Agricultural Drought Risk Assessment of Rainfed Agriculture in the Sudan Using Remote Sensing and GIS: The Case of El Gedaref State

Hitherto, most research conducted to monitor agricultural drought on the African continent has focused only on meteorological aspects, with less attention paid to soil moisture, which describes agricultural drought. Satellite missions dedicated to soil moisture monitoring must be used with caution across various scales. The rainfed sector of Sudan takes great importance due to it is high potential to support national food security. El Gedaref state is significant in Sudan given its potentiality of the agricultural sector under a mechanized system, where crop cultivation supports livelihood sources for about 80% of its population and households, directly through agricultural production and indirectly through labor workforce. The state is an essential rainfed region for sorghum production, located within Sudan's Central Clay Plain (CCP). Enhancing soil moisture estimation is key to boosting the understanding of agricultural drought in the farming lands of Sudan. Soil moisture measuring stations/sensors networks do not exist in the El Gedaref agricultural rainfed sector. The literature shows a significant gap in whether soil moisture is sufficient to meet the estimated water demands of cultivation or the start of the growing season. The purpose of this study is to focus principally on agricultural drought. The soil moisture data retrieved from the Soil Moisture Active Passive (SMAP) mission launched by NASA in 2015 were compared against in situ data measurements over the agricultural lands. In situ points (at 5 cm, 10 cm, and 20 cm depths) corresponding to 9×9 km SMAP pixel foot-print are rescaled to conduct a point-to-pixel evaluation of SMAP product over two locations, namely Samsam and Kilo-6, during the rainy season 2018. Four errors were measured; Root Mean Squared Error (RMSE), Mean Bias Error (MBE), unbiased RMSE (ubRMSE), Mean Absolute Bias Error (MABE), and the coefficient of determination R2. SMAP improve (significantly at the 5% level for SM). The results indicated that the SMAP product meets its soil moisture accuracy requirement at the top 5 cm and in the root zone (10 and 20 cm) depths at Samsam and Kilo-6. SMAP demonstrates higher performance indicated by the high R2 (0.96, 0.88, and 0.97) and (0.85, 0.94, and 0.94) over Samsam and Kilo-6, respectively, and met its accuracy targeted by SMAP retrieval domain at ubRMSE 0.04 m3m-3 or better in all locations, and most minor errors (MBE, MABE, and RMSE). The possibility of using SMAP products was discussed to measure agricultural drought and its impacts on crop growth during various growth stages in both locations and over the CCP entirely. The croplands of El Gedaref are located within the tropical savanna (AW, categorization following the Köppen climate classification), warm semi-arid climate (BSh), and warm desert climate (BWh). The areas of interest are predominantly rainfed agricultural lands, vulnerable to climate change and variability. The Climate Hazards Group Infrared Precipitation with Station data (CHIRPS), SMAP at the top surface of the soil and the root zone, and Soil Water Deficit Index (SWDI) derived from SMAP were analyzed against the Normalized Difference Vegetation Index (NDVI). The results indicate that the NDVI val-ues disagree with rainfall patterns at the dekadal scale. At all isohyets, SWDI in the root zone shows a reliable and expected response of capturing seasonal dynamics concerning the vegetation index (NDVI) over warm desert climates during 2015, 2016, 2017, 2018, and 2019, respectively. It is concluded that SWDI can be used to monitor agricultural drought better than rainfall data and SMAP data because it deals directly with the available water content of the crops. SWDI monitoring agricultural drought is a promising method for early drought warning, which can be used for agricultural drought risk management in semi-arid climates. The comparison between sorghum yield and the spatially distributed water balance model was assessed according to the length of the growing period. Late maturing (120 days), medium maturing (90-95 days), and early maturing variety (80-85 days). As a straightforward crop water deficit model. An adapted WRSI index was developed to characterize the effect of using different climatic and soil moisture remote sensing input datasets, such as CHIRPS rainfall, SMAP soil moisture at the top 5 cm and the root zone, MODIS actual evapotranspiration on key WRSI index parameters and outputs. Results from the analyses indicated that SMAP best captures season onset and length of the growing period, which are critical for the WRSI index. In addition, short-, medium-, and long-term sorghum cultivar planting scenarios were con-sidered and simulated. It was found that over half of the variability in yield is explained by water stress when the SMAP at root zone dataset is used in the WRSI model (R2=0.59–0.72 for sorghum varieties of 90–120 days growing length). Overall, CHIRPS and SMAP root zone show the highest skill (R2=0.53–0.64 and 0.54–0.56, respectively) in capturing state-level crop yield losses related to seasonal soil moisture deficit, which is critical for drought early warning and agrometeorological risk applications. The results of this study are important and valuable in supporting the continued development and improvement of satellite-based soil moisture sensing to produce higher accuracy soil moisture products in semi-arid regions. The results also highlight the growing awareness among various stakeholders of the impact of drought on crop production and the need to scale up adaptation measures to mitigate the adverse effects of drought

