Crop Irrigation Research Articles

Accurate Characterization of Soil Moisture in Wheat Fields with an Improved Drought Index from Unmanned Aerial Vehicle Observations

Soil moisture content is a crucial indicator for understanding the water requirements of crops. The effective monitoring of soil moisture content can provide support for irrigation decision-making and agricultural water management. Traditional ground-based measurement methods are time-consuming and labor-intensive, and point-scale monitoring cannot effectively represent the heterogeneity of soil moisture in the field. Unmanned aerial vehicle (UAV) remote sensing technology offers an efficient and convenient way to monitor soil moisture content in large fields, but airborne multispectral data are prone to spectral saturation effects, which can further affect the accuracy of monitoring soil moisture content. Therefore, we aim to construct effective drought indices for the accurate characterization of soil moisture content in winter wheat fields by utilizing unmanned aerial vehicles (UAVs) equipped with LiDAR, thermal infrared, and multispectral sensors. Initially, we estimated wheat plant height using airborne LiDAR sensors and improved traditional spectral indices in a structured manner based on crop height. Subsequently, we constructed the normalized land surface temperature–structured normalized difference vegetation index (NLST-SNDVI) space by combining the SNDVI with land surface temperature and calculated the improved Temperature–Vegetation Drought Index (iTVDI). The results are summarized as follows: (1) the structured spectral indices exhibit better resistance to spectral saturation, making the NLST-SNDVI space closer to expectations than the NLST-NDVI space, with higher fitting accuracy for wet and dry edges; (2) the iTVDI calculated based on the NLST-SNDVI space can effectively characterize soil moisture content, showing a significant correlation with measured surface soil moisture content; (3) the global Moran’s I calculated based on iTVDI deviations ranges between 0.18 and 0.30, all reaching significant levels, indicating that iTVDI has good spatial applicability. In conclusion, this study proved the effectiveness of the drought index based on a structured vegetation index, and the results can provide support for crop moisture monitoring and irrigation decision-making in the field.

Bio-economic analysis of irrigation schedules considering shallow groundwater: lessons from South Africa

Due to the pressure on South Africa’s irrigated agriculture to improve efficiency and optimal water use, irrigators must consider alternative water sources, such as root-accessible shallow groundwater tables, to supply the crop evapotranspiration requirement. Devising irrigation scheduling strategies that will optimize conjunctive water use is difficult because the contribution of shallow groundwater tables is not directly observed and is a function of irrigation management decisions; as a result, very few irrigators use these strategies. This paper aims to evaluate the profitability of using shallow groundwater tables as a source of irrigation water to satisfy crop evapotranspiration requirements. A bio-economic simulation model consisting of the soil–water–atmosphere–plant model and an economic accounting module was developed to calculate the profitability of conjunctive irrigation practices under different states of nature. The bio-economic simulation model was linked to a differential evolutionary algorithm to optimize the irrigation scheduling decisions. The results showed that irrigators could substantially increase profitability and water use efficiency if they consider the shallow groundwater table in their irrigation decision. About 51% of crop evapotranspiration could originate from shallow groundwater tables, reducing the irrigation requirements substantially without impacting crop yields. Sequential adaptive irrigation decision-making does not improve the bio-economic indicators much since using the shallow groundwater table mitigates the risk of undersupplying water. Therefore, conjunctive water use strategies using shallow groundwater tables economically benefit irrigators. However, a complex interplay exists between irrigation adjustments, crop yields and economic performance in different states, emphasizing the careful consideration of context-specific factors in irrigation management decisions.

Water Requirement and Irrigation Scheduling of Selected Vegetable Crops Grown in Welmera District, Central Highland of Ethiopia

