybean, were used to quantify the variability in canopy-scale transpiration (T), ecosystem-scale evapotranspiration (ET) as well as the associated water use efficiencies (WUET and WUEET). On the basis of ET partitioning, the results indicated that the transpiration ratio–T/ET as well as T and ET exhibited an obvious single-peak seasonal pattern across the typical croplands. However, at the early and late growing stages, there existed large discrepancies in T and ET owing to low vegetation coverage, while T and ET were very close during the peak period. Among them, maize exhibited the largest T/ET by 0.50 ± 0.12, followed by soybean of 0.43 ± 0.08 and winter wheat of 0.38 ± 0.09, respectively. Furthermore, the coupling relationships between gross primary productivity (GPP) and water fluxes including T and ET changed from linear to nonlinear. The study also found that the variability in WUET and WUEET were not consistent. Specifically, WUEET showed distinct seasonal characteristic whereas WUET kept constant as a plateau almost throughout the growth period, which reflected the inherent physiological property controlled by plant stomata at the canopy scale. Among these crops, maize exhibited the largest WUET and WUEET (5.30 ± 0.89 and 2.48 ± 1.14 g C kg?1 H2O), followed by winter wheat (4.97 ± 1.52 and 2.35 ± 0.64 g C kg?1 H2O) and soybean (4.88 ± 1.59 and 1.89 ± 0.99 g C kg?1 H2O), respectively.
      原文链接:
    • NSTL
    • Elsevier

    Effect of irrigation depth on biomass production and metabolic profile of Lippia alba (linalool chemotype) essential oil

    Mendoza J.D.S.Ming L.C.Correia L.C.Siqueira W.J....
    9页
    查看更多>>摘要：? 2021 Elsevier B.V.Water is a limiting factor in agricultural production. In the cultivation of aromatic plants, abiotic factors, such as water deficit, influence the yield and composition of essential oils. Lippia alba, linalool chemotype, is an aromatic species originally from South America in domestication, and it can be used in the cosmetics industry to develop natural products, such as flavorings, fragrances, and perfumes. So far, the effect of water deficit on the agronomic characteristics and the species' essential oil composition has not been evaluated. In this work, total aerial dry matter production, total leaf dry matter production, essential oil production, and chemical composition of the essential oil from the leaves of selected clones of L. alba were evaluated under four different irrigation depths, using an organic production system during four cutting cycles of 90 days. Nineteen compounds were detected in the essential oil of L. alba. The irrigation treatments altered the relative proportions of the compounds linalool, geranial, β-elemene, and germacrene D. The highest total biomass production of the aerial part was obtained with the 125% of the reference evapotranspiration (ET0) at 180 (second cut) and 270 (third cut) days after formation pruning. The irrigation depths of 100% and mainly the 125% ET0 at 180 days (second cut) favored the total dry matter production of leaves in Lippia alba, chemotype linalool, and the essential oil production. In general, a moderate water deficit generated an adjustment in the use of water resources with increased the essential oil production, in particular the linalool compound.
      原文链接:
    • NSTL
    • Elsevier

    A co-simulation approach to study the impact of gravity collective irrigation constraints on plant dynamics in Southern France

    Cheviron B.Veyssier J.Barreteau O.Braud I....
    13页
    查看更多>>摘要：? 2022Crop models allow simulating irrigated plant dynamics at the plot level. However, in many places irrigation is managed collectively to share water at the network level. To study the impact of the irrigation network constraints on plant dynamics, we proposed a co-simulation approach based on the coupling of the Optirrig crop model at the plot level with the WatASit agent-based model at the network level. As a proof of concept applied on a typical gravity network of the South-East of France, the approach allowed to consider the effects of the network spatial (i.e. water flow gradient) and temporal (i.e. network coordination) constraints on leaf area index and water stress index dynamics of 16 cereal plots. Four progressive levels of collective irrigation constraints are simulated: no collective constraints, space collective constraints, time collective constraints, and space and time collective constraints. Retrospective simulation of the 2017 irrigation campaign is consistent with field surveys, and simulation results suggest that plant water stress could be underestimated when simulated at the plot level rather than at the network level. Spatially, the most severe water stress was observed for the plants located furthest downstream of the network. Temporally, the absence of network coordination can lead to earlier plant water stress and lower plant growth during the collective irrigation campaign, while time-slot-based coordination tends to delay the impact. For future research, reinforcing the coupling from the crop model to the agent-based model could allow to study the feedback loop of plant dynamics on irrigation practice adaptations. It is also a first step towards an optimization approach for irrigation networks.
      原文链接:
    • NSTL
    • Elsevier

