Agriculture, in general, has a long production cycle and is affected by many endogenous and exogenous uncertainty factors. Changes in rainfall patterns, maximum or minimum temperature, types and amounts of fertilizer input, timing, availability of irrigation water, and soil quality can drastically change the agricultural yield. In developing countries such as India, where more than half of countries population is engaged in agriculture, and the whims of nature may affect the agricultural output, it is essential to check how the entire agricultural system reacts to the changes in climatic parameters and anthropogenic practices. This study analyses agricultural trends in four primary staple crops, trends in climatic parameters, and anthropogenic inputs in Indian districts. Significant trends were detected and quantified using the non-parametric Mann-Kendall (MK) test, modified MK test, and Theil-Sen estimator at a 5% significance level. Spearman’s correlation test is used to determine the contributing factors to the changes in agricultural yield. Rice, Wheat, Pearl Millet, and Maize yields have shown significant increasing trends in a large number of the districts. Despite decreases in the gross cropped area in the majority of the districts, the trends in production are mostly positive. According to Spearman’s Rho correlation test, the increase in fertilizer consumption in most districts and the increase in crop-wise irrigated land in many districts are the significant reasons for the increase in yields. The rainfall did not change much compared to maximum and minimum temperatures at both the annual and seasonal levels. Although there were significant climatic changes in the last three decades, the correlation with agricultural yield is mostly insignificant.

How to cite: Sarkar, N. and Ray, S.: Analysis of Agricultural and Climatic trends in Indian Districts and finding the contributing factors in recent Indian Agricultural Outputs, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-653, https://doi.org/10.5194/egusphere-egu23-653, 2023.

The Mediterranean region is widely recognized as a climate change hotspot, where, mainly due to the increase of CO2 concentration, both historical records and future climate models’ projections reveal an increase of the daily average temperature and a reduction of the mean annual precipitation, with less frequent but more intense rainfall events. These changes could have strong impacts on the durum wheat production, and consequently to the food chain that derives from it. Water availability is expected to be the main limiting factor in the durum wheat growth, which is usually rainfed in Mediterranean region. On the other hand, CO2 increase may act as a counterbalance factor, by increasing the water use efficiency. In this work, within the framework of the H2020 European Union project ARSINOE (“Climate-resilient regions through systemic solutions and innovations”), we investigated the possibility to adapt durum wheat production to climate changes, compensating the rainfall reduction with emergency irrigation derived from a rainwater harvesting system, with the aim to keep constant the durum wheat production or alleviate the yield reduction. The Aquacrop model, a crop growth model developed by FAO’s Land and Water Division, has been calibrated to reproduce the actual durum wheat production in the Campidano region in Sardinia (Italy), implementing the local climate and soil characteristics. The model has been then used to simulate the crop production in correspondence of different bias corrected future climate scenarios, which foreseen an average rainfall reduction and increase of average temperature and CO₂ concentration in the atmosphere. A rainwater harvesting system to collect rainfall from the rooftops or impervious surface within the cultivated area (100m2/ha) has been designed and the volume for potential emergency irrigation has been estimated year by year. Preliminary results show the importance of implementing rainwater harvesting systems to provide emergency irrigation and sustain durum wheat production in a context of climate changes.

Acknowledgments

This project has received funding from the European Union’s Horizon H2020 innovation action programme under grant agreement 101037424.

How to cite: Viola, F., Deidda, R., Urru, S., and Cristiano, E.: Rainwater harvesting as climate change adaptation strategy for durum wheat production in Sardinia, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2603, https://doi.org/10.5194/egusphere-egu23-2603, 2023.

With the impact of climate change and the main rainfall seasons in Taiwan are concentrated in the plum rain season from May to June and the typhoon season from July to September each year.There are significant differences in rainfall and spatial and temporal distribution between the wet season and the dry season,the droughts will occur and even lead to severe water shortages, such as the worst drought in half a century in 2021.From a macroscopic spatial scale, for example, the El Niño phenomenon and solar activity may have a certain impact on the overall climate and water resources of the earth.Therefore, this study analyzes the correlation between rainfall and large-scale influencing factors such as sunspots, El Niño-Southern Oscillation,and uses machine learning models to predict and classify rainfall under different conditions,the prediction accuracy rate through historical data can reach 89.9% , with sunspots as the most significant factor. It is hoped that relevant units can provide reference for water resources management and planning.