Genomic and Physiological Approaches to Improve Drought Tolerance in Soybean

Drought stress is a major global constraint for crop production, and improving crop tolerance to drought is of critical importance. Direct selection of drought tolerance among genotypes for yield is limited because of low heritability, polygenic control, epistasis effects, and genotype by environment interactions. Crop physiology can play a major role for improving drought tolerance through the identification of traits associated with drought tolerance that can be used as indirect selection criteria in a breeding program. Carbon isotope ratio (δ13C, associated with water use efficiency), oxygen isotope ratio (δ18O, associated with transpiration), canopy temperature (CT), canopy wilting, and canopy coverage (CC) are promising physiological traits associated with improvement of drought tolerance. Genome-wide association studies (GWAS) are one of the genomic approaches to provide a high mapping resolution for complex trait variation such as those related to drought tolerance. The objectives of this research were to identify genomic regions and favorable alleles that contribute to drought-tolerant traits. A diverse panel consisting of 373 maturity group (MG) IV soybean accessions was evaluated for δ13C, δ18O, canopy wilting, canopy coverage, and canopy temperature in multiple environments. A set of 31,260 polymorphic SNPs with a minor allele frequency (MAF) ≥ 5% was used for association mapping of CT using the FarmCPU model. Association mapping identified 54 significant SNPs associated with δ13C, 47 significant SNPs associated with δ18O, 61 significant SNPs associated canopy wilting, 41 and 56 significant SNPs associated with CC for first and second measurements dates, respectively, and 52 significant SNPs associated with CT. Several genes were identified using these significant SNPs, and those genes had reported functions related to transpiration, water transport, growth, developmental, root development, response to abscisic acid stimulus, and stomatal complex morphogenesis. Favorable alleles from significant SNPs may be an important resource for pyramiding genes to improve drought tolerance and for identifying parental genotypes for use in breeding programs

Assessment of the hydrological effect of drought and fire events on evapotranspiration at a regional scale

Doutoramento em Engenharia Florestal e dos Recursos Naturais - Instituto Superior de Agronomia / ULUntil today, there is only little knowledge about the behavior of actual evapotranspiration (ETa) before and after wildfires in Portugal, which can be estimated from remote sensing techniques. In this thesis, an existing Simplified Two-Source Energy Balance model (STSEB) was adapted, based on moderate resolution imagery to estimate ETa and its contributing parts of transpiration and evaporation. The study served to test the model and its precision. A bias of about 1 mm d1 for the estimated ETa was observed, where evaporation was regularly overestimated and transpiration underestimated. This error is acceptable for two-layer models based on satellite imagery, but estimates cannot be used for irrigation management. The evolution of the estimated ETa after wildfires (up to four years) was analyzed at eucalypt stands at the Caramulo mountain range in Portugal. By investigating the recovery of ETa after wildfire, the difference between burnt and unburnt stands was mainly related to fire severity and stand characteristics. Two to three years after the fire events, the difference between burnt and unburnt stands became nonsignificant for all severity classes. At the same region, the prediction of soil moisture deficit from drought indices was tested. The drought indices empirically estimate the dryness of an area and are directly related to fire danger. They are based on a simple water balance equation where effective rainfall and ETa are the only input and output, respectively. In this work the empirical equation of (ETa) was substituted by the estimated ETa from STSEB, which enhanced the spatial resolution of the drought indices, being regularly interpolated from point estimates. Spatial patterns of soil moisture deficit were predicted, which indicated a relationship to fire occurrences. To conclude, the ETa estimated by the remote sensing based STSEB model, was used to make observations of the water cycle on a regional scale. In contrast to other post-fire studies, eucalypt stands in Portugal were found to be subject to a smaller hydrological impact after wildfires. This implies a fast recovery and a smaller influence on streamflow and groundwater resources. Furthermore, the drought indices, using the ETa from STSEB, identified areas with higher proneness to drought, by improving the spatial resolution, using satellite imagery compared to traditional interpolation techniques. The results support fire danger rating and might help to improve fire regime and forest managementN/

Spatial-Temporal Patterns of Agricultural Drought in Upper Progo Watershed Based on Remote Sensing and Land Physical Characteristics