Accurate quantification of irrigation water requirement at different physiological growth stages of crops is important to prevent over or under irrigation. A study was carried out to determine the crop water requirement and irrigation scheduling of the selected vegetable crops grown under irrigated conditions at Holeta, Central Highland of Ethiopia. The crops include, cabbage, and tomato. By using the 30-year climatic data, the crop evapotranspiration (ETc), reference crop evapotranspiration (ETo), and irrigation water requirement for each crop were determined using the CROPWAT model. The study shows that the total CWR of cabbage, and tomato was estimated to be 426.6mm, and 497.7mm respectively, and the total GIR of cabbage, and tomato was found to be 598.3 mm, and 691.7 mm respectively. for early January planted cabbage, irrigation should be given twelve times ( 1-Jan, 9-Jan, 16-Jan,24-Jan, 2-Feb, 11-Feb, 21-Feb, 5-Mar,15-Mar, 25-Mar, 2- Apr and last irrigation on 12-Apr) with a gross irrigation water amount of 59.8mm, 32.5mm, 31.5mm, 35.2mm,42.2mm,43.3mm,48.1mm, 52mm, 51.3mm, 49.5mm, 51mm, and 51.7mm depth respectively. Similarly for early January planted tomato, irrigation should be given twelve times (1-Jan, 5-Jan, 10-Jan, 16-Jan, 21-Jan, 29-Jan, 6-Feb, 15-Feb, 26-Feb, 12-Mar, 26-Mar, and the last irrigation on 10-Apr) with a gross irrigation water amount of 53.9mm, 19.3mm, 25.5mm, 30.7mm, 30.6mm, 36.4mm, 47.8mm, 58.5mm, 73.4mm, 83.5mm, 80.9mm, 80.7mm irrigation depth respectively. This study might be useful to prevent over or under irrigation which will help in effectively planning and management of irrigation water in the district.

A Machine Learning Approach to Monitor the Physiological and Water Status of an Irrigated Peach Orchard under Semi-Arid Conditions by Using Multispectral Satellite Data

Climate change is making water management increasingly difficult due to rising temperatures and unpredictable rainfall patterns, impacting crop water availability and irrigation needs. This study investigated the ability of machine learning and satellite remote sensing to monitor water status and physiology. The research focused on predicting different eco-physiological parameters in an irrigated peach orchard under Mediterranean conditions, utilizing multispectral reflectance data and machine learning algorithms (extreme gradient boosting, random forest, support vector regressor); ground data were acquired from 2021 to 2023 in the south of Italy. The random forest model outperformed in predicting net assimilation (R2 = 0.61), while the support vector machine performed best in predicting electron transport rate (R2 = 0.57), Fv/Fm ratio (R2 = 0.66) and stomatal conductance (R2 = 0.56). Random forest also proved to be the most effective in predicting stem water potential (R2 = 0.62). These findings highlighted the potential of integrating machine learning techniques with high-resolution satellite imagery to assist farmers in monitoring crop health and optimizing irrigation practices, thereby addressing the challenges determined by climate change.

Evaluation of three irrigation application systems for watermelon production in the Texas High Plains

AbstractAbout 90% of the Texas High Plains area depends on the water supply from the Ogallala aquifer. The decline of the Ogallala water table raises a concern for the sustainability of producing the traditional irrigated field crops due to their high water demand. Thus, diversifying cropping systems may be a solution. However, about 70% of the irrigated cropland uses center pivots that may negatively impact specialty crop quality. Thus, a 3‐year experiment was conducted to assess the potential of using mobile drip irrigation (MDI) to produce fresh watermelon compared to center pivot sprinkler irrigation using low‐elevation spray application (LESA), and surface drip irrigation with plastic mulch (DI). The study evaluated the effects of irrigation systems on crop yield, fruit quality, physiological responses, water use efficiency (WUE), and irrigation use efficiency (IUE). On average, plants under MDI had higher yields (106 Mg ha−1) than those under DI (70 Mg ha−1) and LESA (68 Mg ha−1). Plants under MDI and DI also had more fruits per plant (1.9) than those under LESA (1.4). The higher yield was associated with higher biomass and photosynthetic function. In general, fruit quality was not affected by irrigation system. In addition, plants under MDI system showed a higher WUE and IUE, and less water stress than those under LESA and DI systems. Results of this study suggest that irrigating under an MDI system is a good alternative for growers in the Texas High Plains that aim to diversify their cropping system with high‐value vegetable crops such as watermelon (Citrullus lanatus).