How to cite: Weng, Z.-H. and Lin, Y.-C.: Establishing a macroscopic-scale rainfall climate and water resources estimation model by machine learning method, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3008, https://doi.org/10.5194/egusphere-egu23-3008, 2023.

In our changing climate, heatwaves and droughts and their spatio-temporal co-occurrences are likely to intensify. This will inevitably challenge future agricultural production and calls for adaptation strategies to protect future yields. To find suitable climate adaptation strategies for Germany’s major staple crop - winter wheat - it is important to know how heat stress, drought stress or their compound effects drive wheat yield failures. The principal aim of this study is, therefore, to quantify the impacts of heat, drought, and their compound effects on winter wheat yields in Germany, in a spatially and temporally discrete manner.

To address our aim, we develop a statistical crop-climate model for the time period 1991-2019 at the county level. We first create agroclimatic proxies for heat stress, drought stress and their compound effects and use these to construct a separate time series model with the addition of time-dependent interaction terms. Our approach constructs separate regression models for each county, based on common elements that allow for comparing and jointly interpreting individual models.

Preliminary results show that more than 50% of Germany’s wheat yield variability can be explained by climate effects. Compound effects of heat and drought stresses are responsible for approx. 42% of the variability in Germany’s winter wheat yields. Drought stress alone explains approx. 7%, with higher impacts in the east of the country, and heat stress alone explains approx. 3% of the year-to-year yield variability, with higher impacts in the north-west of Germany. The results confirm the importance of compound effects and underline their dominating impacts on winter wheat yields, when compared to individual heat and water stress impacts – a finding which should guide future adaptation strategies. Furthermore, our study shows that heat stress is becoming increasingly important for wheat yield failure in Germany – alone and in conjunction with moisture stress.

In conclusion, we suggest that climate change adaptation strategies for winter wheat in Germany should focus on combined measures against drought and heat extremes. While the increase of multi-stress resilience should be the main goal for entire Germany, north-western areas should prioritize strategies to increase heat resilience and eastern areas should prioritize strategies to increase drought resilience.

How to cite: Becker, R., Schauberger, B., Merz, R., Schulz, S., and Gornott, C.: Effects of heat and drought stress and their co-occurrence on winter wheat yields in Germany under climate change, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3528, https://doi.org/10.5194/egusphere-egu23-3528, 2023.

Effective management of water losses in water distribution networks (WDNs) still remains a demanding task, as the temporal and spatial variability of water resources under changing climatic conditions and the increasing needs for drinking water may lead to freshwater shortages. In this context, pressure management strategies are widely adopted in an effort to reduce the water losses in the supply and distribution parts of water networks and, consequently, deescalate their environmental footprint. Installation of pressure reducing valves (PRVs) at critical locations of WDNs plays a central role in pressure regulation strategies, as PRVs reduce the upstream pressure to a set outlet pressure (i.e., downstream of the PRV), usually referred to as set point. Perdios et al. (2022) developed a novel statistical framework and applied it to an existing pressure management area (PMA) of the city of Patras in western Greece, aiming at early detection of PRV malfunctions that may significantly influence network’s operation and the corresponding lifetime of related infrastructure. The results showed that the suggested methodology allows reliable detection of critical malfunctions at least 2 days prior to flow disruptions. Ιn this study, we calibrate and implement Perdios et al. (2022) statistical framework, using pressure data for a 4-year period from 01/Jan./2017 to 26/Nov./2020 from several important PMAs of the WDN of the city of Patras, aiming towards better understanding of the causes of the malfunctions, by decomposing the observed pressure deviations from the set point to systematic and random error components.

Acknowledgements

The research work has been conducted within the project PerManeNt, which has been co-financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation under the call RESEARCH – CREATE – INNOVATE (project code: T2EDK-04177).

