Crop Water Requirements Research Articles

Effect of Foliar Fertilizer and Irrigation Levels on Carrot Productivity

Effect of Foliar Fertilizer and Irrigation Levels on Carrot Productivity

Green and Blue Water Footprints of Major Crops in the Wami/Ruvu Basin, Tanzania: Implications for Water Scarcity and Sustainability

Accurate estimation of the blue and green water requirements of the various crops cultivated in a water basin is essential for planning specific plant/crop irrigation schedules. In this study, the crop water requirements of the major crops grown in the Wami/Ruvu basin were determined using the FAO-CROPWAT model based on meteorological parameters: monthly maximum and minimum temperature, wind speed, mean relative humidity, sunshine hours, rainfall data, and effective rainfall for 23 hydro-meteorological stations distributed in the study area. The studied crops are rice, maize, beans, tomatoes, and sugarcane. The crop reference evapotranspiration (ETo) and actual evapotranspiration (ETc) for each crop were determined using crop coefficients (Kc) of various growth stages of crops. The results indicated that the total annual reference evapotranspiration (ETo) was 1604 mm, whereas an average ETo per month was 134 mm. The highest total irrigation water requirements were recorded in sugarcane, followed by rice, while the lowest water requirements were observed in beans and tomatoes. The results presented in this study may facilitate plant-specific water irrigation implementation and maximize crop production while conserving the environment.

Study on Real-Time Water Demand Prediction of Winter Wheat–Summer Corn Based on Convolutional Neural Network–Informer Combined Modeling

The accurate prediction of crops’ water requirements is an important reference for real-time irrigation decisions on farmland. In order to achieve precise control of irrigation and improve irrigation water utilization, a real-time crop water requirement prediction model combining convolutional neural networks (CNNs) and the Informer model is presented in this paper, taking the real-time water demand of winter wheat–summer maize from 2017 to 2021 as the research object. The CNN model was used to extract the depth features of the day-by-day meteorological data of the crops, and the extracted feature values were inputted into the Informer model according to the time series for training and prediction to obtain the predicted water demand of winter wheat and summer maize. The results showed that the prediction accuracy of the constructed CNN–Informer combination model was higher compared to CNN, BP, and LSTM models, with an improvement of 1.2%, 25.1%, and 21.9% for winter wheat and 0.4%, 37.4%, and 20.3% for summer maize; based on the good performance of the model in capturing the long-term dependency relationship, the irrigation analysis using the model prediction data showed a significant water-saving effect compared with the traditional irrigation mode, with an average annual water saving of about 1004.3 m3/hm2, or 18.4%, which verified the validity of the model, and it can provide a basis for the prediction of crops’ water demand and sustainable agricultural development.

IoT Based Smart Irrigation System using Artificial Intelligence

This proposed of the project introduces an innovative IoT-based AI-driven solution engineered to revolutionize water management in agricultural contexts, catering to both large-scale piped irrigation networks and micro-irrigation systems. Our proposed system leverages cutting-edge AI algorithms to forecast dynamic crop water requirements, seamlessly integrating real-time soil moisture data to optimize water utilization and amplify crop yields. By amalgamating AI-driven predictive analytics with instantaneous sensor feedback, our system facilitates proactive water resource management, curbing wastage and mitigating environmental repercussions. Moreover, the integration of Deep Learning-based pest detection augments the system's capabilities by safeguarding crops against potential threats, thus fostering sustainable agricultural practices. Additionally, our solution pioneers the incorporation of Deep Learning-based crop production for the detection of wild animals, thereby enabling timely alerts through buzzer sound notifications, thereby ensuring comprehensive crop protection. This amalgamation of IoT, AI, and Deep Learning technologies promises to significantly enhance agricultural sustainability, optimize resource utilization, and mitigate ecological impact, thus paving the way for a more resilient and productive agricultural ecosystem.