Agricultural drought is alarmed by meteorological drought characterized by lower year-to-year rainfall. Under long period and continuous water deficits, plants may demonstrate stress symptoms and wilt or die. Furthermore, agricultural drought leads to crop failures and threaten the food security of an area. Progo Hulu sub-watershed is a major agricultural area in Temanggung Regency. Spatial-temporal pattern-based information about agricultural drought can be a basis for decision making in drought mitigation. This study aims to analyze spatial and temporal distribution patterns of drought, analyze the physical characteristics of land and their influence on drought pattern, and establish a prediction model of drought distribution patterns based on four physical characteristics of the land. Landsat 8 imagery is used to determine the spatial and temporal patterns of agricultural drought in Upper Progo watershed using an improved Temperature vegetation Dryness Index (iTVDI). Slope, land use, landform, and soil texture are the physical characteristics of land as the variables to determine the most influential factor of drought pattern. They are analyzed using multiple regression analysis techniques. Pixel samples are obtained through purposive sampling method based on land units. The results reveal that the spatial-temporal distribution of agricultural drought occurs rapidly on the slopes and foothills of Sumbing and Sindoro. These areas have the highest average value of the iTVDI index. Agricultural drought extends gradually in line with the number of days without rainfall. Landform is a physical characteristic that most influences the distribution of agricultural drought. The established model by utilizing four variables of physical characteristics generates an average value which almost similar to the iTVDI value produced by remote sensing data. The model can be useful to estimate drought distribution based on the number of days without rainfall

Novel Satellite-Based Methodologies for Multi-Sensor and Multi-Scale Environmental Monitoring to Preserve Natural Capital

Global warming, as the biggest manifestation of climate change, has changed the distribution of water in the hydrological cycle by increasing the evapotranspiration rate resulting in anthropogenic and natural hazards adversely affecting modern and past human properties and heritage in different parts of the world. The comprehension of environmental issues is critical for ensuring our existence on Earth and environmental sustainability. Environmental modeling can be described as a simplified form of a real system that enhances our knowledge of how a system operates. Such models represent the functioning of various processes of the environment, such as processes related to the atmosphere, hydrology, land surface, and vegetation. The environmental models can be applied on a wide range of spatiotemporal scales (i.e. from local to global and from daily to decadal levels); and they can employ various types of models (e.g. process-driven, empirical or data-driven, deterministic, stochastic, etc.). Satellite remote sensing and Earth Observation techniques can be utilized as a powerful tool for flood mapping and monitoring. By increasing the number of satellites orbiting around the Earth, the spatial and temporal coverage of environmental phenomenon on the planet has in-creased. However, handling such a massive amount of data was a challenge for researchers in terms of data curation and pre-processing as well as required computational power. The advent of cloud computing platforms has eliminated such steps and created a great opportunity for rapid response to environmental crises. The purpose of this study was to gather state-of-the-art remote sensing and/or earth observation techniques and to further the knowledge concerned with any aspect of the use of remote sensing and/or big data in the field of geospatial analysis. In order to achieve the goals of this study, some of the water-related climate-change phenomena were studied via different mathematical, statistical, geomorphological and physical models using different satellite and in-situ data on different centralized and decentralized computational platforms. The structure of this study was divided into three chapters with their own materials, methodologies and results including: (1) flood monitoring; (2) soil water balance modeling; and (3) vegetation monitoring. The results of this part of the study can be summarize in: 1) presenting innovative procedures for fast and semi-automatic flood mapping and monitoring based on geomorphic methods, change detection techniques and remote sensing data; 2) modeling soil moisture and water balance components in the root zone layer using in-situ, drone and satellite data; incorporating downscaling techniques; 3) combining statistical methods with the remote sensing data for detecting inner anomalies in the vegetation covers such as pest emergence; 4) stablishing and disseminating the use of cloud computation platforms such as Google Earth Engine in order to eliminate the unnecessary steps for data curation and pre-processing as well as required computational power to handle the massive amount of RS data. As a conclusion, this study resulted in provision of useful information and methodologies for setting up strategies to mitigate damage and support the preservation of areas and landscape rich in cultural and natural heritage

Genetics of Physiological Traits Associated with Drought Tolerance in Soybean (Glycine max)

