Agricultural Water Management最新文献

Excessive agricultural nitrogen application can lead to low nitrogen use efficiency, but the use of drip irrigation with plastic film mulching has been shown to improve crop yield, quality, and nitrogen use efficiency. Field experiments were conducted in 2020 and 2021 at the Special Potato Experimental Station, China Agricultural University, in Rizhao City, Shandong Province, China. The aim was to investigate the effects of different soil wetted percentages and nitrogen (N) application rates on soil nitrate-N (NO₃-N) distribution and the growth of sweet potatoes (Ipomoea batatas L.). Three irrigation levels (no irrigation, P0; soil wetted percentage 30 %, P1; and soil wetted percentage 60 %, P2) and three N-application rates (90 kg hm⁻², N1; 180 kg hm⁻², N2; and 270 kg hm⁻², N3) were designed. Various parameters such as soil water and nitrate-N content, dry matter, tuber grade, yield, quality, and nitrogen use efficiency were measured. The results showed that the soil water content was lower in the treatment with N-application rate of 180 kg hm⁻² compared to the treatments with N-application rates of 90 kg hm⁻² and 270 kg hm⁻². Increasing nitrogen application rates led to an increase in soil nitrogen content and residue at a depth of 0–40 cm. The soil nitrate-N residue in the treatment with N-application rate of 180 kg hm⁻² was significantly lower than that in the treatment with N-application rate of 270 kg hm⁻² treatment. Sweet potato growth, tuber dry matter, number and weight of large and medium tubers, and yield initially increased and then decreased with increasing nitrogen application rates. In the soil wetted percentage of 60 % treatment, the tuber yield increased in the treatment with N-application rate of 180 kg hm⁻² compared to the treatments with N-application rates of 90 kg hm⁻² and 270 kg hm⁻². A moderate nitrogen application rate improved tuber quality, including the contents of crude protein, starch, and soluble sugar. Nitrogen use efficiency, sweet potato nitrogen uptake amount, and nitrogen use efficiency initially increased and then decreased with increasing nitrogen application rates in soil wetted percentage of 60 % treatment. Irrigation increased nitrogen uptake efficiency and nitrogen use efficiency. In the treatment with N-application rate of 180 kg hm⁻², nitrogen use efficiency in the soil wetted percentages of 30 % and 60 % treatments were higher than that in the treatment without irrigation. In conclusion, the optimal combination of 180 kg/hm² and soil wetted percentage of 60 % was found to be suitable for sweet potato cultivation in the study area, considering factors such as soil nitrate-N residue, sweet potato yield and quality, and nitrogen use efficiency.

Historically, forestlands have been widely recognized to lose more water through evapotranspiration (ET) than croplands. Using remote sensing data from MODIS (Moderate Resolution Imaging Spectroradiometer) with an 8-day temporal and 500 m spatial resolution, we compared the annual ET between croplands and forestlands in the Yazoo River Basin (YRB), a humid subtropical region in Mississippi, USA, over a 21-year period from 2001 to 2021. Based on the Mann-Kendall test, there were significant increasing trends in annual ET for the croplands (τ > 0.44, p < 0.01) but not for forestland over the 21-year period. According to Pettitt’s test, there was an abrupt change (or turning point) in annual ET starting in 2011 for the croplands. Using the time at this turning point (i.e., 2011) along with the Kolmogorov-Smirnov test, we found that there was a very significant difference (α = 0.05) in annual ET between croplands and forestlands with 19 % higher ET in the croplands over the 11-year period from 2011 to 2021. This occurred because of increasing irrigated cropland areas in the YRB during this period, providing more water for ET. Our finding on croplands lost more water than forestlands through ET challenge the traditional concept on how forestlands and croplands influence ET.