A Benchmarking Study of Irrigation Advisory Platforms

In the contemporary agricultural landscape, agriculture faces four pressing demands: competitiveness, ensuring food security for a growing population, environmental sustainability, and providing farmers with acceptable living conditions. To meet this global challenge, digital technologies represent a major avenue for innovation and development towards modernized digital agriculture. In this context, irrigation advisory platforms have proven to be transformational tools for both farmers and policymakers, offering insights into the appropriate crop water requirements. This article presents a benchmarking analysis of around 20 professional irrigation advisory platforms. The methodology involves selecting 20 platforms based on accessibility ensuring geographical characteristic diversity. Our findings highlight key criteria shaping the ecosystem of such platforms, including the services offered and their objectives, the types of covered crops, the target users, the form, as well as the source, the availability of the platform, and the variety of data utilized. Lastly, we discuss the main conclusions drawn from our analysis and provide insights into the challenges and future perspectives of irrigation advisory platforms in enhancing agricultural practices and sustainability.

Does climate change affect wheat productivity and water demand in arid regions? Case study of Egypt

The study investigates the effects of climate change on wheat productivity (Yw) and crop water requirements (CWR) across different regions in Egypt, highlighting the potential threat to the country's wheat self-sufficiency goals. Data spanning from 1987 to 2019 from lower, middle, and upper Egypt, as well as areas outside the Nile valley of Egypt, underwent both parametric and non-parametric statistical analysis to discern trends in climate parameters and their impact on wheat growth stages. Findings reveal a significant increase (P ≤ 0.05) in Yw across the study areas, attributed to effective self-sufficiency strategies, with increases of 0.61, 0.60, 0.59, and 0.56 Mg ha−1 per decade in middle Egypt, outside the Nile valley, lower, and upper Egypt study zones, respectively. Although the observed significant increase in Yw, results revealed a significant negative relationship between Yw, maximum air temperature (Tmax), minimum air temperature (Tmin), and wheat growing degree days (GDD) depending on the study zone and the wheat growth stage. The study notes a more significant rise in Tmin than Tmax, adversely affecting Yw, particularly in upper Egypt, where Tmin rose by 0.43 °C per decade. CWR rose by ∼12 % in lower Egypt, ∼15 % in middle and upper Egypt, and ∼18 % outside the Nile valley between 2009 and 2019 compared to 1987–1997 under the best farming conditions. Wheat in middle Egypt showed resilience to climatic change, whereas in the Nile valley zone, the node development stage was significantly sensitive to Tmax fluctuations. In lower Egypt, Tmin and wheat GDD changes during anthesis and physiological stages significantly negatively impacted Yw. The study suggests that wheat self-sufficiency strategies in Egypt and similar climates should start to incorporate air temperature and GDD considerations. Recommendations include adopting heat, drought, and disease-resistant wheat varieties and focusing on expanding wheat cultivation in other zones rather than upper Egypt to cope with climate change.

Impact of different water regimes on maize grown at two distinctive pedo-climatic locations in Bosnia and Herzegovina

Aim of study: A two-year experiment (2021-2022) was conducted to assess the response of a local maize hybrid BL-43 to different water regimes (full irrigation, deficit irrigation and rainfed) at two distinguished pedo-climatic locations (Aleksandrovac and Butmir) in Bosnia and Herzegovina (BiH).
 Area of study: The field experiment was located in Aleksandrovac (near Banja Luka) and Butmir (near Sarajevo) in BiH.
 Material and methods: A randomized block design was adopted at both experimental locations with three replicates. An Excel-based irrigation tool was used to manage crop water requirements and irrigation scheduling.
 Main results: Crop response to water was affected by site-specific agronomic management, the duration of phenological stages and their interconnection with precipitation events. At both locations, the effect of the water inputs on grain yield was statistically significant confirming the beneficial impact of irrigation. The effect of water stress on yield was particularly pronounced at Aleksandrovac, which was under water and temperature stresses during flowering time. During both seasons and for all water regimes, the total average grain yield was greater at Butmir than at Aleksandrovac for 38% and 27%, respectively.
 Research highlights: This is the first experimental study conducted in BiH on the effect of irrigation on maize grain production under different pedoclimatic conditions. The study emphasizes the need for knowledge regarding the impacts that climate change is having on the productivity of one of the region's most important crops.