Assessment of Crop water requirement using FAO CROPWAT 8.0 for Groundnut in South Odisha

Water has a major impact on crop growth, output, and quality and increased production with less water consumption is crucial due to increased demand from domestic and industrial sectors. With evaporative demand-based irrigation scheduling the water consumption gets reduced and improves water productivity. The type of soil, growing season, weather, and crop type affect specific crops' water needs. To compute crop water requirement, irrigation requirement, and its schedule for groundnut cultivated during the rabi season in south Odisha, India, the FAO CROPWAT 8 program was used, with the meteorological data of Centurion University, Paralakhemundi, and NASA power. The water requirement for groundnut was 362.1 mm, the irrigation requirement for groundnut crops was 323.7 mm, and crop evapotranspiration (ETc) was 373.4 mm during rabi season. At 50% critical depletion of soil moisture and soil replenishment to field capacity, the yield did not decrease. These results indicate that irrigation schedules can be practiced for different regions' crops without experimental results by estimating crop water requirements and irrigation schedules obtained through the FAO CROPWAT 8 model.

A multidisciplinary analysis of over 53,000 fascioliasis patients along the 1995–2019 countrywide spread in Vietnam defines a new epidemiological baseline for One Health approaches

Fascioliasis, only foodborne trematodiasis of worldwide distribution, is caused by Fasciola hepatica and F. gigantica, liver flukes transmitted by freshwater snails. Southern and southeastern Asia is an emerging hot spot of F. gigantica, despite its hitherto less involvement in human infection. In Vietnam, increasing cases have been reported since 1995, whereas only sixteen throughout 1800–1994. A database was created to include epidemiological data of fascioliasis patients from the 63 Vietnam provinces throughout 1995–2019. Case profiles were based on serology, symptoms, eosinophilia, imaging techniques, stool egg finding, and post-specific-treatment recovery. Radio broadcasting about symptoms and costless diagnosis/treatment led patients to hospitals after symptom onset. Yearly case numbers were modelled and spatio-temporally analyzed. Missing data and confounders were assessed. The countrywide spread has no precedent. It started in the central coast, including 53,109 patients, mostly adults and females. Seasonality, linked to vegetable consumption, peaks in June, although the intensity of this peak differs according to relief/climatic zones. Incidence data and logistic regression curves are obtained for the first time in human fascioliasis. Fasciolid hybrids accompanying the spreading F. gigantica flukes, and climate change assessed by risk index correlations, are both ruled out as outbreak causes. Human-guided movements of livestock from an original area prove to be the way used by fasciolids and lymnaeid vectors to expand geographically. Radix viridis, a highly efficient transmitting and colonizing vector, played a decisive role in the spread. The use of irrigated crop fields, widely inhabited by R. viridis, for livestock grazing facilitated the transmission and spread of the disease. General physician awareness and diagnostic capacity improvement proved the successful impact of such knowledge transfer in facilitating and increasing patient infection detection. Information, education and communication to the public by radio broadcasting demonstrated to be very helpful. Fasciola gigantica is able to cause epidemic and endemic situations similar to F. hepatica. The magnitude of the human outbreak in Vietnam is a health wake-up call for southern and southeastern countries of Asia which present the highest human population densities with increasing food demands, uncontrolled livestock inter-country exchange, foreign import practices, and monsoon's increasing climate change impact.

Internet-of-Things for smart irrigation control and crop recommendation using interactive guide-deep model in Agriculture 4.0 applications

ABSTRACT The rapid advancements in Agriculture 4.0 have led to the development of the continuous monitoring of the soil parameters and recommend crops based on soil fertility to improve crop yield. Accordingly, the soil parameters, such as pH, nitrogen, phosphorous, potassium, and soil moisture are exploited for irrigation control, followed by the crop recommendation of the agricultural field. The smart irrigation control is performed utilizing the Interactive guide optimizer-Deep Convolutional Neural Network (Interactive guide optimizer-DCNN), which supports the decision-making regarding the soil nutrients. Specifically, the Interactive guide optimizer-DCNN classifier is designed to replace the standard ADAM algorithm through the modeled interactive guide optimizer, which exhibits alertness and guiding characters from the nature-inspired dog and cat population. In addition, the data is down-sampled to reduce redundancy and preserve important information to improve computing performance. The designed model attains an accuracy of 93.11 % in predicting the minerals, pH value, and soil moisture thereby, exhibiting a higher recommendation accuracy of 97.12% when the model training is fixed at 90%. Further, the developed model attained the F-score, specificity, sensitivity, and accuracy values of 90.30%, 92.12%, 89.56%, and 86.36% with k-fold 10 in predicting the minerals that revealed the efficacy of the model.

