Farmland Water Research Articles

The Impact of Typhoons on Agricultural Productivity—Evidence from Coastal Regions of China

The impact of natural disasters on agricultural production has garnered global attention. This study takes typhoons as an example, employing their movement paths to construct a difference-in-differences (DID) model and combining survey data from Rural Fixed Observation Spots to estimate changes in agricultural productivity from coastal regions of China, including Guangdong, Fujian, and Zhejiang provinces. This study finds that typhoons significantly deteriorate local agricultural productivity. Specifically, the planting income per mu and planting income per capita of rural households have decreased by 11% and 14%, respectively, while agricultural total factor productivity (TFP) has dropped by 3.7%. The decline in productivity can be attributed to two channels. Firstly, typhoons directly damage crops, leading to reduced total output. Secondly, in anticipation of typhoons, rural households increase asset input but reduce labor input and intermediate goods, resulting in the misallocation of agricultural inputs, which further diminishes productivity. The cost-benefit analysis indicates that to compensate for 20% of the negative impact of typhoons on agricultural productivity, local financial funds ranging from 3.4 million to 20 million yuan are required. Therefore, it is imperative for the Chinese government to strengthen the natural disaster warning system and improve farmland water conservancy infrastructure to mitigate the misallocation of agricultural inputs by rural households.

Can agricultural water rights trading promote intensive and economical utilization of water resources in China based on the difference-in-differences model?

IntroductionImproving the level of intensive and economical utilization of water resources is an important step towards water and food security.MethodsBased on China’s provincial panel data from 2015 to 2021, this study uses a difference-in-differences (DID) model to examine the effect of agricultural water rights trading on intensive and economical utilization of water resources.ResultsThe results show that agricultural water rights trading can significantly promote an intensive and economical utilization of water resources at the 1% level, and the results remain valid even after a series of robustness tests. Further analysis indicates that agricultural water rights trading can promote better utilization of water resources by significantly reducing agricultural water consumption at the 1% level and reducing the proportion of agricultural water consumption at the 5% level to optimize water use structure. Moreover, in regions with scarce water resources, strong water resource constraints, more farmland water conservancy projects, and greater economic development, the promoting effect of agricultural water rights trading on the intensive and economical use of water resources is more significant.DiscussionThis study provides evidence of the impact of China’s agricultural water rights trading policy and offers new ideas and experiences to improve China’s water resource utilization efficiency. The contributions of this work are mainly threefold. First, this paper clearly distinguishes the intensive conservation of water resources, presents an evaluation index system, and complements the existing literature on measuring the intensive and economical utilization of water resources. Second, the path of agricultural water rights trading is analyzed to improve the level of intensive and economical utilization of water resources, thus enriching related research on transmission mechanisms. Third, the heterogeneity of the influence of different regional conditions on the level of intensive and economical utilization of water resources is discussed to improve relevant policies.

Research and application of the method of measuring the effective utilization coefficient of irrigation water on farmland

Abstract Under the guidance of the new water management idea of “water conservation priority, spatial balance, system management, and two-handed effort,” the National Water Conservation Action Plan released in 2019 puts forward the key task requirements of “double control of total intensity,” in which the effective utilization coefficient of irrigation water in farmland is one of the indicators of water intensity control. The effective utilization coefficient of irrigation water is one of the water intensity control indicators. At present, there are practical difficulties in measuring the effective utilization coefficient of irrigation water in farmland, such as the low measurement rate of water consumption in the current irrigation area, the high input equipment and manpower requirements, and the difficulty in accurately measuring the net irrigation water absorbed by crops. Therefore, this paper proposed the method of “field monitoring - remote sensing inversion - a comprehensive analysis of data.” The method of “field monitoring - remote sensing inversion - econometric model construction - comprehensive analysis” was proposed in this paper, which can obtain gross irrigation water utilizing field monitoring, overcome the difficulty of accurate monitoring of net irrigation water using remote sensing, and carried out a comprehensive analysis, to realize the scientific, effective and rapid calculation of effective utilization coefficient of irrigation water. This method was applied to the Meixian District, Guangdong Province, and the research results show that it not only generates reliable measurement data but also alleviates workload. Furthermore, it offers scientific measurement techniques for water use efficiency indicators, facilitating the achievement of “dual control over total volume and intensity.” This paper provides an important technical basis for the rational allocation of water consumption and scientific approval of water use plans and supports the assessment of the effectiveness of water-saving irrigation development and the reasonable evaluation of the water-saving potential of farmland irrigation, thus providing a basis for local government departments at all levels to make planning, scientific decision-making, and macro-management.