IoT Based Smart Irrigation System

In recent years, the integration of Internet of Things (IoT) technology into agriculture has shown great potential for enhancing irrigation practices, thereby improving crop yield and water efficiency. This paper presents the design, implementation, and performance evaluation of an IoT-based smart irrigation system tailored for agricultural applications. The proposed system employs various sensors to monitor soil moisture levels, weather conditions, and crop water requirements in real time. Data collected from these sensors are transmitted wirelessly to a central control unit, which utilizes an intelligent algorithm to optimize irrigation scheduling and water distribution. The system also incorporates remote monitoring and control capabilities, allowing farmers to access and manage irrigation operations from anywhere via a web or mobile interface. Experimental results demonstrate the effectiveness of the IoT-based smart irrigation system in conserving water, reducing energy consumption, and enhancing crop productivity. Moreover, the scalability, reliability, and cost-effectiveness of the proposed solution make it suitable for deployment in both small-scale and large- scale agricultural settings. Key Words: IoT, smart irrigation, agriculture, soil moisture sensor, wireless communication, optimization, remote monitoring

Single and basal crop coefficients for estimation of water requirements of subtropical and tropical orchards and plantations with consideration of fraction of ground cover, height, and training system

AbstractThis paper provides an overview of the research carried out over the last 25 years on the FAO56 single and basal crop coefficients of subtropical and tropical orchards and plantations of cactus pear, dragon fruit, fig, jujube, passion fruit, pomegranate, cape gooseberry, cherimoya, guava, longan, lychee, mango, papaya, acerola, carambola, cashew, cacao, coffee, jaboticaba, jatropha, macadamia, açai palm, coconut, date palm, guayule, oil palm, peach palm, ramie and rubber tree. The main objective of this review is to update standard single crop coefficients (Kc) and basal crop coefficients (Kcb) and complete the Kc and Kcb values tabulated in FAO56. Kc is the ratio between the non-stressed crop evapotranspiration (ETc) and the grass reference evapotranspiration (ETo), and Kcb is the ratio between the crop transpiration (Tc) and the ETo. When selecting and analysing the literature, only studies that used the FAO Penman–Monteith equation, or another equation well related to the former to compute ETo were considered, while ETc or Tc were obtained from accurate field measurements on crops under pristine (non-stress cropping conditions) or eustress (“good stress”) conditions. Articles meeting these conditions were selected to provide data for updating Kc and Kcb under standard conditions. The related description of orchards and plantations refers to crop cultivar and rootstock, irrigation systems and scheduling, planting spacing, fraction of ground cover (fc) by the crops, crop height (h), crop age and training systems, as Kc and Kcb values depend on these characteristics. To define the standard Kc and Kcb values of the selected crops, the values collected in the literature were compared with previously tabulated standard Kc and Kcb values. The updated tabulated values are transferable to other locations and climates and can be used to calculate and model crop water requirements, primarily for irrigation planning and scheduling, and thereby supporting of improved water use and savings, which is the overall aim of the current review.

Water and carbon footprints in irrigated vineyards: an on-farm assessment

AbstractThis research aims to contribute to improving water and carbon efficiency in irrigated grapevine production in the dry Mediterranean climate of southern Europe. In regions with water scarcity, irrigation has become a relevant input in viticulture, essential to increase productivity and achieve profits. The joint estimation of the water footprint (WF) and the carbon footprint (CF) can help to comprehensively assess the environmental implications and sustainability associated with water-intensive grapevine cultivation. In this study, the WF and CF, of the farming stage of grapes production, were calculated for three years, in three vineyards located in southern Portugal. Data used for the calculation included meteorological data, irrigation requirements, energy use, fertilizers, and pesticide inputs. The total WF mean value for the study period was 223 m3 ton−1, lower than values found for similar conditions, but the blue component, related to irrigation, was predominant, with a higher proportion (75%) occurring during the driest year. The mean total CF was 98 kg CO2e ton−1; the major contributors were fuel use, fertilizer greenhouse gas emissions, and energy for irrigation. The factor analysis revealed relationships between footprint components, yielding latent variables participated by irrigation water and energy use, pollution loads and agrichemicals use. The examination of trade-offs and/or advantageous relations between footprints and yields showed that seasonal climate conditions play an important role via their effect on the farming practices and the inputs most influential on these indicators, namely: crop water requirement; irrigation volumes; energy for irrigation; fuel consumption; nitrogen and phosphorus fertilization rates.

