Simulation of site-specific irrigation control strategies with sparse input data

Crop and irrigation water use efficiencies may be improved by managing irrigation application timing and volumes using physical and agronomic principles. However, the crop water requirement may be spatially variable due to different soil properties and genetic variations in the crop across the field. Adaptive control strategies can be used to locally control water applications in response to in-field temporal and spatial variability with the aim of maximising both crop development and water use efficiency. A simulation framework ‘VARIwise’ has been created to aid the development, evaluation and management of spatially and temporally varied adaptive irrigation control strategies (McCarthy et al., 2010). VARIwise enables alternative control strategies to be simulated with different crop and environmental conditions and at a range of spatial resolutions. An iterative learning controller and model predictive controller have been implemented in VARIwise to improve the irrigation of cotton. The iterative learning control strategy involves using the soil moisture response to the previous irrigation volume to adjust the applied irrigation volume applied at the next irrigation event. For field implementation this controller has low data requirements as only soil moisture data is required after each irrigation event. In contrast, a model predictive controller has high data requirements as measured soil and plant data are required at a high spatial resolution in a field implementation. Model predictive control involves using a calibrated model to determine the irrigation application and/or timing which results in the highest predicted yield or water use efficiency. The implementation of these strategies is described and a case study is presented to demonstrate the operation of the strategies with various levels of data availability. It is concluded that in situations of sparse data, the iterative learning controller performs significantly better than a model predictive controller

Air pollution and livestock production

The air in a livestock farming environment contains high concentrations of dust particles and gaseous pollutants. The total inhalable dust can enter the nose and mouth during normal breathing and the thoracic dust can reach into the lungs. However, it is the respirable dust particles that can penetrate further into the gas-exchange region, making it the most hazardous dust component. Prolonged exposure to high concentrations of dust particles can lead to respiratory health issues for both livestock and farming staff. Ammonia, an example of a gaseous pollutant, is derived from the decomposition of nitrous compounds. Increased exposure to ammonia may also have an effect on the health of humans and livestock. There are a number of technologies available to ensure exposure to these pollutants is minimised. Through proactive means, (the optimal design and management of livestock buildings) air quality can be improved to reduce the likelihood of risks associated with sub-optimal air quality. Once air problems have taken hold, other reduction methods need to be applied utilising a more reactive approach. A key requirement for the control of concentration and exposure of airborne pollutants to an acceptable level is to be able to conduct real-time measurements of these pollutants. This paper provides a review of airborne pollution including methods to both measure and control the concentration of pollutants in livestock buildings

Use of habitat suitability modeling in the integrated urban water system modeling of the Drava River (Varazdin, Croatia)

The development of practical tools for providing accurate ecological assessment of rivers and species conditions is necessary to preserve habitats and species, stop degradation and restore water quality. An understanding of the causal mechanisms and processes that affect the ecological water quality and shape macroinvertebrate communities at a local scale has important implications for conservation management and river restoration. This study used the integration of wastewater treatment, river water quality and ecological assessment models to study the effect of upgrading a wastewater treatment plant (WWTP) and their ecological effects for the receiving river. The WWTP and the water quality and quantity of the Drava river in Croatia were modelled in the software WEST. For the ecological modeling, the approach followed was to build habitat suitability and ecological assessment models based on classification trees. This technique allows predicting the biological water quality in terms of the occurrence of macroinvertebrates and the river status according to ecological water quality indices. The ecological models developed were satisfactory, and showed a good predictive performance and good discrimination capacity. Using the integrated ecological model for the Drava river, three scenarios were run and evaluated. The scenario assessment showed that it is necessary an integrated approach for the water management of the Drava river, which considers an upgrading of the WWTP with Nitrogen and Phosphorous removal and the treatment of other diffuse pollution and point sources (including the overflow of the WWTP). Additionally, if an increase in the minimum instream flow after the dams is considered, a higher dilution capacity and a higher self-cleaning capability could be obtained. The results proved that integrated models like the one presented here have an added value for decision support in water management. This kind of integrated approach is useful to get insight in aquatic ecosystems, for assessing investments in sanitation infrastructure of urban wastewater systems considering both, the fulfilling of legal physical chemical emission limits and the ecological state of the receiving waters