Reference

Perdios A., G. Kokosalakis, N. Th. Fourniotis, I. Karathanasi and A. Langousis (2022) Statistical framework for the detection of pressure regulation malfunctions and issuance of alerts in water distribution networks, Stoch. Env. Res. Risk Asses., https://doi.org/10.1007/s00477-022-02256-5

How to cite: Perdios, A., Kokosalakis, G., Fourniotis, N. Th., Pantzalis, D., and Langousis, A.: A probabilistic approach for detection and classification of PRV malfunctions in the water distribution network of the city of Patras in western Greece, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5567, https://doi.org/10.5194/egusphere-egu23-5567, 2023.

Water leakage is acknowledged as one of the most important issues that drinking water supply systems are facing worldwide. Non-Revenue Water is estimated to 346 million m³ per day and its cost/value is estimated to 39 billion USD per year. At the same time drinking water quality is jeopardized from the water intake points to the consumer’s tap, even during normal operating conditions.

ICT support water utility operators to improve the operational capacity of their water supply system. A smart green system to control water leakage and monitor drinking water quality in the water supply system of Paramythia city will be built in the context of SMASH project. It consists of: (a) IoT system comprising three local control stations, installed in selected parts of the water supply network, monitoring water quantity&quality parameters in real time; (b) the hydraulic simulation model of the water supply system of Paramythia; (c) a virtual sensors system, which will be used for water quality prediction; (d) a Decision Support System (DSS) for leakage detection and optimal management of water supply system parameters in an automated manner.

The DSS will detect and locate water leakages within the DMA zone and inform the operators for excessive values in drinking water quality parameters. The DSS will use as inputs the data from the IoT system, will interact with the hydraulic simulation model, and obtain the water quality data from the virtual sensors. All these data will be processed by the DSS logic in the backend subsystem. The IoT and the hydraulic simulation data, based on the digital twin of the water supply system, are used for the calculation of specific performance indicators related to water leakage, such as well-known IWA indicators: water losses, ILI, etc. Calculating the divergences between the PI values observed & the ones representing the optimal operation of the water network without leakages and setting appropriate thresholds, the DSS will detect the leakage, while several different scenarios will run in hydraulic simulation. The frontend subsystem of the DSS will be able to visualize the water distribution network, statistical values of water quantitative & qualitative parameters. It will provide alarms in case of leakage or exceedance of water quality parameters’ values and it will show the leakage location in a map. The architecture of the smart green system, currently under development, is depicted in Fig.1.

Figure 1. The DSS for the water parameters management in the water supply system

Keywords: Drinking water; water quality; leakage; virtual sensors; smart system; decision support.

Acknowledgement: This work is co-financed by EEA Grants 2014 – 2021 and Greek Public Investments Program.

• Liemberger, R., & Wyatt, A. (2019). Quantifying the global non-revenue water problem. Water Supply, 19(3), 831-837.
• Antzoulatos G., Mourtzios C., et al (2020), Making urban water smart: the SMART-WATER solution. Water Science & Technology, 82(12), 2691–2710.
• Alegre, H., Baptista, et al (2016). Performance indicators for water supply services. 3^rd IWA publishing.

How to cite: Chasiotis, A., Tsitsifli, S., Panytsidis, K., Nilsen, V., Mantas, N., Theodorou, D., Kyriakidis, T., Chasiotis, S., Bousdeki, M., Feloni, E., Ratnaweera, H., Nastos, P., and Louta, M.: Building a smart green system to control water leakage and monitor drinking water quality in the water supply system of Paramythia city, Greece: the case of SMASH project, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10057, https://doi.org/10.5194/egusphere-egu23-10057, 2023.

current legislation requires the inspection and calibration of operational survey radiation monitoring instruments used in nuclear medicine and radiotherapy departments as well as in any field that uses ionizing radiation sources. As a result, Morocco's national secondary standard dosimetry laboratory provides reliable calibration results with high accuracy while adhering to national and international radiation protection standards and covering the various measurement ranges, using the attenuators offered by the automated Gamma G10 irradiator or the validated beam qualities produced by the X-ray irradiator type X80-320kV as required. The measurements’ reliability was demonstrated by participation in a comparison program launched by the International Atomic Energy Agency (IAEA).