Automatic gate controlling system for sustainable agriculture water management in Sri Lanka: A review

This research introduces an automatic gate-controlling system explicitly tailored for sustainable paddy cultivation in Sri Lanka. Paddy fields, critical to the nation's food security, often face water management and resource efficiency challenges. Given the pressing concerns of water scarcity and the need for efficient resource utilization in Sri Lanka's agricultural landscape, the automatic gate-controlling system emerges as a cutting-edge solution. This research explores the transformative potential of this technology in revolutionizing traditional irrigation practices. It provides a detailed examination of the system's components and functionalities, emphasizing the integration of smart sensors to gather real-time data on soil moisture levels, weather conditions, and crop water requirements. This data is then processed through advanced analytics, facilitating informed decision-making for the automated control of irrigation gates. These data inputs are then processed by an intelligent control system that automates the operation of water gates. By precisely regulating water flow, the system minimizes water wastage and mitigates over-irrigation risks, contributing to sustainable paddy cultivation practices. The automated nature of the system reduces the dependency on manual labor, providing farmers with an efficient and reliable tool for water management. By adopting this technology, the study envisions increased yields in paddy cultivation while promoting responsible use of water resources. This research marks a significant stride towards sustainable agriculture in Sri Lanka, particularly in the crucial paddy cultivation sector.

Water conservation in tomato production using deficit irrigation and SALTMED model under greenhouse conditions

The use of modeling for water conservation during irrigation is still in the research stage. There are many models can be used for this purpose, including Saltmed model. The SALTMED model is a holistic model can be used for different irrigation water and soil qualities, variety of crops, and water irrigation systems. This research aimed to interpret the tomato yield using different water quantities and qualities under greenhouse experiments. The trial included deficit irrigation with 3 treatments (80 %, 60 %, 40 % along with 100 % crop water requirements (ETc), with three water qualities (ECw 1.0, 2.3 and 3.6 dSm−1). The outcomes of the SALTMED model indicate the potential of the model to interpret soil-water content, salinity and tomato yield in regions having semi-arid climates under deficit irrigation strategies. Results of the SALTMED model revealed that data was underestimated. Furthermore, the values range of the CRM coefficient of the residual mass were 0.005–0.055, -0.01–0.02, and -0.02–0.04 for 0.9, 2.3 and 3.6 dSm−1, respectively. Most salinity showed the CRM values 0.18 to -0.02, 0.2 to -0.41, and -0.05 to -0.38 respectively, for the three irrigation water qualities. The values of observed and simulated data predicted a good relationship obtained. For soil water content and salinity, R2 values ranged between 0.80–0.99 and 0.74–0.99 respectively. Model efficiently simulates tomato yield outcomes under saline and freshwater. The correlation between the simulated and observed yield were 0.92, 0.94, and 0.79 for water quality of 1.0, 2.3 and 3.6 dSm−1, respectively. Finally, the SALTMED model showed good simulations of soil water content, soil salinity, and final tomato yield.

Assessment of crop water requirement and irrigation scheduling of potato using weather models under sub temperate climatic condition of North-Western Himalaya

To optimize irrigation water and enhance yield, appropriate amount of irrigation water and its scheduling according to real time weather data can help in reducing crop water foot prints. The evapotranspiration (ET) based irrigation scheduling using present weather conditions reflecting changed climatic scenarios in the study regions in the past few decades has proved to be sagacious use of water. To study the ET based irrigation scheduling, field experiment conducted with the treatments comprising four ET based weather models irrigation scheduling methods viz. no irrigation or rainfed, Thornthwaite, Hargreaves method, Temperature and modified Penman Monteith under sowing windows of 20th December and 20th January were conducted at research farm, of Department of Agronomy, CSK HPKV, Palampur, Himachal Pradesh during Rabi season of 2015-16. The estimated crop evapo-transpiration (ETc) with different ET estimation methods under North Western Himalayas showed variation during crop season. The highest estimated ET was observed in December as compared to January sown potato crop with Hargreaves method (376.8 mm) followed by Temperature (354 mm), Thornthwaite (329.5 mm) and the lowest in modified Penman Monteith methods (241.9 mm). The estimated ETc from pan evaporation observed to be the highest to the tune of (382.3 mm) compared to other methods of ETc estimation. The crop coefficients modeled at different pheno-phases were used for estimating water requirement of potato crop. It was found that irrigation scheduling based on modified Penman Monteith of ET estimation saved one irrigation to the tune of 60 mm during crop period without affecting significantly growth, and tuber yield of the potato as compared to other methods, viz., However, the irrigation schedule based on Thornthwaite method has recorded highest total potato tuber yield (171.76 q ha-1) being at par with irrigation schedules worked out from other ET estimation methods. The total water footprints were the lowest in modified Penman Monteith to the tune of 300 liters per kg of potato yield compared to other methods.