Soybean (Glycine max L.) is one of the major row crops in the United States, and its production is often limited by drought stress. Physiological traits from exotic germplasm that confer drought tolerance may be useful in improving commercial soybean production. For example, carbon isotope ratio (δ13C) is positively correlated with water use efficiency (WUE), and nitrogen isotope ratio (δ15N) is negatively correlated with N2 fixation; canopy temperature (CT) is an indicator for genetic variation in transpiration and stomatal conductance. Therefore, the objectives of this research were to identify the genomic regions associated with: (1) δ13C and δ15N using a population of 196 F6-derived recombinant inbred lines (RIL) from PI 416997 × PI 567201D that was phenotyped in four environments, (2) CT and δ13C using a population of 168 F5-derived RILs from KS4895 × Jackson that was phenotyped in multiple environments and irrigation treatments. In the PI 416997 × PI 567201D population, δ13C and δ15N had a wide phenotypic range in all environments, and PI 416997 had higher δ13C and lower δ15N values than PI 567201D. δ13C had high heritability (90%) whereas the heritability of δ15N was relatively lower (35%), indicating that δ15N was more affected by the environment. QTL mapping identified eight loci on seven chromosomes associated with δ13C, and these loci explained between 2.5 to 30% of the phenotypic variation. There were 13 loci on 10 chromosomes associated with δ15N, explaining 1.7 to 14.4% of the phenotypic variation. There were strong interactions between QTLs and environments for δ15N. In the KS4895 × Jackson RIL population, Jackson had a cooler canopy than KS4895, and the heritability of CT had low heritability (31%) across environments. There were 11 loci present on eight chromosomes associated with CT that individually explained 4.6 to 12.3% of the phenotypic variation. The heritability of δ13C in KS4895 × Jackson RIL population heritability was 83% when estimated over environments and over irrigation treatments. A total of 24 QTLs associated with δ13C were identified and clustered in nine genomic loci on seven chromosomes. The identified QTLs for δ13C, δ15N, and CT were co-localized with genomic regions associated with drought tolerance-related traits from previous studies. These genomic regions may be important resources in soybean breeding programs to improve tolerance to drought. Further research is needed to fine map the identified QTLs and validate markers linked with these regions

Physiological Characterization of the SoyNAM Parental Lines under Field Conditions

The narrow genetic pool of soybean (Glycine max L. Merr.) in North America can limit its future yield gains. Among the worldwide germplasm collection of 45,000 unique landraces, only 80 contribute 99% to the collective parentage of North American soybean cultivars. Among these 80 landraces, just 17 contribute to 86% of the collective parentage of the modern cultivars. The Soybean Nested Association Mapping population (SoyNAM) was therefore developed with the objective of diversifying the soybean gene pool. Forty diverse soybean genotypes from maturity groups (MG) 1 through 5 were crossed with a common MG 3 parent to develop 40 recombinant inbred populations. Each of these populations has 140 recombinant inbred lines (RILs) and have been genotyped with molecular markers and characterized for few important traits. This experiment was conducted during three consecutive summers, in Fayetteville, Arkansas with the objective to phenotype the SoyNAM parental lines for yield and drought-related traits. And, then identify the extreme genotypes among these parental genotypes, which have either not been mapped previously or if mapped have not been mapped very extensively. Canopy coverage was estimated through aerial digital images taken 3 to 4 times until canopy closure. After canopy closure, during late vegetative or early R1 stage, shoot samples were taken that were used to determine N2 derived from the atmosphere (NDFA), shoot nitrogen and ureide concentrations, and δ13C (an indirect measure of water use efficiency). Two harvests were made at mid-R5 and two weeks later, to calculate seed growth rate and effective filling period. Wilting measurements were taken towards the end of irrigation cycles when drought symptoms started appearing. Yield and harvest index (HI) were determined from a bordered section of each plot at maturity. Statistical analysis indicated that several parents differed statistically from the hub parent. Some genotypes were also identified as common extreme parents for more than one trait. Identification of such divergent parental lines will aid in selecting recombinant inbred populations for future quantitative trait loci mapping studies

New trends in plant breeding - example of soybean

Soybean breeding and selection is a continual process designed to increase yield levels and improve resistance to biotic and abiotic stresses. Soybean breeders have been successful in producing a large number of varieties using conventional breeding methods, the Single Seed Descent method in particular. In recent decades, with the increased use of genetic transformations, backcrossing is more frequent though the only trait that has been commercialized is glyphosate tolerance. Physiological breeding poses a particular challenge, as well as phenotyping and development of useful criteria and techniques suitable for plant breeding. Using modern remote sensing techniques provides great opportunity for collecting a large amount of physiological data in real environment, which is necessary for physiological breeding. Molecular based plant breeding methods and techniques are a conceptual part of any serious breeding program. Among those methods, the most extensively used is marker-assisted selection, as a supplement to conventional breeding methods