County-Level Cultivated Land Quality Evaluation Using Multi-Temporal Remote Sensing and Machine Learning Models: From the Perspective of National Standard

Scientific evaluation of cultivated land quality (CLQ) is necessary for promoting rational utilization of cultivated land and achieving one of the Sustainable Development Goals (SDGs): Zero Hunger. However, the CLQ evaluation system proposed in previous studies was diversified, and the methods were inefficient. In this study, based on China’s first national standard “Cultivated Land Quality Grade” (GB/T 33469-2016), we constructed a unified county-level CLQ evaluation system by selecting 15 indicators from five aspects—site condition, environmental condition, physicochemical property, nutrient status and field management—and used the Delphi method to calculate the membership degree of the indicators. Taking Jimo district of Shandong Province, China, as a case study, we compared the performance of three machine learning models, including random forest, AdaBoost, and support vector regression, to evaluate CLQ using multi-temporal remote sensing data. The comprehensive index method was used to reveal the spatial distribution of CLQ. The results showed that the CLQ evaluation based on multi-temporal remote sensing data and machine learning model was efficient and reliable, and the evaluation results had a significant positive correlation with crop yield (r was 0.44, p < 0.001). The proportions of cultivated land of high-, medium- and poor-quality were 27.43%, 59.37% and 13.20%, respectively. The CLQ in the western part of the study area was better, while it was worse in the eastern and central parts. The main limiting factors include irrigation capacity and texture configuration. Accordingly, a series of targeted measures and policies were suggested, such as strengthening the construction of farmland water conservancy facilities, deep tillage of soil and continuing to construct well-facilitated farmland. This study proposed a fast and reliable method for evaluating CLQ, and the results are helpful to promote the protection of cultivated land and ensure food security.

Estimation of Soil Moisture during Different Growth Stages of Summer Maize under Various Water Conditions Using UAV Multispectral Data and Machine Learning

Accurate estimation of soil moisture content (SMC) is vital for effective farmland water management and informed irrigation decision-making. The utilization of unmanned aerial vehicle (UAV)-based remote sensing technology to monitor SMC offers advantages such as mobility, high timeliness, and high spatial resolution, thereby compensating for the limitations of in-situ observations and satellite remote sensing. However, previous research has primarily focused on SMC diagnostics for the entire crop growth period, often neglecting the development of targeted soil moisture modeling paradigms that account for the specific characteristics of the canopy and root zone at different growth stages. Furthermore, the variations in soil moisture status between fields, resulting from the hysteresis of water flow in irrigation channels at different levels, may influence the development of soil moisture modeling schemes, an area that has been seldom explored. In this study, SMC models based on UAV spectral information were constructed using Random Forest (RF) and Particle Swarm Optimization-Support Vector Machine (PSO-SVM) algorithms. The soil moisture modeling paradigms (i.e., input–output mapping) under different growth stages and soil moisture conditions of summer maize were systematically compared and discussed, along with the corresponding physical interpretability. Our results showed that (1) the SMC modeling schemes differ significantly across the various growth stages, with distinct input–output mappings recommended for the early (i.e., jointing, tasselling, and silking stages), middle (i.e., blister and milk stages), and late (i.e., maturing stage) periods. (2) these machine learning-based models performed best at the jointing stage, while subsequently, their accuracy generally exhibited a downward trend as the maize grew. (3) the RF model demonstrates superior robustness in estimating soil moisture status across different fields (moisture conditions), achieving optimal estimation accuracy in fields with overall higher SMC in line with the PSO-SVM model. (4) unlike the RF model’s robustness in spatial SMC diagnostics, the PSO-SVM model more reliably captured the temporal dynamics of SMC across different growth stages of summer maize. This study offers technical references for future modelers in UAV-based SMC modeling across various spatial and temporal conditions, addressing both the types of models as well as their input features.