Spatial and Temporal Distribution of Soil Moisture in Maize Using Frequency Domain Reflectometry Sensor

Field experiment was conducted during kharif 2018, rabi 2018-19 and kharif 2019 at Department of Soil Water Engineering, Dr. NTR College of Agricultural Engineering, Bapatla, Andhra Pradesh to study the spatial and temporal distribution of soil moisture in maize using FDR sensor.The experiment was laid out in split plot design consisting of three irrigation levels (0.6 ETc (I1), 0.8 ETc (I2) and 1.0 ETc (I3)) and four nitrogen levels consisting of fertigation levels (80% RDN (N1), 100% RDN (N2), 120% RDN (N3) and Manual application (N4) on sandy clay loam soil. Water requirement of the maize crop was calculated on the daily basis by using CROPWAT 8.0. Frequency Domain Reflectometry (FDR) Sensor was used to measure soil moisture data by installing 34 access tubes up to a depth of 100 cm with an interval of 10 cm from the soil surface and measured soil water content during crop period. Soil moisture was evaluated for 2.0 lph emitter discharges at 3h after irrigation, 6h after irrigation, 24h after irrigation at emitter, 15 cm from emitter, 30 cm from emitter and 45 cm from emitter for soil moisture in the soil for arriving the best irrigation strategy. The soil water distribution under all drip irrigation treatments indicated that it was relatively high near the dripper and decreased as the distance from the dripper point increased.

Experimental and numerical investigations on soil–water dynamics in Moistube irrigation under dynamically regulated working pressure heads: scenario simulations under maize cultivation

Moistube irrigation (MTI) accurately delivers moisture to root zone through subsurface nanotube irrigators. How to dynamically adjust the working pressure head (WPH) considering crop evapotranspiration remains unstudied. Here, the Moistube discharge characteristics, wetting front (WF) morphology, and soil water content (SWC) distribution under adjusted WPH (0 → 1, 0 → 2, 1 → 2, 1 → 0, 2 → 0 and 2 → 1 m) were experimentally investigated. HYDRUS 2D model was employed to reproduce the soil water dynamics and validated against the experimental data. Maize growth scenarios under regulated WPH for three representative regions (Nanjing, Hami, and Siping in China) with typical hydrological years were performed. The relatively long-term Moistube discharge, SWC, and deep percolation were analyzed. Results demonstrated that flexible WPH adjustment regulated Moistube discharge, the advance velocity of WF, and SWC within the wetting patterns. With increased (or decreased) WPH, the Moistube discharge rate precipitously raised (or descended) to values positively correlated with WPHs. WPHs of 0, 1, and 2 m corresponded to discharge rates of 0.63–0.76, 2.15–3.34, and 5.53–6.41 cm2 h−1, respectively. WF also noticeably accelerated (or decelerated) as WPH, meanwhile SWC within the wetting pattern increased (or decreased). Upon WPH increase, the elevated SWC intensified the deviation (δ) of wetting body centroid from Moistube. HYDRUS 2D model accurately reproduced the Moistube discharge rate and WF advance (R2 ≥ 0.90, NSE ≥ 0.70, RSR close to 0), corroborating the feasibility of treating Moistube as a heavy clay. Surprisingly, the Moistube discharge rate obeyed exponential decreasing trend over time after WPH increased but abnormally displayed restrained trends followed by rebounding after WPH decreased. SWC and the deviation of wetting body centroid from Moistube exhibited discrepancies in absolute values but consistencies in trends between the observed and simulated data. Scenario analyses of maize cultivation revealed that regulating WPH accurately replenished crop evapotranspiration and maintained appropriate SWC environments. Regulated WPH in MTI was efficacious in reducing deep percolation and suitable for arid regions. These results could contribute to intelligent MTI by automatically maintaining a balance between Moistube discharge and crop water requirements.