    If the combination of straw interlayer and irrigation water reduction maintained sunflower yield by boosting soil fertility and improving bacterial community in arid and saline areas

    Song J.Zhang H.Chang F.Yu R....
    8页
    查看更多>>摘要：? 2021 Elsevier B.V.Limited water resources have severely restricted saline soil amelioration and utilization in the Hetao irrigated area, China. Burying straw interlayer is an effective management for salt-affected soil amelioration. However, little is known whether the straw interlayer could boost soil fertility and improve bacterial community, while reducing irrigation water consumption by 10% in the third and fourth years after application. Therefore, the legacy effect of the combination of straw interlayer and irrigation water reduction was investigated following the three treatments: (i) no straw interlayer plus 100% irrigation (CK); (ii) straw interlayer plus 100% irrigation (SI+W100), and (iii) straw interlayer plus 90% irrigation (SI+W90). SI+W90 significantly increased 0–40 cm soil organic carbon (SOC) content and soil SOC/TN (C/N) ratio than CK by 12% and 13%, respectively. In addition, SI+W90 significantly decreased 0–40 cm soil total nitrogen (TN) and SOC contents by 10% and 8% respectively, but significantly increased 0–40 cm soil C/N ratio by 9%, compared to SI+W100. Furthermore, SI+W100 and SI+W90 significantly increased the relative abundance of Proteobacteria, Bacteroidetes, and Nitrospirae than that of CK, while SI+W90 significantly decreased the relative abundance of Acidobacteria compared to SI+W100, and these bacterial phyla were significantly correlated with 0–40 cm SOC content. Notably, there was no significant correlation between crop yield and soil parameters in 2018 with the relatively low yield, while crop yield was significantly correlated with 0–20 cm SOC content and the relative abundance of Proteobacteria and Firmicutes in 2019 with relatively high yield. These results indicated that both soil fertility and certain bacterial phyla may contribute to yield variation. Therefore, SI+W90 maintained crop yield in the two years. In conclusion, the combination of straw interlayer and irrigation water reduction may be more recommended for agricultural sustainable development under the condition of limited irrigation water supply in the arid and salt-affected regions.
      原文链接:
    • NSTL
    • Elsevier

    Grapevine crop evapotranspiration and crop coefficient forecasting using linear and non-linear multiple regression models

    Netzer Y.Munitz S.Ohana-Levi N.Ben-Gal A....
    11页
    查看更多>>摘要：? 2021 Elsevier B.V.Vineyard irrigation management relies on accurate assessment of crop evapotranspiration (ETc). ETc is affected by the by type of plant, its physiological properties, and meteorological parameters. Rapid measurement of these factors facilitates quantification of ETc and enables skilled decision-making for data-driven irrigation. Our main objective was to quantify the performance of different modeling approaches for forecasting seasonal ETc using meteorological and vegetative data (e.g., leaf area) from five consecutive growing seasons (2013–2017) of Vitis vinifera 'Cabernet Sauvignon' vines. Time series of ETc was acquired from water balance from vines grown in drainage lysimeters within the vineyard. ETc forecasts were generated for each season using twelve regression models: six linear and six non-linear multivariate adaptive regression spline (MARS) models. Each regression model constituted a unique combination of variables, some relying on crop coefficient (Kc) and others based on direct ETc forecasting. The models were trained using data from four growing seasons and compared via measures of coefficient of determination (R2), residual standard deviation, and coefficient of variation. Each model was then tested using ETc forecasts for a fifth growing season, and compared to the measured ETc values using correlation, root mean squared error (RMSE), and normalized RMSE. Finally, a mean-seasonal rolling RMSE with a window of 7 days was used to assess the accuracy of the different models. The results show a clear advantage to using non-linear modeling for ETc forecasting (average RMSE range of 0.81–1.05 vs. 0.64–0.71 mm day?1, respectively). Furthermore, direct forecasting and Kc-based methods yielded similar results, and all models benefited from the incorporation of leaf area data. Similar outcomes were found for the rolling RMSE analysis, with improved model accuracy credited to the inclusion of leaf area, especially early in the season. Our findings confirm that advanced algorithms promote site-specific and location-oriented irrigation management.
      原文链接:
    • NSTL
    • Elsevier