A Review of Drip Irrigation’s Effect on Water, Carbon Fluxes, and Crop Growth in Farmland

The substantial depletion of freshwater reserves in many pivotal agricultural regions, attributable to the dual pressures of global climate change and the excessive extraction of water resources, has sparked considerable apprehension regarding the sustainability of future food and water security. Drip irrigation, as an efficient and precise irrigation method, reduces water loss caused by deep percolation, soil evaporation, and runoff by controlling the irrigation dosage and frequency, thus improving the efficiency of water resource utilization. Studies have shown that compared with traditional irrigation methods, drip irrigation can significantly decrease water consumption, optimize the water–energy relationship by reducing soil evaporation, increase the leaf area index, and promote crop growth, thereby enhancing plant transpiration. Although more wet and dry soil cycles from drip irrigation may increase soil CO2 emissions, it also enhances crop photosynthesis and improves crop net ecosystem productivity (NEP) by creating more favorable soil moisture conditions, indicating greater carbon sequestration potential. The advantages of drip irrigation, such as a short irrigation cycle, moderate soil moisture, and obvious dry and wet interfaces, can improve a crop’s leaf area index and biomass accumulation, improve root dynamics, promote the distribution of photosynthetic products to the aboveground parts, and thus enhance crop yields. This study highlights the potential for the application of drip irrigation in arid regions where resource optimization is sought, providing strong technical support for the achievement of sustainable agricultural development. Future research needs to consider specific agricultural practices, soil types, and environmental conditions to further optimize the implementation and effectiveness of drip irrigation.

Distribution and Variation of Soil Water and Salt before and after Autumn Irrigation

Autumn irrigation is a key measure for alleviating soil salinity and promoting sustainable agricultural development in the Hetao Irrigation district; however, only a part of farmland is irrigated in autumn during the non-growth period of crops, which leads to the redistribution of soil water and salt between autumn-irrigated land (AIL) and adjacent non-autumn-irrigated land (NAIL) after autumn irrigation. To explore the distribution and variation of soil water and salt in different positions of AIL and NAIL after local autumn irrigation and reveal the interaction range between AIL and NAIL, field experiments were carried out for two years in typical test areas. The results showed that compared with non-autumn irrigation, autumn irrigation improved the distribution uniformity of soil water and salt profiles in both horizontal and vertical directions; after autumn irrigation, the water content of the soil at the nearest sampling point to the boundary in the AIL increased the least, but the desalination rate was the greatest, while the water and salt contents of the soil within 45 m from the sampling points to the boundary in the NAIL both increased significantly. NAIL received the drainage of AIL and made the groundwater level after the rise in AIL fell quickly back, but unreasonable autumn irrigation caused the groundwater level of AIL to remain at a high level before freezing, exacerbating the risk of groundwater carrying salts to the surface soil during the freezing and thawing period, detrimental to the growth of crops in the next spring. The research results are of great significance to the rational use of farmland water resources and the improvement of soil salinization in cold and dry areas.

Multispectral Inversion of Citrus Multi-Slope Evapotranspiration by UAV Based on Modified RSEB Model

Evaptotranspiration (ETc) is a crucial link in the farmland water cycle process. To accurately obtain the citrus ETc in different slope positions, the METRIC, RSEB, and FAO Penman–Monteith (P-M) models were constructed based on unmanned aerial vehicle (UAV) multispectral images to invert the ETc values. The ETc of citrus calculated by the P-M model was used as a reference standard, and the accuracy of the ETc inversion was evaluated by the METRIC model and the RSEB model. The results showed that the R2, RMSE, and SE of the METRIC model and the RSEB model were 0.396 and 0.486, 4.940 and 3.010, and 4.570 and 2.090, respectively, indicating a higher accuracy of the RSEB model for inverting the ETc values. Furthermore, the accuracy of the RSEB model could be improved by introducing the optimal correction coefficient (after correction: RMSE = 1.470, SE = 0.003). Based on the modified RSEB model, the ETc values of the citrus in different slope positions were obtained. We also found that the middle slope ETc > the top slope ETc > the bottom slope ETc, indicating that the slope position indeed affected the citrus ETc. This research provides a favorable framework for the ETc inversion, and the results are of theoretical and practical importance to realize crop water conservation.