Evaluating the Climate Change Trends and Spatiotemporal Variations of Evapotranspiration: A Case Study in El-Beheira Governorate, Egypt

Aims: Evapotranspiration (ET) is crucial for determining crop water requirements, while climate change and global warming are major concerns. Therefore, in this study, we aimed at evaluating the climate change trends and the spatiotemporal variations in ET in a selected area located in El-Beheira Governorate, Egypt, while accounting for potential land use/land cover (LULC) changes. Methodology: We analyzed three Landsat images acquired in 1984, 2001, and 2020 to assess LULC changes in the study area. Climate change was studied from 1984 to 2021 using data from NASA's Prediction of Worldwide Energy Resource (POWER) project. We also used the Terra MODIS MOD16A3GF Version 6.1 ET product from 2000 to 2021 to evaluate the spatiotemporal variations of ET. Results: LULC change detection showed that the unchanged agricultural area from 1984 to 2020 accounted for 29.3% of the study area and was used to evaluate the changes of ET. Evaluating climate change revealed that there was an increased trend in temperature, relative humidity (RH), and precipitation, while no change was observed in wind speed. Similarly, the anomalies changes of temperature, RH, and precipitation had an increasing trend while wind speed was constant. On the other hand, the yearly ET values calculated from 2000 to 2021 had an increasing trend. There was a moderate correlation between RH and precipitation, as well as between ET and precipitation. Conclusion: The increased trends in climatic parameters such as temperature will eventually require changes in crop patterns to adjust to these changes and maintain a profitable yield. Furthermore, the increase in ET will result in an increase in crop water requirements, which is problematic, especially considering the limited water resources in Egypt.

Optimizing crop planning in the winter fallow season using residual soil nutrients and irrigation water allocation in India

Effective management of water resources is essential for crop diversification and food security. This study proposes an Irrigation-Food-Environment-Chance-constrained Programming (IFEC) model for simultaneously optimizing crop planting area, irrigation water, and residual fertilizer considering inflow uncertainty along with farmer preference crop. Eight irrigation water allocation optimal models were constructed, fixing the preference crop cultivation area, while deviations in downstream release, and vegetable crop area cultivation were executed for sensitivity analysis. Model is then applied in a command area fed by a sub-tributary of Brahmaputra, India. On averaging, plant available N and P for the area were 62.14 kg ha−1 and 1.13 kg ha−1 respectively. With variation in available water, changes would occur in vegetable and cereal crops having higher yield and relatively less crop water requirement as compared to maize. Results showed that complying with preference crop area up to 60% would decrease the profit by 49% as compared to 20% at even 10% risk probability for 70% release. At existing conditions, water would be insufficient at 60% preference crop. Further, R2 value between benefit and water availability for vegetable cultivation varies from 0.99 to 0.78 for all scenarios. The tool featured that, setting specific preference crop areas provides equitable situation rather than mono-cropping. From the study findings, we suggest two salient recommendations: (1) promoting policies with appropriate financial subsidies for vegetable cultivation that focus on intensification with less water-requiring crops and (2) optimization results could be achieved by expanding the water utilization in the present condition while increasing efficiency.

Agricultural Water Management for Rice Production: A Comprehensive Analysis

The identified problem revolves around establishing the water requirements for rice seeding, growth, and maturity in hot climatic conditions. This study reviews the net water requirement for rice growth or production under various climatic conditions in the Cross River Basin. It was observed that the water requirement from seeding to harvest varies from location to location, maintaining a FAO range, as postulated by Brouwer et al., 1986, ultimately falling between 450mm to 700mm for hot weather.The methodology used for investigating the Net Irrigation Water Requirements for rice growth to maturity involves the application of New_Clim Location and CropWater software. These tools were utilized to compute Reference Evapotranspiration (ETo) and Crop Water Requirements (CWR) for the net irrigation water requirements of rice, respectively. The data were obtained from meteorological stations within the Cross River Basin. In rainfed conditions, no water stress is practically observed throughout the entire crop cycle, affirming that no irrigation system is needed in such a specific climate. This study uses data generated through New_LocClim as input into CropWat, which, among other factors, determines the net water requirement for rice growth and production. Consequently, the results obtained from the use of the CropWat model in determining rice water requirements for Obudu, Nkari, Ikom, and Ijegu-Yala were 633.7 mm, 397.9 mm, 303.0 mm, and 548.4 mm, respectively. The water requirement of only two locations, Obudu and Ijegu-Yala, fell within the FAO-suggested range of 450 mm to 700 mm (Brouwer et al., 1986) for hot climates, while Nkari and Ikom LGAs did not comply with Brower et al., 1986 postulation due to their location in a relatively less hot climate.