A diagnostic approach to modeling watersheds with human interference

Most watersheds have human impacts that modify hydrological responses differently over a range of timescales. However, these impacts are not accounted for in most hydrological models. Human impacts in watersheds are diverse and case specific, unlike natural hydrologic processes. Incorporating all plausible human impacts comes at a high data acquisition and modeling cost. This raises the question, which human impacts do we need to incorporate to represent observed streamflow patterns at different timescales? To answer this question, we develop a diagnostic approach to modeling watersheds with human interference. This mixed methods approach is informed by the case history and builds on the top-down hydrological modeling approach where process complexity is incrementally added with changing timescales to identify and respond to changing dominant hydrological processes in a given watershed. Here we implement this modeling approach in the East Fork watershed in California, USA for which data on changes in water imports, withdrawals, irrigation and agriculture land cover is available from the early 1940′s, making it an ideal demonstration case. In the East Fork watershed, we find that incorporation of water imports and rights are sufficient to replicate annual patterns of runoff variability, and that adding crop water demand and irrigation enables replication of monthly and daily patterns, while incorporation of groundwater pumping results in negligible improvements. To demonstrate the capabilities of the diagnostic approach in and beyond this case we conducted two computational experiments: checking for needed model structural change and exploring a counterfactual scenario of intensified agriculture.

Improvement of earthen dikes with binders: a civil engineering advancement for agricultural irrigation and drought mitigation in Algeria

In recent decades, Algeria has faced significant challenges due to climate change, resulting in cracks in agricultural soils and degradation of the vegetation layer, especially during unusual summer rains. Dikes play a crucial role in preserving this vegetation layer, allowing farmers to restore soil integrity and utilize dikes as earth barriers for crop irrigation and animal watering.Within civil engineering, dikes are often classified as either hydraulic or geotechnical structures, typically constructed using compacted earth, especially in non-draining soils like clays or soft soils. It has been observed that accumulation of mud or water can compromise their structural integrity partially or completely.To address these issues, this study focuses on presenting reinforcement systems for earth-built dikes using commonly used binders in the Algerian market such as Portland cement, lime, gypsum, etc. Specific calculations are conducted to assess the strength, mechanical behavior, and resilience of each dike type based on soil type, reinforcement method, and concentration. This approach aims to enhance the mechanical properties of dikes and optimize soil characteristics, offering solutions to strengthen the irrigation and agricultural sector, potentially yielding significant economic benefits for the country.

AN AUTOMATED CROP MONITORING AND IRRIGATION SYSTEM WITH PREDICTIVE ANALYSIS

Background: The rising global population and decreasing agricultural land challenge the food supply, worsened by land degradation and water scarcity. Using IoT solutions, precision farming offers promise for addressing these issues by enhancing productivity, profitability, and environmental sustainability. The paper proposes an IoT-based automated monitoring and irrigation system with predictive analysis as a solution. Aims: To develop an IoT-based irrigation model with cloud computing, a mobile app for remote monitoring and irrigation control, and a predictive analysis method for forecasting weather conditions. Methods: IoT sensors are connected to a node MCU, and they monitor real-time temperature, humidity, and soil moisture. A water pump adjusts irrigation accordingly. Data is sent to ThingSpeak for visualization and Firebase for storage and is accessible via a mobile app. Predictive analysis combines sensor and historical weather data using the Random Forest algorithm to optimize irrigation. This determines the ideal irrigation frequency, duration, and timing based on predicted temperature and soil moisture levels, ensuring optimal soil moisture for plant growth. Results: The system exhibited encouraging outcomes. It displayed transformative potential by harnessing IoT, cloud computing, and predictive analytics. Offering precise soil moisture monitoring, instant data access, and tailored advice, the system can elevate crop management, streamline water consumption, and boost productivity among Indian farmers. Discussion: The automated crop monitoring and irrigation system has transformative potential in farming. Some future scope and enhancements may include broadening crop compatibility, integrating satellite data, enhancing pest monitoring, improving connectivity, enriching the mobile app, scaling up through partnerships, and refining the predictive models. Conclusions: The IoT-driven crop monitoring system revolutionizes Indian agriculture by optimizing irrigation and integrating weather forecasts. With mobile app enhancements and future improvements, it promises sustainable farming and empowerment for farmers.