A comprehensive knowledge of irrigation information is crucial for agricultural water management. However, current investigations have mainly focused on extracting spatial extent of irrigated farmlands and quantifying irrigation amounts, lacking an understanding of irrigation timing at the field scale. In this study, a novel approach for detecting irrigation events from soil moisture (SM) time-series was proposed. To this end, in-situ SM measurements with different depths (10 cm, 25 cm, and 50 cm) were primarily decomposed into seasonal, trend, and residual components using the Bayesian Estimator of Abrupt change, Seasonal change, and Trend (BEAST) model over a period of seven years from 2014 to 2020. The rationale for the determination of a specific irrigation timing relies on the observed rising abrupt change of SM time-series in its trend component when precipitation is unavailable. Specifically, the BEAST model was primarily optimized over two irrigated farmlands in the University of Nebraska Agricultural Research and Development Center near Mead, Nebraska, US. were subsequently used to identify irrigation. Results indicate that the decomposed SM time-series by the BEAST model correlate well with in-situ SM measurements with an average coefficient of determination of 0.98 and 0.97 over farmlands with continuous maize and maize-soybean rotation, respectively. Furthermore, it was found that SM measurements with a depth of 10 cm are optimal for detecting irrigation timing over the study area. When compared with local irrigation records, the accuracy of detected irrigation timing over farmlands with continuous maize and maize soybean rotation can reach 84 % and 89 %, respectively, revealing promising prospects for deriving irrigation timing with SM measurements. These results provide a reference for detecting irrigation timing using satellite-derived SM data.

Sweet Cherry (Prunus avium L.) is a highly sought-after fruit in the global market due to its nutritional value and appealing sensory characteristics. This paper investigated the indicators of photosynthetic physiological characteristics and changes in soil physical properties of different varieties of sweet cherries grown in the Loess Plateau region, which strengthened the research on the ecological aspects of photosynthetic physiology of sweet cherries. By analyzing the characteristics and similarities of photosynthesis processes in different varieties, the results were obtained to showed that most of these sweet cherries showed obvious inhibition of photosynthesis at noon. The daily net photosynthetic rate (Pn) variation graph displayed a typical bimodal curve, with the lowest values occurring successively at 12:00 pm and 14:00. The Pn of sweet cherry is mainly influenced by water use efficiency (WUE) and transpiration rate (Tr). Rainier and Summit exhibited the best performance in the daily mean of Pn due to the interplay between photosynthetic physiological and ecological factors. To affect the photosynthesis of sweet cherries, covering the greenhouses with plastic film would impact the changes in light intensity and temperature. The crop management mode of uniform mulching and drip irrigation can affect the water content of different soil depths, leading to a reduction in Pn due to the varying WUE and Tr of different varieties of sweet cherries. The order of photosynthetic physiological characteristics of different varieties of sweet cherries, as revealed by principal component analysis, was as follows: Rainier > 4YJimei > 2YTieon > 4YTieon > Summit > Jimei > Tieon > Hongdeng. These results provide a scientific theoretical basis for the introduction, promote the production of sweet cherries and provide a new understanding of the plantation management mode.

In recent years, farmers in Maharashtra have engaged in aquaculture-sugarcane farming, with those from the Ujjani reservoir area practicing integrated sugarcane farming using tilapia farm discharge water. This study aims to systematically compare the economic viability and environmental externalities between sugarcane-only farms (SF) and tilapia-sugarcane farms (TSF) within the commanding area of the Ujjani reservoir. Primary data was collected from the 160 farmers (80 SF, 80 TSF), and Secondary data were collected from published literature, the Department of Fisheries, and fisheries cooperative societies. Data analysis used descriptive statistics to characterize demographic profile, farm performance (cost-benefit analysis, production function), and farmers' perceptions. Principal component analysis (PCA), cluster analysis, and two-way MANOVA were employed for environmental assessments. Results indicate that TSF yields higher profits across all cost scenarios, with a Benefit-Cost ratio of 1.69, surpassing SF's ratio of 1.55. The Cobb-Douglas production function analysis reveals increasing returns to scale for both SF and TSF, with the sum of coefficients exceeding one. The results of the Principal Component Analysis (PCA) indicate that 31 variables rated by sugarcane growers were extracted into ten components, which explains 65 % of the total variance for SF and 62 % for TSF. K-means cluster analysis grouped these components into four clusters, termed externalities of irrigation. A Two-way MANOVA indicated significant differences (p < 0.05) between SF and TSF farmers' perceptions of these externalities, with further distinctions among four farmer categories and significant interaction effects. Policy recommendations include financial incentives and environmental regulations to support TSF adoption, sustainable land-use policies, and institutional interventions for management practices and market research. These measures address challenges and promote the transition to sustainable TSF practices.