    Comparison of hydrological and vegetation remote sensing datasets as proxies for rainfed maize yield in Malawi

    Anghileri D.Sheffield J.Bozzini V.Molnar P....
    11页
    查看更多>>摘要：? 2021Weather Index-based Insurances (WIIs) have emerged as a promising risk coping mechanism to compensate for weather-induced damage to rainfed agriculture. Remote sensing may provide cost-effective information capable of discriminating the weather spatial variability thus reducing the spatial basis risk, i.e., the mismatch between the weather-based index triggering the insurance payout and the actual damage experienced by the farmers, which is often one of the causes hindering the wide implementation of WIIs. In this work we assess which indices based on remote sensing datasets are the best proxy indicators for rainfed maize yield in Malawi. We analyse the spatial (district scale) and temporal (monthly) correlations of historical maize yield data and several remote sensing datasets including the Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset, the ESA CCI Soil Moisture combined dataset (version 4.2), the Evaporative Stress Index (ESI) from the Atmosphere-Land Exchange Inversion model (ALEXI), the MOD13Q1 Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). With respect to the previous literature, this work exploits a historical crop yield dataset at the sub-national level which allows us to analyse the correlation of the hydro-meteorological and vegetation variables at a higher spatial resolution than what is commonly done (i.e., at the national level using FAO national yield statistics) and ultimately explore the issues related to WII spatial basis risk. Results show that the correlations between crop yield and satellite datasets show high spatial and temporal variability, making it difficult to identify a unique WII index that is at the same time simple and effective for the entire country. Precipitation, particularly the standardized March precipitation anomaly, has the highest correlations with maize yield (with Pearson correlation values higher than 0.55), in Central and South Malawi. Soil moisture and NDVI do not add much value to precipitation in anticipating historical maize yield at the district scale. From a methodological perspective, our work shows that WII indexes are best identified by: i) considering datasets with fine spatial resolution, whenever possible; ii) accounting for the vulnerability of the different crop growing stages to water-stress; iii) distinguishing between water scarce and water abundant events.
      原文链接:
    • NSTL
    • Elsevier

    Effects of saline water mulched drip irrigation on cotton yield and soil quality in the North China Plain

    Wang H.Zhang A.Zhang J.Sun C....
    11页
    查看更多>>摘要：? 2021 Elsevier B.V.The shortage of freshwater resources is a considerable challenge for agricultural production in the North China Plain (NCP). Safe and efficient use of saline water resources is thus urgently required. To reveal the effects of different salinities of irrigation water on the yield and soil quality of mulched drip-irrigated cotton, a field experiment was conducted from 2017 to 2019. Five salinity levels of irrigation water were included: 1.3 (T1, control), 5.4 (T2), 8.8 (T3), 12.4 (T4) and 15.9 (T5) dS·m?1. The results showed that the harvesting density and seed cotton yield increased first and then decreased with increasing salinity of irrigation water. Saline water irrigation with salinity ≤ 8.8 dS·m?1 did not lead to salt accumulation in the main root zone (0–60 cm) with each passing year but a decrease of salt in 2018. In the third year of saline water irrigation, with the increase in irrigation water salinity, the electrical conductivity of the saturated soil extract (ECe), soil sodium adsorption ratio (SAR), pH and bulk density (BD) in the plow layer gradually increased. However, the soil saturated hydraulic conductivity (Ks), water stable macroaggregate (> 0.25 mm) content, catalase (CAT) and urease (URE) activity decreased with increasing salinity. Moreover, the soil organic carbon (SOC) content and alkaline phosphatase (ALP) activity increased first and then decreased. Irrigation water salinity ≤ 5.4 dS·m?1 had no significant effect on most physicochemical properties, such as pH, SOC content, BD, Ks, water stable aggregate content and activities of CAT, URE and ALP. Compared with the T1 treatment, the soil quality index (SQI) of T2, T3, T4 and T5 treatments decreased by 1.2%, 10.5%, 16.5% and 23.7%, respectively. Considering cotton yield, soil salt accumulation and SQI, mulched drip irrigation is conducive to the sustainability of cotton with saline water levels below 5.4 dS·m?1.
      原文链接:
    • NSTL
    • Elsevier