Multi-objective optimization of farmland water level and nitrogen fertilization management for winter wheat cultivation under waterlogging conditions based on TOPSIS-Entropy

Aiming at the problems of reduced winter wheat yield and aggravation of nitrogen leaching pollution caused by the waterlogging in the Middle-Lower Yangtze Plain, China, a two-year field experiment with three farmland water levels (W40, W60, W80) and three nitrogen application rates (N150, N225, N300) as well as a non-waterlogged treatment (CK) was carried out, to investigate the coupling effects of farmland water level and nitrogen application rate on the plant growth, grain yield, crop water productivity (WPC) and nitrogen load with waterlogging conditions. Three man-made waterlogging events were applied at winter wheat jointing-booting stage, heading-flowering stage and grain filling stage, respectively. The results indicated that with the farmland water level decreased from −40 cm to −60 cm and the nitrogen application rate increased from 150 kg∙ha−1 to 225 kg∙ha−1, the plant height, aboveground dry matter, leaf area index, spike length, grain yield, effective panicles, grain number per ear, 1000-grain weight and WPC in the waterlogging field increased significantly. However, since the nitrogen application rate exceeded 225 kg∙ha−1 and farmland water level lowered more than −60 cm, the favorable effects of nitrogen application rate and farmland water level for winter wheat growth and production reduced. Additionally, both the nitrogen load and partial factor productivity of nitrogen (PFPN) increased with the decline of farmland water level, while the nitrogen load increased and the PFPN decreased with the increasing nitrogen application rate. The raise of nitrogen rate from 150 kg∙ha−1 to 225 kg∙ha−1 was beneficial to plant growth, however, the increase of nitrogen application resulted in the decrease of PFPN and increase of drainage nitrogen loads. Compared with the water farmland water level of −40 cm and nitrogen application rate of 150 kg∙ha−1, the increase of nitrogen application rate and the decrease of farmland water level in the range of 50%-100% resulted in yield raise by 5.78%-32.29% approximately and the increase of nitrogen load by 36.20%-178.44% approximately. The comprehensive evaluation with TOPSIS-Entropy method for plant growth, grain yield, WPC, PFPN and nitrogen loads suggested that, the appropriate nitrogen application rate for winter wheat in the waterlogging areas of Middle-Lower Yangtze Plain in China was 225 kg∙ha−1, and the proper farmland water level was lowering to −80 cm in wet year and −60 cm in dry year within 3 days after waterlogging.

Electrochemical analysis of harmful dichlorvos insecticides based on a high‐performance composite sensor

AbstractBACKGROUNDOrganophosphorus pesticides (OPs) were used in large quantities, and pesticide residues have caused different degrees of environmental pollution. Because the current laboratory detection techniques cannot meet the requirements of quickly and accurately monitoring pesticide concentration, there is an urgent need for a portable and accurate method to trace pesticide residues. This paper reports an effective electrochemical sensor composed of ZrO2@PDA electrode material and glassy carbon electrode (GCE).RESULTSThis sensor displayed optimal selectivity for dichloride among various OPs with a low limit of detection (LOD = 1.318 × 10−3 mg L−1). Wherein the accuracy and reliability of this electrochemical sensor were verified through the standard method of gas chromatography–mass spectrometry (GC–MS) (p = 0.977 > 0.05), as well as the stability and anti‐interference capabilities. In addition, the results of recovery experiments have shown that the recovery rate of the electrochemical sensor for dichlorvos ranged from 89.2% to 103.8%, with RSD less than 5%, indicating its practical application in the actual sample detection.CONCLUSIONSFinally, the portable sensing platform for on‐site analysis of dichlorvos concentration in farmland water samples was also demonstrated with excellent results. All the results suggested that the prepared ZrO2@PDA composites could serve as a prospective microchip sensor for the on‐site detection of dichlorvos. © 2024 Society of Chemical Industry (SCI).