This work aims to develop a digital graphical user interface designed for the calibration of measuring instruments in radiation protection through the programming language Python, which serves to facilitate the establishment of all operations and calculations related to the determination of calibration factors and measurement uncertainties according to the ISO 4037 standard in a minimum time that allows to process several instruments during the day with high accuracy, while minimizing the sources of errors, this interface allows the recording of calculations as well as the establishment and electronic archiving of the calibration certificate in pdf format ported from PHP FPDF.

How to cite: Belhaj, O. E., Boukhal, H., and Belhaj, S.: Digital graphical user interface as a facilitator for the calibration of radiation monitoring instruments according to ISO 4037:2019 at the national secondary standard dosimetry laboratory of morocco, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4503, https://doi.org/10.5194/egusphere-egu23-4503, 2023.

By calculating the water demand and programming a fine irrigation project, the management and cultivating efficiency of traditional agriculture can be greatly improved. Taking rotational irrigation for example, the efficiency of irrigation can be maximized by adjusting water distribution routes, irrigation area allocation, and irrigation schedule planning. However, in actual operation, some problems are often encountered, such as how to persuade farmers and promote the designed irrigation project, and the negotiation of various stakeholders. Generally, due to the complexity of the irrigation design model, it is impossible to have an effective and immediate communication or presentation. Therefore, this study introduces the Bayesian network to presents the key points of the irrigation project after simplifying the relationship. In addition to being simpler for stakeholders to understand, it is also possible to adjust various parameters in time to obtain rough estimation results.

The research area of this study is a 100-hectare farmland, which is located in Kinmen County, Taiwan. For many years, local farmers have only relied on precipitation to cultivate sorghum, wheat and other crops. However, the precipitation in Kinmen is semiarid and unstable. In the past five years, the annual rainfall has been lower than the average in previous years, which directly led to a very bleak crop harvest. Therefore, we hope to establish an irrigation project in Kinmen, using recycled water as the water source to provide local farmers with a reliable water source.

The Bayesian network used in this study is a directed acyclic graphical (DAG) model based on conditional probability and Bayesian theorem to express the possible relationship between variables. In terms of operation, the different influencing factors in the research topic are converted into nodes, and the relationship between nodes is given by different conditional probabilities. This study uses GeNIe to establish a Bayesian network that can be used to estimate water profit and loss and other results. This Bayesian network can be divided into four sub-blocks, which are the relevant data of the irrigation area, the water demand, the water supply, and the final result calculation. Therefore, when the stakeholders are negotiating the irrigation project, they can discuss the different estimation results by adjusting each node of the first three sub-blocks.

How to cite: Su, Y. and Yu, H.-L.: Application of Bayesian Network in Analysis and Management of Agricultural Water - Taking Kinmen for Example, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10515, https://doi.org/10.5194/egusphere-egu23-10515, 2023.

Assessing the Sustainability in Water Use under
Different Agricultural Management Planning
in Yeongsan-River Basin, South Korea

Yujong Jeong¹, Hyun-woo Jo¹, YanYan¹, Minwoo Noh¹, Woo-Kyun Lee^1*

¹ Department of Environmental Science and Ecological Engineering, Korea University, Seoul 02841, Republic of Korea

*E-mail: leewk@korea.ac.kr

(Address: Korea University, Anamro 145, Seongbukgu, Seoul 02841, Republic of Korea)

Abstract:

From the past, South Korea has been experiencing high level of water stress as reported by WRI, in 2013, and chronically imbalanced spatiotemporal water allocation. Yeongsan-river basin, where the biggest national breadbasket is located, is facing unequal water allocation among different water uses and inefficient water management under episodic water shortage conditions. Therefore, the main objective of this study was to analyse current water management and allocation scheme, and to evaluate 3 different agricultural management plans in terms of efficiency and equity. The Soil and Water Assessment Tool(SWAT) was applied to simulate the hydrological process and crop yield in the basin. The model was calibrated and validated using observed outflows to set adequate system parameters for the entire watershed. Crop water productivity and spatial-temporal-sectoral water distribution are utilized as the indices to evaluate different agricultural strategies. The results suggested that there was potential to improve both crop productivity and water allocation at the same time with the suggested plannings. Crop water productivity increased in all three strategies in order of on-farm management measures (precise agriculture), crop diversification (replacing rice to beans) and agroforestry (mixing trees and crops). The crop water productivity of on-farm measurement ranges from 5t/L to 13t/L and rises about 20% on average. In addition, it is found that applying the combination of different agricultural management measures could achieve better water allocation in terms of space and time, and between agriculture and ecosystem. The outcomes of this study can serve scientific-evidence policy and decision-making systems for sustainable agricultural society and ecosystem.

Keywords: Hydrological Modelling, SWAT, Crop water productivity, Water allocation, Agricultural Management Planning, Yeongsan-River Basin

How to cite: Jeong, Y.: Assessing the Sustainability in Water Use under Different Agricultural Management Planning in Yeongsan-River Basin, South Korea, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10943, https://doi.org/10.5194/egusphere-egu23-10943, 2023.

Water, food, and energy security are the major climate risks of global warming. The Paris Agreement proposed an ambitious target of limiting the rise in global mean surface temperature to well below 2⁰C, and preferably to 1.5⁰C, compared to the pre-industrial era. However, the implication of this policy discourse on the agricultural system is imperative for ensuring food security in the face of global warming. This research focuses on understanding the changes in water availability and rice productivity under 1.5⁰C global warming over a global rice-exporting semi-arid watershed in Central India. Towards this goal, the mean climate under 1.5⁰C of global warming was computed for 21 Coupled Model Intercomparison Project Phase 6 (CMIP6) Global Climate models (GCMs). For each GCM, the corresponding changes in blue-green water availability and rice productivity at 1.5⁰C warming period were estimated under two global warming scenarios (SSP2-4.5 and SSP5-8.5) based on the semi-distributed Soil and Water Assessment Tool (SWAT). Results suggest that the green and blue water is projected to change by ~ -20% to 10 and ~ -50 to 20%, respectively. The rice yield is projected to reduce in the range of 5% to 50%, with an increase in local temperature (~1⁰C) and a decrease in local precipitation (~20%) being the limiting factor. This study provides useful information on when the 1.5⁰C global warming could reach and how it can affect the agricultural productivity of semi-arid watersheds across different global warming scenarios. This study will help develop appropriate strategies to reduce/alleviate the impacts of global warming and foster food security at the watershed-scale.   

How to cite: Tyagi, S., Sahany, S., Saraswat, D., Mishra, S. K., Dubey, A., and Niyogi, D.: Implications of 1.50C global warming for agricultural productivity over a global rice exporting region in Central India, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11183, https://doi.org/10.5194/egusphere-egu23-11183, 2023.

        Due to climate change, Taiwan's rainfall has become unstable in recent years, leading to short rainy seasons and low rainfall. In 2021, a severe drought occurred due to the lowest rainfall on record. Groundwater is essential for agricultural development, but less than 10% of wells are legal. Improper or excessive use of groundwater resources can cause serious disasters, such as sea intrusion and land subsidence. However, if the government and farmers extract groundwater effectively and sustainably, it will bring more flexibility to water management.

        In this study, a land subsidence model was conducted based on geological conditions and groundwater level. This study analyzes multi layer compaction monitoring well profiles, and further finds the correlation among the two main factors and subsidence. The goal of this study is to visualize which areas are more suitable for using groundwater and assist the government in water resource management. This study focuses on the Choshui river alluvial fan in Taiwan. A risk map of land subsidence for this area is made by evaluating two main factors, geological conditions and groundwater level.

How to cite: Su, S.-H. and Yu, H.-L.: Assessment of Land Subsidence based on Geological Conditions, Groundwater Levels in the Choshui River Alluvial Fan, Taiwan, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11879, https://doi.org/10.5194/egusphere-egu23-11879, 2023.