A site-specific and dynamic modeling system for zoning and optimizing variable rate irrigation in cotton

Cotton irrigation has been rapidly expanding in west Tennessee during the past decade. Variable rate irrigation is expected to enhance water use efficiency and crop yield in this region due to the significant field-scale soil spatial heterogeneity. A detailed understanding of the soil available water content within the effective root zone is needed to optimally schedule irrigation. In addition, site-specific crop-yield mathematical relationships should be established to identify optimum irrigation management. This study aimed to design and evaluate a site-specific modeling system for zoning and optimizing variable rate irrigation in cotton. The specific objectives of this study were to investigate (i) the spatial variability of soil attributes at the field-scale, (ii) site-specific cotton lint yieldwater relationships across all soil types, and (iii) multiple zoning strategies for variable rate irrigation scenarios. The field (73 ha) was sampled and apparent soil electrical conductivity (ECa) was measured. Landsat 8 satellite data was acquired, processed, and transformed to compare indicators of vegetation and soil response to cotton lint yields, variable irrigation rates, and the spatial variability of soil attributes. Multiple modeling scenarios were developed and examined. Although experiments were performed during two wet years, supplemental irrigation enhanced cotton yield across all soil types in comparison with rain-fed conditions. However, length of cropping season and rainfall distribution remarkably affected cotton response to supplemental irrigation. Geostatistical analysis showed spatial variability in soil textural components and water content was significant and correlated to yield patterns. There was as high as four-fold difference between available water content between coarse-textured and fine-textured soils on the study site. A good agreement was observed (RMSE = 0.052 cm3 cm-3 [cubic centimeter per cubic centimeter] and r = 0.88) between predicted and observed water contents. ECa and space images were useful proximal data to investigate soil spatial variability. The site-specific water production functions performed well at predicting cotton lint yield with RMSE equal to 0.131 Mg ha-1 [megagram per hectare] and 0.194 Mg ha-1 in 2013 and 2014, respectively. The findings revealed that variable rate irrigation with pie shape zones could enhance cotton lint yield under supplemental irrigation in west Tennessee

Novel Approach for Forecasting Sugarcane Crop Yield: A Real-Time Prediction

Agriculture is an environment in which there is considerable confusion. Crop development depends largely on several variables, including climate, temperature, genetics, politics and economics. In addition, a huge number of raw agriculture statistics are available, but study of those details for estimating crop yield is quite challenging. The most challenging job is therefore to include accurate details and awareness about the raw farm data. In order to evaluate cultivation yield, data mining could customize data expertise. The objective of this study was to predict crop yields through the use of data mining technological advances. Moreover, this paper compared various classification algorithms and it is expected that the results of the study may enhance the actual yields of sugarcane in a wide number of tropical fields. The specifications used in the forecast were plot (soil size, plant area, rain distance, previous year's plant yield), sugar-cane characteristics (cane class and sort), crop cultivation procedure (normal water resource size, cultivation technique, disease management process, sort / procedure of fertilizer) as well as rain quantity

Densification of spatially-sparse legacy soil data at a national scale: a digital mapping approach