How economically and environmentally viable are multiple dams in the upper Cauvery Basin, India? A hydro-economic analysis using a landscape-based hydrological model

Abstract. The construction of dams threatens the health of watershed ecosystems. The purpose of this study is to show how multiple dams in a basin can impact hydrological flow regimes and subsequently aquatic ecosystems that depend on river flows. The approach assesses the ecosystem services (ESs), including the tradeoffs between economic and ecological services due to altered flow regimes. It uses a previously developed model that integrates a landscape-based hydrological model with a reservoir operations model on a basin scale. The approach is novel because not only does it offer the analysis of alterations in ecosystem services on a daily scale when pre-dam data are unavailable but also allows for dams to be synthetically placed anywhere in the river network and the corresponding alterations in flow regimes to be simulated in a flexible manner. As a proof of concept, we analyse the economic and ecological performances of different spatial configuration of existing reservoirs instead of synthetically placed reservoirs in the upper Cauvery River basin in India. Such a study is timely and conducted for the first time, especially in light of calls to assess the cascade of reservoirs in India and regions elsewhere where pre-dam data are unavailable. The hydrological impact of different configurations of reservoirs is quantified using indicators of hydrologic alteration (IHAs). Additionally, the production of two major ecosystem services that depend on the flow regime of the river, as indicated by irrigated agricultural production and the normalized fish diversity index (NFDI), is estimated, and a tradeoff curve, i.e. a production possibility frontier, for the two services is established. Through the lens of the indices chosen for the ecosystem services, the results show that smaller reservoirs on lower-order streams are better for the basin economy and the environment than larger reservoirs. Cultivating irrigated crops of higher value can maximize the value of stored water and, with lower storage, generate a better economic value than cultivating lower-value crops while reducing hydrological alterations. The proposed approach, especially when simulating synthetic spatial configurations of reservoirs, can help water and river basin managers to understand the provision of ecosystem services in hydrologically altered basins, optimize dam operations, or even prioritize dam removals with a goal of achieving a balanced provision of ecosystem services.

Irrigated corn yield and soil phosphorus response to long‐term phosphorus fertilization

AbstractLong‐term experiments can help to understand soil phosphorus (P) dynamics and improve nutrient management strategies. This research evaluated long‐term (2002–2021) soil P dynamics and yield response to a range of P fertilizer rates in a continuous high‐yielding irrigated corn (Zea mays L.) experiment with low initial soil phosphorus test (SPT, 10.5 mg kg−1). The experiment was established near Clay Center, NE, and five P rates (0, 10, 20, 39, and 59 kg P ha−1) were evaluated. Soil samples at 20‐cm depth were collected in eight cropping seasons. Yield response to P fertilizer increased after 20 years from 0.64 to 2.79 Mg ha−1. The application of 39 kg P ha−1 year−1 increased soil Bray‐1 P to 19.5 mg kg−1, outyielded all other P treatments, and resulted in a positive relative P balance. Over 20 years, 0 kg P ha−1 year−1 decreased Bray‐1 P from 10.5 to 5.5 mg kg−1. Annual P rates of 0, 10, and 20 kg P ha−1 produced a negative relative P balance and SPT below the critical soil test value (CSTV). The CSTV was 22.2 mg kg−1 for a continuous irrigated corn cropping system. A 53% increase in the P fertilizer rate (from 39 to 59 kg P ha−1 year−1) produced a threefold increment in the soil test P build‐up rate. High‐yielding irrigated continuous corn production systems (>14 Mg ha−1) are required to apply at least 39 kg P ha−1 year−1 to maintain SPT and a positive relative P balance over years.

Regional irrigation expansion can support climate-resilient crop production in post-invasion Ukraine.

Ukraine supplies a large proportion of grain and oilseeds to the world market and faces disruptions from the Russian invasion in 2022. Here we explore the combined effects of the invasion and climate change on Ukraine's irrigation. In 2021, only 1.6% of Ukraine's cropland was irrigated. Of this portion, 73% experienced substantial declines in irrigated crop production following the invasion. We estimate that by the mid-twenty-first century, three-quarters of croplands will experience water shortages, making business-as-usual rain-fed agricultural practices inadequate in addressing the challenges posed by climate change. We explore how leveraging local surface and groundwater resources could enable sustainable irrigation expansion over 18 million hectares of croplands and form a viable climate adaptation strategy. Finally, we identify regions for implementing enhancements or expansions of irrigation systems that can foster a more resilient agricultural sector-underscoring the growing importance of irrigation in sustaining crop production in Ukraine.