Harmful Algal Blooms (HABs) are increasing in frequency and magnitude globally. These episodes are associated with the generation of biotoxins, which pose a potential risk to human and animal health. Biotoxins notably affect aquaculture activities and shellfish production, which has a clear impact on food and human health. Consequently, it is sometimes necessary to close the harvesting areas until the organisms are decontaminated. These natural detoxification mechanisms depend largely on the type of toxin and physiology of the organism, resulting in lengthy processes that can cause severe economic losses to aquaculture activities. As the main goal of this communication, we propose a technological alternative for the degradation of marine biotoxins through the implementation of UV technology as a treatment for agricultural, environmental, and health-related purposes. Therefore, advanced photochemical processes should be evaluated for the efficient degradation of marine biotoxins. The toxin selected was okadaic acid (OA), which is a very stable diarrheal toxin (DSP) and has a great impact on shellfish production areas, e.g. on the Portuguese coast. First, irradiation experiments were performed under UV-A, UV-B, and UV-C irradiation. In general, the concentration remained similar after different UV exposures, indicating that there was no observable photodegradation of OA after 3 h of UV irradiation, detecting a maximum degradation of 19.5% (± 0.95) in the UV-C region, suggesting that OA is clearly resistant to UV photodegradation. Second, the combined UV/H₂O₂, UV/HSO₅⁻, and UV/S₂O₈^{2 −} processes were tested. Two different UV sources were evaluated: LED and low-pressure lamps (LP), performing OA exposure in distilled water and seawater, with a maximum UV exposure of 3 h. In general, a clear degradation of OA is observed in photochemical processes in distilled water, with a slight decrease in efficiency in the UV/H₂O₂ process with an LED irradiation source. In the case of UV/S₂O₈^{2 −} and UV/HSO₅⁻, both the LP lamp and LED achieved a total degradation of OA. In the case of the marine matrix, the effect is clearly inhibited for the UV/H₂O₂ process; however, for UV/ HSO₅⁻, salinity does not seem to affect OA degradation, obtaining practically 100% removal. The study of new UV-LEDs would favor aquaculture activities by increasing sustainability and health safety. Likewise, the results obtained might provide the basis for a possible scale-up of technological processes specifically designed for the minimization of marine biotoxins.

How to cite: Moreno-Andrés, J., Lage, S., Braga, A. C., Romero-Martínez, L., Acevedo-Merino, A., Nebot, E., and Costa, P. R.: Photo-driven processes for the removal of biotoxins derived from Harmful Microalgal Blooms, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12693, https://doi.org/10.5194/egusphere-egu23-12693, 2023.

The need for water management in the agriculture sector, which is a 70% consumer of global water resources, is imperative. For the same, a plant-based index called crop water stress index (CWSI) is widely being adopted for irrigation scheduling. An empirically derived CWSI is dependent on three parameters of canopy temperature (T_c), air temperature (T_a), and relative humidity (RH).This study was conducted by performing controlled crop experiments in the arid region of Uttar Pradesh state of India, which aims to evaluate the significance of height of T_c observations, taken from March to April 2022, on CWSI calculations for the wheat crop.This has been done by observing theT_c by aiming the wheat crop from the top of the crown at two distances of 10 cm and 100 cm, respectively. Handheld remote sensingdevice known as infrared thermometeris used for the observation of canopy temperature. Variation in the height from 10 cm to 100 cm leads to a variation in the field of view from 51.28 sq. cm to 5128 sq. cm. The effect of enhanced area and the involvement of extra soiland vegetation pixels can be understood by this work. Five different irrigation regimes have been provided to study the effect of change in height for T_c observations. The regimes consist of five plots 1,2,3,4, and 5 with soil moisture depletion by the following percentage respectively: 50% in drip irrigation, 25% in drip irrigation, unregulated flood irrigation, 50% in flood irrigation, and no irrigation plot.Plot 2 has been used to formulatea lower baselinefor CWSI calculations. A lower baseline represents a non-water-stressed condition of the crop where the crop is provided with sufficient irrigation treatment leading towards negligible stress conditions. The lower baseline equations used for CWSI assessment for 10 cm and 100 cm height are -1.287(VPD) -2.19 and -1.214(VPD)-1.738, respectively. VPD represents vapor pressure deficit which is a function of T_a and RH. Upon increasing the height from 10 cm to 100 cm, Tc increased by 2.1%, 2.7%, 0.6%, 0.9%, and 1.3% for plots 1,2,3,4, and 5, respectively. This change in temperature led to a decrease in CWSI by 21.8%,36.4 %,9.2%, and 12.2% in plots 1, 2, 3, and 4 respectively. An increase in CWSI by 5.8% for a rise of 1.3% in T_c for plot 5 was also noted. Further coefficient of determination R² was observed between CWSI at 10 cm height and CWSI at 100 cm height for all plots. It was observed to be 0.65, 0.50, 0.93, 0.93, and 0.87 for plots 1, 2, 3, 4, and 5, respectively. This study shows the effect of observation distance of crop canopy temperature on CWSI that can lead to the development of sampling procedures meant for CWSI studies.