Digital soil mapping (DSM) is a viable approach to providing spatial soil information but its adoption at the national scale, especially in sub-Saharan Africa, is limited by low spread of data. Therefore, the focus of this thesis is on optimizing DSM techniques for densification of sparse legacy soil data using Nigeria as a case study. First, the robustness of Random Forest model (RFM) was tested in predicting soil particle-size fractions as a compositional data using additive log-ratio technique. Results indicated good prediction accuracy with RFM while soils are largely coarse-textured especially in the northern region. Second, soil organic carbon (SOC) and bulk density (BD) were predicted from which SOC density and stock were calculated. These were overlaid with land use/land cover (LULC), agro-ecological zone (AEZ) and soil maps to quantify the carbon sequestration of soils and their variation across different AEZs. Results showed that 6.5 Pg C with an average of 71.60 Mg C ha–1 abound in the top 1 m soil depth. Furthermore, to improve the performance of BD and effective cation exchange capacity (ECEC) pedotransfer functions (PTFs), the inclusion of environmental data was explored using multiple linear regression (MLR) and RFM. Results showed an increase in performance of PTFs with the use of soil and environmental data. Finally, the application of Choquet fuzzy integral (CI) technique in irrigation suitability assessment was assessed. This was achieved through multi-criteria analysis of soil, climatic, landscape and socio-economic indices. Results showed that CI is a better aggregation operator compared to weighted mean technique. A total of 3.34 x 106 ha is suitable for surface irrigation in Nigeria while major limitations are due to topographic and soil attributes. Research findings will provide quantitative basis for framing appropriate policies on sustainable food production and environmental management, especially in resource-poor countries of the world

Mesoscale mapping of sediment source hotspots for dam sediment management in data-sparse semi-arid catchments

Land degradation and water availability in semi-arid regions are interdependent challenges for management that are influenced by climatic and anthropogenic changes. Erosion and high sediment loads in rivers cause reservoir siltation and decrease storage capacity, which pose risk on water security for citizens, agriculture, and industry. In regions where resources for management are limited, identifying spatial-temporal variability of sediment sources is crucial to decrease siltation. Despite widespread availability of rigorous methods, approaches simplifying spatial and temporal variability of erosion are often inappropriately applied to very data sparse semi-arid regions. In this work, we review existing approaches for mapping erosional hotspots, and provide an example of spatial-temporal mapping approach in two case study regions. The barriers limiting data availability and their effects on erosion mapping methods, their validation, and resulting prioritization of leverage management areas are discussed.BMBF, 02WGR1421A-I, GROW - Verbundprojekt SaWaM: Saisonales Wasserressourcen-Management in Trockenregionen: Praxistransfer regionalisierter globaler Informationen, Teilprojekt 1DFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli

Quantitative Assessment of Water Security Using a Hydrological Modeling Framework

Water scarcity and drought are major threats to water security. Quantifying and defining boundaries between these threats are necessary to properly assess water security of a region. A comprehensive assessment of water security in terms of water scarcity, water vulnerability and drought can address water policy issues related to hydrological conditions and their interactions with societal and ecosystem functioning. Therefore, study of water security can provide useful information to multiple stakeholders. The overarching goal of this thesis is to improve water security in river basins around the world. To demonstrate our proposed methods, we selected Savannah River Basin (SRB) as a case study. In addition to water security assessment of SRB, we also explored the combined as well as individual roles of climate, anthropogenic (e.g., urbanization, agriculture, water demand) and ecological elements on various aspects of water security. Realizing the importance of water security impacts on society and ecosystem, the following objectives are formulated: 1) To investigate the blue and green water security of Savannah River Basin by applying the water footprint concept. 2) To quantify the influence of climate variability and land use change on streamflow, ecosystem services, and water scarcity. 3) To assess the climate, catchment, and morphological variables control over hydrological drought of a river basin. To summarize, the results obtained from first objective shows that our proposed modeling framework can be applied to investigate spatio-temporal pattern of blue and green water footprints as well as water security at a county scale for SRB, thereby locating the emerging hot spots within the river basin. The results obtained from second objective indicate that the land use change and climate variability have a key influence (either concomitant or independent) in altering the blue (green) water and related water security over the basin. The results based on third objective demonstrate that in addition to climate variables, catchment and morphological properties significantly control short, medium and long-term duration of hydrological droughts in SRB. An integrated modeling framework was developed to achieve these objectives and additional findings are explained in detail through the following chapters