Long term response and adaptation of farmland water, carbon and nitrogen balances to climate change in arid to semi-arid regions

Climate change poses a challenge for resource utilization and environmental pollution issues caused by agricultural production, especially in arid to semi-arid regions. Farmland water, carbon and nitrogen balances are closely related to these resource and environmental issues. Thus, the Agro-Hydrological & chemical and Crop systems simulator was used to assess the response of water, carbon and nitrogen balances to climate change in a spring wheat farmland of arid to semi-arid Northwest China and to propose adaptation strategies. Five Global Climate Models from the Coupled Model Intercomparison Project 6 and two Shared Socioeconomic Pathways (SSP1–2.6 and SSP5–8.5) were used to establish scenarios with the Agro-Hydrological & chemical and Crop systems simulator to simulate farmland water, carbon and nitrogen balances for the 2025–2100 period. Various irrigation amounts and nitrogen fertilization rates were tested as compensation strategies. Results indicated that climate change could negatively affect farmland water, carbon and nitrogen balances, especially under the SSP5–8.5 scenario. Precipitation showed an increasing trend, thus percolation increased and soil water consumption decreased from 2025 to 2100. However, for the carbon budget, although the soil carbon dioxide emissions tend to decrease, the net primary production was also significantly reduced, which resulted in declining the net ecosystem carbon budget under future climatic conditions. In addition, higher temperature and increased precipitation enhanced soil inorganic nitrogen leaching and nitrous oxide emissions but reduced ammonia volatilization from 2025 to 2100. Overall, the soil total nitrogen loss was increased over time, whereas crop nitrogen uptake was significantly reduced. In relation to the SSP1–2.6 scenario, the SSP5–8.5 scenario accelerated the increase rates of soil water percolation and total nitrogen loss over time, as well as the decrease rates of crop nitrogen uptake and net primary production over time. The negative effects caused by climate change can be mitigated by reducing irrigation and increasing nitrogen fertilization. For the SSP1–2.6 scenario, 30% irrigation reduction and 30% nitrogen fertilization increase can effectively decrease soil water percolation and the related nitrogen losses while crop nitrogen uptake, net primary production and net ecosystem carbon budget increase in relation to the current management (irrigation = 240 mm and nitrogen fertilization = 200 kg ha–1). For SSP5–8.5 the strategy with 45% irrigation reduction and 45% nitrogen fertilization increase can also decrease nitrogen losses and increase crop nitrogen uptake, net primary production and net ecosystem carbon budget.

The important role of intelligent water conservancy in the construction of high-standard farmland

Farmlandis the basis of food production,and also the key and difficult point of agricultural development in China.At present, China’s farmland is generally characterized by small scale, scattered distribution, weak infrastructure, and low level of informatization. High-standard farmland construction is an important policy formulated at the national level to ensure food security and promote agricultural transformation and upgrading. It is based on farmland water conservancy and combines information technology and communication technology with high-standard farmland construction, realizing the whole process of information perception, transmission, and management from field to market. However, China’s farmland water conservancy has problems such as backward irrigation conditions and technology, imperfect supervision and management mechanism, and lack of information professionals. The contradiction between agricultural modernization and high-quality development and insufficient demand for farmland water conservancy has gradually become prominent. Therefore, it is necessary to strengthen the construction of smart water conservancy, and strengthen the use of Internet of Things, cloud computing, big data, artificial intelligence, and other technologies to achieve information sharing and data sharing of high-standard farmland construction, to realize the efficient, accurate, and scientific management of high-standard farmland, and to ensure China’s food security.