How to cite: Yadav, A., Upreti, H., and Singhal, G.: Effect of distance of crop canopy temperature observations on Crop Water Stress Index, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15429, https://doi.org/10.5194/egusphere-egu23-15429, 2023.

The importance of forecasting agricultural production in Sub-Saharan Africa (SSA) is increasing for the management of agricultural supply chains, market forecasting, and targeting of food aid. In particular, agricultural forecasts enable governments and humanitarian organizations to respond more effectively to shocks in food production and price spikes resulting from extreme droughts. In this study, we use hydroclimate, earth observations (EO) and machine learning to develop an operational, sub-national grain production forecast system for a number of SSA countries, including food-insecure regions where rapid response is critical. Before creating the forecast, we collect and organize crop production data from the Famine Early Warning Systems Network in order to identify trends and variability in agricultural technology, climate, and vegetation. In addition, we demonstrate the capability of hydroclimate and EO data to capture favorable or unfavorable crop development conditions during the growing season. In addition, we demonstrate a unique capability that explains how EO characteristics influence current grain production forecasts, thereby enhancing the forecasts' reliability and efficacy. This research lays the groundwork for the development of a large-scale, operational crop yield forecasting system that will provide actionable predictions of food shocks for famine early warning and guide advanced preparedness and response strategies.

How to cite: Lee, D., Davenport, F., Shukla, S., Harrison, L., Husak, G., Funk, C., Budde, M., Rowland, J., McNally, A., and Verdin, J.: Leveraging Hydroclimate and Earth Observation to Predict Grain Production in Sub-Saharan Africa, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10953, https://doi.org/10.5194/egusphere-egu23-10953, 2023.

Irrigation plays a crucial role in alleviating the negative effects of drought on crop production. However, increasing competition for water by other sectors, such as industry and domestic use, increases the pressure on available water supplies. Under these circumstances, agricultural producers must be able to manage their available supplies efficiently to optimize irrigation water use. The objective of this research is to develop a decision support system (DSS) for optimizing irrigation scheduling for cotton production using Deep Reinforcement learning (DRL). Our approach uses multiple DRL algorithms that enable an intelligent agent to learn cotton irrigation needs in an interactive environment by trial and error using feedback from its past actions and experiences. Aquacrop is used as an environment (cotton field) simulator and is coupled with a DRL model to simulate crop yield for different actions taken by the agent. Our proposed software estimates the daily irrigation needs of a 7-acre crop field irrigated by a center pivot system located at Clemson University's Edisto Research and Education Center (REC), near Blackville, South Carolina. This new system enables a closed-loop control scheme to adapt the DSS to local perturbations such as soil moisture and rainfall variabilities.

How to cite: Umutoni, L.: An Intelligent Irrigation Decision Support System for Optimizing Cotton Water Use, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16787, https://doi.org/10.5194/egusphere-egu23-16787, 2023.