Simulating the potential impacts of future climate change on hydrology and crop yields provides opportunities to select suitable crops and improve the climate change resilience of agricultural systems. The Texas High Plains (THP), a significant agricultural region in the United States (U.S.), is confronted with substantial challenges from climate change risks and the depletion of the Ogallala Aquifer’s groundwater supply. This study quantified the influences of climate change on water demands and yields of staple crops under both irrigated farming and dryland farming in addition to continuous fallow at Bushland of the THP using an improved Soil and Water Assessment Tool (SWAT) model. The model incorporates management-allowed depletion (MAD) irrigation scheduling and a dynamic carbon dioxide (CO₂) input method (SWAT-MAD-CO₂). Future climate data projected by the latest bias-corrected 22 General Circulation Models (GCMs) of the Coupled Model Intercomparison Project 6 (CMIP6) under three Shared Socioeconomic Pathway (SSP) emission scenarios of SSP1–2.6, SSP2–4.5, and SSP5–8.5 were used. The modeling results indicated a mixed change in actual evapotranspiration (ET_a) across different irrigated crops, while ET_a generally increased (2.4%-11.5%) for dryland crops and continuous fallow under the future climate change scenarios compared to the baseline period (1986–2015). Irrigation water use (except irrigated cotton) was expected to decrease, with larger reductions under three SSP scenarios for future irrigated winter wheat (16.1%-85.5%) and irrigated sorghum (18.1%-78.0%) compared to other irrigated crops over two 30-year periods. Regarding crop yields, the annual yield for future irrigated cotton, irrigated sunflower, and dryland cotton was expected to increase by 109.3%-142.7%, 1.1%-9.4%, and 93.9%-150.2%, respectively, compared to the baseline period. Conversely, the simulated yields of the irrigated sorghum and dryland soybean showed the greatest reductions of 6.4%-27.3% and 5.9%-51.3%, respectively, under the climate change scenarios relative to the baseline period.

Agroecosystems play an important role in carbon sequestration and water consumption of global terrestrial ecosystems. However, the magnitude, pattern, and regulation of the ratio of transpiration to evapotranspiration (T/ET), and also water use efficiency at ecosystem (WUE_e) and canopy (WUE_c) scales, remain unclear in agroecosystems in arid areas. In this study, a six-year synchronous observation of water-carbon fluxes and sap flow by the eddy covariance and sap flow techniques was conducted in an irrigated vineyard in northwest China. We found that T/ET, WUE_e, and WUE_c changed dynamically at the daily (1.0–98.6%, 0.3–4.9 g C kg^–1 H₂O, 0.4–5.9 g C kg^–1 H₂O) and monthly (26.0–71.0%, 0.8–1.8 g C kg^–1 H₂O, 2.3–3.2 g C kg^–1 H₂O) scales. At the annual scale, the range was relatively narrow, with mean values of 61.7% ± 3.7%, 1.5 ± 0.1 g C kg^–1 H₂O, and 2.4 ± 0.3 g C kg^–1 H₂O, respectively (mean ± standard deviation). Biological and environmental factors regulated their dynamics on different temporal scales. Specifically, leaf area index (LAI) and canopy conductance (g_c) mainly determined the seasonal variation of T/ET, WUE_e, and WUE_c at the daily and monthly scales. However, at the annual scale, vapor pressure deficit (VPD) and air temperature (T_a) became the main influencing factors. These results highlight the complexity of water-carbon coupling in agroecosystems and the necessity of considering specific factors at different temporal scales when modeling and managing agricultural water resources. In addition, there is still great water-saving potential from the perspective of WUE in the cultivation of vines in Northwest China.