Effect of foliar fungicide application timing on corn yield across different water regimes

ABSTRACT Corn (Zea mays L.) is a major irrigated crop in the Texas High Plains (THP). Inadequate precipitation, declining groundwater resources, and inevitable seasonal drought in the area have led to a greater number of studies on crop hybrids, water regimes, and other yield-enhancing techniques. A four-year field study was conducted to investigate the effect of foliar fungicide timing on corn grain yield, grain moisture, and grain test weight under different irrigation regimes. A fungicide containing QoI + DMI was applied to four to six corn hybrids each year at various growth stages (V5, R1, and V5 + R1). For all years, corn plants were grown under three irrigation regimes to meet the seasonal evapotranspiration (ET) requirements of 50% (I50), 75% (I75), and 100% (I100). Hybrid response to the timing of fungicide application and irrigation regime was not consistent over the years. However, in 2 of the 4 years, timing of fungicide application resulted in yield differences among the hybrids, especially at I75 and L100. For those years, the fungicide applied at R1 resulted in a higher grain yield, grain moisture, and grain test weight followed by the fungicide applied at V5 + R1. Results showed that hybrids reacted differently to the foliar fungicide based on the availability of water under disease-free conditions. This study further revealed that irrigation water could be reduced from I100 to I75 with a limited (5.6% in 2011, 11.3% in 2012, 1.3% in 2013, and 0.3% in 2014) yield penalty in the THP region.

Estimation of irrigated crop artificial irrigation evapotranspiration in China

Agriculture water use accounts for 70% of the total water withdrawal worldwide. The evapotranspiration during crop growth is one of the important hydrological processes in the agricultural water cycle. This study proposed the concept of artificial irrigation evapotranspiration of irrigated crops to describe that the evapotranspiration caused by irrigation water use. Irrigated crops rely on two kinds of water sources: precipitation and irrigation water. With the construction of irrigation schemes, the artificial irrigation evapotranspiration plays an increasingly important role in the dualistic water cycle system of irrigated cropland. To reveal the amount of artificial irrigation evapotranspiration of 17 categories of irrigated crops in China, this study proposed a new quantitative model system which was established based on traditional evapotranspiration models and soil water balance models. Based on the new model system, we calculated the annual artificial irrigation evapotranspiration of irrigated crops for the period 2013 to 2017 in China. The results showed that the proportion of artificial irrigation evapotranspiration to the total evapotranspiration of irrigated crops was 41.3%, whose value was 228.1 km3 a−1. The artificial irrigation evapotranspiration in different agricultural water management regions were 90.0 km3 a−1 in the northeast region, 86.0 km3 a−1 in the southeast region, and relatively low 52.2 km3a−1 in the west region. The results of this study can provide methods for water management and policy–making in agricultural irrigated areas, and it can also provide a preliminary understanding of the influence of human activities on the dualistic water cycle in cropland.

Agent-based model predictive control of soil–crop irrigation with topographical information

The amount of water in the soil has an impact on how well plants absorb nutrients. As a result, both above and below-the-surface water dynamics have an effect on crop growth. The quality of the soil and its mechanical qualities can be significantly altered by both an excess or a lack of water, which could make food production unsustainable. This study deals with the development of a precision farming approach to irrigation that considers the topographical features of the arable land and incorporates its morphological properties. The prediction of the water movement in the topsoil layer is an essential element of this strategy, which uses an agent-based model to describe the soil dynamics and the impact of irrigation and exploits a model predictive control (MPC) to optimize water usage. As a case study, the municipality of Samacá in the department of Boyacá, Colombia, is considered.