Impacts of climate change on sustainable agricultural development and adaptation countermeasures

Sustainable agricultural development is crucial for ensuring food security. However, climate change is increasingly affecting sustainable agricultural development. The purpose of this paper is to explore the impact of climate change on sustainable agricultural development and sustainable agricultural technologies for adaptation to climate change. It first analyses the trends and characteristics of global climate change and the characteristics of climate change in China, highlighting the negative impacts of the increased frequency of extreme weather events on agricultural production. Subsequently, the negative impacts of climate change on crop production, water resource use and soil are examined in depth, and the challenges of climate change to sustainable agricultural development are comprehensively assessed. Finally, some feasible sustainable agricultural technologies to cope with climate change are proposed. For example, by adjusting the agricultural layout, locally appropriate crops can be grown at the appropriate time and climate. In addition, new seedlings with high adaptive capacity can be cultivated, and emphasis can be placed on growing high-quality crop varieties that are resistant to drought, high temperatures, pests and diseases, and adversity. It is also possible to strengthen farmland water conservancy infrastructure to improve the stability of crop production and the adaptive capacity of crops, and to better cope with global climate change.

An Analytical Study on the Measurement of Agricultural Net Irrigation Water Requirements

The net irrigation water requirement (NIWR) in the irrigated area is the “terminal” of water-use efficiency and the basis for implementing quota management and water-saving incentives. Also, it is significant for farmland water management and water conservation in irrigated areas. In light of the average water consumption per mu and the crop area, which are two key indicators of the NIWR, this paper adopts the field selection measurement method and the statistical method based on the image patches achieved by China’s third national land survey to acquire the data of water consumptions and crop areas. In this case, four principles of field selection are proposed, including “basic requirements”, “moderate size”, “controllable water inflow”, and “measurement practicality”, in an attempt to increase the number of fields selected for analysis. In addition, based on the image patches, the “complete investigation” and the “typical investigation” are suggested for irrigated areas at different scales to determine the crop areas, which are proven practicable and available for evaluating the water-use efficiency of irrigated areas at different scales.

Surface temperature adjustment in METRIC model for monitoring crop water consumption in North China Plain

Evapotranspiration is the real water consumption element in agricultural water management. In order to improve the efficiency of agricultural water use and maintain the sustainable development of agriculture, more and more attention has been paid to the improvement of monitoring and management of farmland evapotranspiration. In this study, MODIS data and the Temperature Vegetation Dryness Index (TVDI) were used in the METRIC model to estimate evapotranspiration for winter wheat and summer corn in the North China Plain. In order to decrease the error caused by the spatial heterogeneity of air temperature in the estimation of evapotranspiration, based on the characteristics of cold pixels with surface temperatures close to their air temperature, the TVDI was used to assist the selection of cold pixels and obtain the spatial variation trend of air temperature with latitude from all cold pixels in the study area. The influence of the spatial heterogeneity of air temperature on the surface temperature at the time of the satellite overpass was corrected accordingly, thereby the estimation accuracy of evapotranspiration was improved. A METRIC-TVDI model was established and the evapotranspiration of winter wheat and summer corn in Hebei Plain was estimated from 2017 to 2019. The average evapotranspiration of the winter wheat over these three years were 361 mm, 327 mm and 354 mm respectively, and that of summer corn were 394 mm, 388 mm and 405 mm respectively. Based on the farmland water balance, the irrigation water consumption (evapotranspiration minus precipitation) of winter wheat and summer corn rotation were 324 mm, 134 mm and 360 mm respectively. This kind of crop rotation pattern consumed more groundwater for irrigation, especially the winter wheat. In order to realize the balance of groundwater between exploitation and replenishment in Hebei Plain, more water-saving irrigation technologies should be adopted to reduce the evapotranspiration and improving water use efficiency.

Assessing resource utilization, food production and ecosystem service value from spring wheat farmland under reduced irrigation and N fertilization management using a process simulation-based framework