    The social wellbeing of irrigation water. A demand-side integrated valuation in a Mediterranean agroecosystem

    Alcon F.Zabala J.A.Martinez-Garcia V.de-Miguel M.D....
    15页
    查看更多>>摘要：? 2021 The AuthorsIrrigation water is a vital input for agricultural production. The supply of irrigation water to crops enhances land productivity and affects the agroecosystem functioning. Agroecosystems co-provide a wide range of agroecosystem services and disservices, which contribute positively and negatively, respectively, to human wellbeing. Therefore, irrigated agroecosystems produce several positive and negative outcomes in relation to society, and agricultural water management is key to the provision of adequate incentives for the enhancement of social wellbeing. In such a context, the aim of this work was to value the contribution of water to the provision of agroecosystem services and disservices, as a way to summarise the contribution of irrigation to social wellbeing. To this end, a demand-side integrated valuation of agroecosystem services and disservices was carried out for both rain-fed and irrigated agriculture in two different agroecosystems of the Region of Murcia (south-eastern Spain), a semi-arid western Mediterranean region characterised by water scarcity. In addition, the intensity of the agricultural water use was considered by distinguishing traditional and highly-intensive irrigated agroecosystems. Almond and lemon, two woody crops, were employed to develop the economic valuation in rain-fed and irrigated agroecosystems, respectively. The assessment of biophysical indicators to quantify the provision of services and disservices and their economic valuation, using market and non-market methods, were used. The results show that the contribution of water to social wellbeing is valued at 9000–12,300 €/ha/year, being greater when the intensive use of agricultural water is promoted. The net economic value of all categories of agroecosystem services and disservices increases when irrigation water is supplied. Notwithstanding, the greatest contribution is due to the increase in provisioning services, mainly food provision in the case of the highly-intensive agroecosystem. Traditional irrigated agroecosystems make a greater contribution to regulating and cultural agroecosystem services. Hence, agricultural water management should focus on increasing the contribution of irrigated agroecosystems to human wellbeing.
      原文链接:
    • NSTL
    • Elsevier

    New cognition on the response of reference evapotranspiration to climate change in China using an independent climatic driver system

    Sun X.Hu Z.Lin S.Wang F....
    13页
    查看更多>>摘要：? 2022 Elsevier B.V.Reference evapotranspiration (ETo) is a key parameter in hydrometeorological studies, but dependent climatic drivers can induce uncertainties when attributing changes of ETo to climate change, and many studies have neglected this issue. In this study, two sets of climatic drivers (the independent/dependent driver system) were used to attribute changes of Penman–Monteith ETo to climatic variables in China. Results show ETo presented an upward–downward–upward trend with the change points in 1978 and 1996. Using the dependent driver system, the sensitivity coefficient and the contribution of average air temperature (Tmean) were underestimated by at least 57% and 46% during three periods (1960–1978, 1979–1996 and 1997–2019). The largest relative changes of contribution of Tmean (C_Tmean) were found in southeast of China with high temperatures, while largest relative changes of C_Tmean peaked in autumn. Therefore, in order to promote the acquisition of relatively objective results in attributing changes of ETo, the independence of climatic drivers must be carefully addressed. Using the independent driver system, we found ETo was most sensitive to Tmean in spring, summer and winter. Tmean was always a dominant factor for the largest percentage (at least 41%) of grids across China, followed by WS (at most 30%) during three periods. However, the large changes of WS in northwest of China promote the changes of ETo during 1960–1978 and 1979–1996, leading to increased WS, decreased WS and increased Tmean responsible for the upward–downward–upward trend of ETo for entire China as a whole during three periods. This study emphasizes the effect of Tmean in changing ETo during the entire study area, and help improve our understanding of the evolution of ETo and providing a guideline for water resource management and water use planning for agriculture.
      原文链接:
    • NSTL
    • Elsevier