Impact of crop rotation and tillage operations on mitigating greenhouse gas emissions and evaluation of sustainability index in rice- wheat-green gram cropping system of north Bihar

In North Bihar (NB), the conventional rice-wheat cropping system has led to soil, water, and environmental degradation, alongside low profitability, threatening sustainability. To address these concerns, a thorough field research was conducted over the course of three years to assess different methods of tillage and crop establishment in a rice, wheat, and greengram cycle. The experiment involved five scenarios with different combinations of crop rotation, tillage techniques, seeding procedures, fertilizer use, and irrigation strategies. Uncertainty analysis showed no significant change in mean and variance estimation among seven scenario replications at 5% significance level. Compared to traditional farming (SN-1), managing DSR-rice (SN-5) increased profitability by 17.56%, improved energy use efficiency (EUE) by 32.16%, and reduced irrigation by 24.76% and global warming potential (GWP) by 23.46%. Similarly, substituting zero tillage wheat (ZTW) SN-5 resulted in comparable profitability gains (18.25%) and significant improvements in irrigation (10 %), EUE (+48.65%), and GWP (−20 %) compared to SN-1. Green gram ZT also showed increased profitability (17.35%), with notable improvements in EUE (+38.31%) and GWP (−12.92%) compared to SN-1. Principal component and correlation analyses revealed relationships between total energy inputs, yields, economic returns, and sustainability indices, highlighting the benefits of crop rotation and tillage practices in optimizing resource use. The study suggests that compared to conventional systems, significant improvements in productivity, profitability, energy-use efficiency, and environmental mitigation can be achieved with Crop Rotation and Tillage Operations techniques.

Examining Crop Yield Losses in Iğdır Plain Irrigation Systems in Türkiye Amidst Water Constraints

Water stands as a crucial component of agricultural production. This study aims to propose water efficiency measures in agriculture as an essential element for climate change adaptation. In this study, yield losses in staple crops in agricultural regions are analyzed by using the most suitable methodologies, particularly in agriculture-reliant developing nations. Furthermore, this study seeks to determine the financial consequences of such losses. The methodology applied for this purpose was implemented in Türkiye’s Iğdır Plain, selected as the study site. As the first step, the yields of the first three most cultivated products in each product group were assessed under normal climatic conditions in terms of their crop water requirements and irrigation water requirements. Subsequently, the irrigation water supply was reduced by 10%, and the resulting yield losses were calculated per hectare. Then, the overall crop losses after applying the 10% water constraint were determined in the total cultivation area. Among the crops cultivated in the region, the analysis reveals that clover from the field crops category exhibits the highest water dependence, while apricot demonstrates the least reliance on water resources. As a result, the recommended crop rotation for the Iğdır Plain under water constraints comprises wheat, apricot, watermelon, maize, melon, apple, tomato, peach, and clover. The following measures are proposed to ensure sustainable use of water resources and reduce exposure to climate change: increasing the water transmission efficiency and water use efficiency in irrigation areas, allocating more space to water-stress-resistant crops in the crop pattern in basins, and substituting crops requiring excessive water with less water-dependent crops.

Effect of Growth Stage-based Water Stress on Yield and Water Use Efficiency of Tomatoes (Solanum lycopersicon L.) in Semi-arid Regions of Tigray, Ethiopia

Water availability is a major concern in regions with limited water resources. Implementation of best irrigation water management methods can maximize crop yields and irrigation water use efficiency. An experiment was conducted in Laelay Koraro district, Tigray, Ethiopia for two consecutive years (2018 and 2019 off seasons) to investigate the effect of growth stage-based deficit irrigation on tomato yield, yield characteristics and water usage efficiency. The experiment used a randomized complete block design (RCBD) with three replications. The treatments were three irrigation levels (100%, 50%, and 25% of crop irrigation requirement) and four FAO-defined tomato growth stages (initial, developmental, mid, and late seasons). Data on agronomic parameters and irrigation water were collected and analyzed statistically. The results revealed that reducing irrigation amount by up to 75% during the development growth stage significantly decreased marketable yield by up to 66.5%. However, the highest water use efficiency (9.2kgm-3) was achieved by reducing irrigation amount by 75% at the end-growth stage of tomato. Treatments with the lowest water use efficiency (3.5kgm-3) were those receiving 75% less irrigation amount than the full requirement during the development growth stage. Irrigation deficit up to 75% of the full requirement during tomato development stages greatly affect marketable yield and water use efficiency. Therefore, the tomato crop is highly susceptible to water stress when receiving more than a 50% reduction in full irrigation requirement during its developmental growth stage.