Excessive irrigation and nitrogen (N) fertilization have caused severe reductions in farmland water and N productivity, and functional degradation of agroecosystems in arid to semi-arid regions. Thus, a process simulation-based framework for resource, food, and ecology (RFE) trade-offs was used to optimize the irrigation and N fertilization strategy (IRNF). A scenario study was conducted based on the framework that was developed in our previous companion study in the arid to semi-arid regions of Northwest China. Based on the local IRNF (i.e., 450 mm irrigation and 340 kg ha−1 N fertilization), reduced irrigation and N fertilization management scenarios were proposed considering eleven irrigation depth reduction cases and ten N fertilization reduction cases in three hydrological years (wet, normal, and dry). The integrated responses of spring wheat yield, water and N productivity, and ecosystem service value (ESV) to various IRNFs were quantified and assessed. The results showed that the local IRNF obtained higher yields and positive ESVs, but was also produced higher greenhouse gas (GHG) emissions, N losses, and water consumption, thus resulting in lower water and N productivity and higher negative ESVs. Among the negative ESVs, GHG emission value accounted for the largest proportion of the total negative ESV (75–80 %), followed by water consumption and fertilizer loss values. Compared with the local IRNF, the scenarios with 210 mm irrigation and 240 kg ha–1 N fertilization in wet years, 240 mm irrigation and 200 kg ha–1 N fertilization in normal years, and 300 mm irrigation and 200 kg ha–1 N fertilization in dry years could not only reduce negative ESVs, improve water and N productivity, and save water and fertilizer resources, but also maintain acceptable yields and positive ESVs. These recommendations reduced the total negative ESV by 4.8–7.7 %, increased water and N productivity by 4.0–8.9 % and 44–69 %, and saved irrigation water and N fertilizer resources by 33.3–53.3 % and 29.4–41.2 % in three hydrological years in relation to the local IRNF, respectively. This study indicates that the framework is a reliable tool for achieving farmland RFE trade-offs and generating appropriate IRNFs for different hydrological years in arid to semi-arid regions. Therefore, sustainable development goals in RFE systems may be achieved by optimizing irrigation and fertilization management.

Zinc oxide nanorod/rutin modified electrode for the detection of Thiourea in real samples

In this work, a novel electrochemical detection strategy was developed based on a metal-organic framework of zinc oxide nanorod nanoparticles and rutin for selective screening of Thiourea as toxic chemicals. The zinc oxide nanorod were synthesized by following direct chemical precipitation methods and characterized by X-ray diffraction and X-ray photoelectron spectroscopy analysis. The surface of modified electrodes was also characterized by field emission scanning electron microscopes, energy-dispersive X-ray spectroscopy, and attenuated total reflectance flourier transform infrared spectroscopy. Furthermore, the electrochemical activity of the developed sensor was tested by cyclic voltammetry, differential pulse voltammetry, and electrochemical impedance spectroscopy. The modified electrode showed outstanding electro-catalytic activity towards the detection of Thiourea in phosphate buffer saline at a high pH level of 12.0. The proposed sensor showed a linear range of linearity in a concentration ranging from 5.0 × 10−6 - 900 × 10−6 molL−1 and a detection limit of 2.0 × 10−6 molL−1. Moreover, the selectivity of the developed electrochemical sensor was investigated for the detection of Thiourea in the presence of organic compounds and a group of anions. Furthermore, the proposed strategy demonstrated an excellent recovery value in the spiked farmland water and fruit juice sample.

A Review of Plastic Film Mulching on Water, Heat, Nitrogen Balance, and Crop Growth in Farmland in China

Plastic film mulching has been widely used to improve crop yield and water use efficiency, although the effects of plastic film mulching on water, heat, nitrogen dynamics, and crop growth are rarely presented comprehensively. This study investigated a large number of studies in film mulching fields from the past 10 years (mostly from 2019 to 2023) and summarized the impact of plastic film mulching, progress in modeling with film mulching, and future research directions. The effects of plastic film mulching were intricate and were influenced by film mulching methods, irrigation systems, crop types, crop growth stages, etc. Overall, plastic film mulching showed a positive effect on improving soil water, temperature, and nitrogen status, enhancing crop transpiration and photosynthetic rates, and promoting crop growth and yield, although film mulching may have negative effects, such as increasing rainfall interception, blocking water entering the soil, and reducing net radiation income. The crop yield and water use efficiency could increase by 39.9–84.7% and 45.3–106.4% under various film mulching methods. Coupled models of soil water and heat transport and crop growth under plastic film mulching conditions have been established by considering the effects of plastic film mulching on the upper boundary conditions of soil water and heat, energy budget and distribution processes, and the exchange of latent and sensible heat between soil and atmosphere. The models have good applicability in film mulched farmland of maize, rice, and potato for different regions of China. Further development is needed for soil water, heat, nitrogen migration, and crop growth models under different plastic film mulching methods, and the acquisition of soil and crop indicators under plastic film mulching conditions based on big data support. The study will provide reference for the subsequent development and innovation of plastic film mulching technology.