The traditional view of Na⁺ as harmful and Ca²⁺ as beneficial doesn't always apply in multi-cationic soil solutions. Initially, adding Ca²⁺ promotes Na⁺ leaching, reducing salinity, but excess Ca²⁺ becomes counterproductive. As Na⁺ leaches, the soil's Ca²⁺-Na⁺-Mg²⁺ mix shifts to Ca²⁺-K²⁺-Mg²⁺, Ca²⁺'s function changes, even causing the opposite effect. To investigate the complex mechanism of Ca²⁺ to Na⁺-Mg²⁺ and K⁺-Mg²⁺, we conducted an indoor soil column experiment using saline water (4 dS m⁻¹) with different cation compositions [Na⁺-Ca²⁺-Mg²⁺ (NCM), Na⁺-Mg²⁺ (NM), K⁺-Ca²⁺-Mg²⁺ (KCM), K⁺-Mg²⁺ (KM)] and deionized water as the control (CK). The results showed that NM exhibited the highest crack volume, while KM had the greatest macropore volume, with NM having approximately 15 % more crack volume than KM. Notably, only NM displayed a more pronounced inclination towards pore anisotropy value of 0 when compared to CK. NCM and KCM had higher pore anisotropy values than NM and KM. KM and KCM had more cracks angled ranging from 45–90° than NM and NCM. KCM notably decreased transitional macropores < 5.5 mm in length compared to KM, with no significant difference (P > 0.05) observed in widths < 2.5 mm between KCM and KM. NM displayed the shallowest macropore distribution and the highest variability in macropore length among all treatments. Only NCM showed significantly reduced variability in both macropore length and width compared to CK. In summary, Ca²⁺ exhibited distinct action patterns on K⁺-Mg²⁺ and Na⁺-Mg²⁺. For specific soil types and cationic compositions, Ca²⁺ may not fully exert its amendment effects. However, Ca²⁺'s effect is soil-specific, necessitating comprehensive studies across varied soil types.

Water demand forecasting plays a crucial role in the efficient planning and management of water distribution systems, particularly in regions facing complex climatic and irrigation dynamics. Fluctuations in water demand occur across both seasonal and sub-seasonal timeframes, driven by diverse factors including weather variations and irrigation management choices. Traditionally, irrigation water demands have been forecasted separately for these two temporal scales, using only subsets of factors most relevant to each specific temporal scale. Sub-seasonal water demands are, however, influenced by annual decisions. This is particularly true in large, complex water systems with extensive water trade and diverse climate conditions. For such systems, a sub-seasonal scale forecast with consideration of annual influencing factors could add significant value to operational management. This paper presents an empirical approach to disaggregate the annual allocation water use for irrigation to a monthly time scale for seven inter-connected regions of the southern Murray-Darling Basin (sMDB), Australia. First, an extensive literature review was conducted to identify the crop coefficient (K_c) values for the crops grown in the sMDB throughout their growth cycles. Following this, a set of monthly K_c values were adopted for nine irrigation activities. Subsequently, the annual allocation water use was disaggregated into monthly volumes using within year proportion of crop water requirement, calculated from reference evapotranspiration and crop coefficients for the seven regions in the sMDB. Finally, the results of the disaggregation approach were compared against the diversion data matched to each region. The disaggregated allocation water use aligns well with the monthly diversion volume. Based on this approach, a monthly water demand forecast model accounting for annual and monthly influencing factors could be developed.