Satellite rainfall bias correction incorporating effects on simulated crop water requirements

ABSTRACT Satellite rainfall estimates (SRE) offer spatial-temporal rainfall representations in regions with limited ground-based gauge rainfall measurements. However, differences exist between SRE and gauge measured rainfall, which needs assessment and reduction. This study presents a method to correct errors in SRE to make their use in agro-hydrological applications and models meaningful. The main scientific objective is the determination of effective window sizes for SRE bias correction. To conclude on effective window sizes, the crop water requirement satisfaction index (WRSI) for gauged rainfall, uncorrected SRE and bias corrected SRE were estimated and propagation effects of SRE errors on respective WRSI estimates were assessed. WRSI indicates how much of the crop water needs are satisfied by rainfall. The Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) SRE was bias corrected using gauged rainfall data from 20 stations in the Lake Victoria basin of Kenya from 2012 to 2018. The results show that the error in WRSI can serve to determine effective window sizes for SRE bias correction rather than using SRE bias error itself. This proposed correction method resulted in improved estimates of WRSI.

Impacts of Crop Type and Climate Changes on Agricultural Water Dynamics in Northeast China from 2000 to 2020

Northeast China (NEC) is one of the most important national agricultural production bases, and its agricultural water dynamics are essential for food security and sustainable agricultural development. However, the dynamics of long-term annual crop-specific agricultural water and its crop type and climate impacts remain largely unknown, compromising water-saving practices and water-efficiency agricultural management in this vital area. Thus, this study used multi-source data of the crop type, climate factors, and the digital elevation model (DEM), and multiple digital agriculture technologies of remote sensing (RS), the geographic information system (GIS), the Soil Conservation Service of the United States Department of Agriculture (USDA-SCS) model, the Food and Agriculture Organization of the United Nations Penman–Monteith (FAO P-M) model, and the water supply–demand index (M) to map the annual spatiotemporal distribution of effective precipitation (Pe), crop water requirement (ETc), irrigation water requirement (IWR), and the supply–demand situation in the NEC from 2000 to 2020. The study further analyzed the impacts of the crop type and climate changes on agricultural water dynamics and revealed the reasons and policy implications for their spatiotemporal heterogeneity. The results indicated that the annual average Pe, ETc, IWR, and M increased by 1.56%/a, 0.74%/a, 0.42%/a, and 0.83%/a in the NEC, respectively. Crop-specifically, the annual average Pe increased by 1.15%/a, 2.04%/a, and 2.09%/a, ETc decreased by 0.46%/a, 0.79%/a, and 0.89%/a, IWR decreased by 1.03%/a, 1.32%/a, and 3.42%/a, and M increased by 1.48%/a, 2.67%/a, and 2.87%/a for maize, rice, and soybean, respectively. Although the ETc and IWR for all crops decreased, regional averages still increased due to the expansion of water-intensive maize and rice. The crop type and climate changes jointly influenced agricultural water dynamics. Crop type transfer contributed 39.28% and 41.25% of the total IWR increase, and the remaining 60.72% and 58.75% were caused by cropland expansion in the NEC from 2000 to 2010 and 2010 to 2020, respectively. ETc and IWR increased with increasing temperature and solar radiation, and increasing precipitation led to decreasing IWR in the NEC. The adjustment of crop planting structure and the implementation of water-saving practices need to comprehensively consider the spatiotemporally heterogeneous impacts of crop and climate changes on agricultural water dynamics. The findings of this study can aid RS-GIS-based agricultural water simulations and applications and support the scientific basis for agricultural water management and sustainable agricultural development.