Scaling solar-induced chlorophyll fluorescence by using VPD0.5 improves the simulation of reference crop evapotranspiration in the arid and semiarid regions of northern China

Accurately estimating reference crop evapotranspiration (ET0) is crucial for decision-making regarding irrigation, improving farmland water use efficiency, planning water resources, and simulating the global water cycle. However, because traditional ET0 estimation models often require a large number of meteorological parameters as input variables, their application potential is limited, especially when simulating arid and semiarid regions where meteorological data are lacking. Therefore, it is still very challenging to estimate ET0 at large scales in arid and semiarid areas. In this study, we first proposed a semi-mechanistic model for estimating ET0 by deriving the relationship between canopy conductance to water vapor (gw) and solar-induced chlorophyll fluorescence (SIF) based on Fick's law and the light use efficiency (LUE) model, combining theories on optimal stomatal behaviour. Second, the relationship between SIF × VPD0.5 (VPD0.5: 0.5-power of the vapor pressure deficit) and ET0 in the model was verified at the site scale. On this basis, the semi-mechanistic model was applied to three machine learning models to simulate ET0. Four statistical indicators, including the coefficient of determination (R2), root mean square error (RMSE), normalized root mean square error (NRMSE) and mean absolute error (MAE), were used to verify the model performance and screen out the optimal model. Finally, the optimal model was compared with five traditional empirical models. The results showed that (1) the relationship between ET0 and the product of SIF and the 0.5-power vapor pressure deficit (SIF × VPD0.5) was closer than the relationship between ET0 and SIF at the site scale. (2) Compared to the case in which SIF was used as the only input variable, three machine learning models performed better when SIF was scaled using VPD0.5 as the input variable, and the random forest regression (RFR) model in which SIF × VPD0.5 was the input variable was the optimal model (ET0opt). (3) The total mean value of ET0 at 22 sites simulated by ET0opt is 15.46 mm/4 days. The total mean ET0 value for the 22 sites calculated by the five traditional empirical models vary from 10.70 mm/4 days to 20.96 mm/4 days, while our results were in this range and close to their mean value. Our study provides a valuable reference basis for large-scale ET0 estimations using remote sensing in arid and semiarid areas.

Mechanisms of the effects of the biochar application rate and time on farmland water, heat and gas environments during soil thawing in seasonally frozen soil areas: A two-year field trial

Biochar has shown great potential in improving the soil environment and alleviating greenhouse gas (GHG) emissions. However, the biochar effect in seasonally frozen soil areas remains unclear due to the complexity of the freezethaw cycle processes. To effectively regulate the soil waterheatgas environment, three treatments, a single high-dose application (BL), two low-dose applications (BS), and no biochar application (CK), were established in a two-year field experiment. The characteristics of soil GHG emissions under the different treatments and their response relationships to soil water, heat and air permeability were explored. The results indicated that the average water content under BL and BS treatments increased by 10.78 % and 7.05 %, respectively, relative to the CK treatment in 2021, while the BS treatment exhibited the best performance in 2022. The temperature regulation effect under the different treatments showed similar trends, with the average temperature under the BS treatment in 2022 was 0.62 °C and 1.38 °C higher, respectively, than that under the BL and CK treatments. The two-year cumulative CO2 emission flux under the BL and BS treatments was 9.36 % and 2.98 % higher, respectively, than that under the CK treatment. Biochar increased soil macropores (>75 µm) and promoted soil gas circulation. Resulting in increased pmoA and nosZ gene abundance and inhibited CH4 and N2O fluxes in soil. The two-year global warming potentials (GWPs) under the BL and BS treatments were 65.7 and 113.17 kg CO2 hm-2 lower, respectively, than that under the CK treatment. Overall, multiple low-dose biochar applications could comprise an efficient and sustainable application mode.