Modelling the soil water balance and applications using a decision support system (DSSAT v3.5).

Thesis (M.Sc.)-University of Natal, Pietermaritzburg, 2003.Water is a scarce resource used by various stakeholders. Agriculture is one of the users of this resource especially for growing plants. Plants need to take up carbon dioxide to prepare their own food. For this purpose plants have stomatal openings. These same openings are used for transpiration. Quantifying transpiration is important for efficient water resource management and crop production because it is closely related to dry matter production. Transpiration could be measured using a number of methods or calculated indirectly through quantification of the soil water balance components using environmental instruments. The use of models such as the Decision Support System for Agrotechnology Transfer (DSSAT v3.5) is, however, much easier than environmental instruments. Nowadays, with increased capabilities of computers, the use of crop simulation modelling has become a common practice for various applications. But it is important that models, such as DSSAT v3.5, be calibrated and verified before being used for various applications such as long-term risk assessment, evaluation of cultural practices and other applications. In this study the model inputs have been collected first Then the model was calibrated and verified. Next sensitivitY analysis was carried to observe the model behavior to changes in inputs. Finally the model has been applied for long-term risk assessment and evaluation of cultural practices. In this study, the data collected formed the basis forthe minimum dataset needed for running the DSSAT v3.5 model. In addition, the factory given transmission of shading material over a tomato crop was compared to actual measurements. Missing weather data (solar irradiance, minimum and maximum air temperature and rainfall) were completed after checking that it was homogeneous to measurements from nearby automatic weather station. It was found that factory-given transmission value of 0.7 of the shade cloth was different from the actual one of 0.765. So this value was used for conversion of solar irradiance measured outside the shade cloth to solar irradiance inside the shade cloth. Conventional laboratory procedures were used for the analysis of soil physical and chemical properties. Soil water content limits were determined using texture and bulk density regression based equations. Other model inputs were calculated using the DSSAT model. Crop management inputs were also documented for creation of the experimental details file. The DSSATv3.5 soil water balance model was calibrated for soil, plant and weather conditions at Ukulinga by modifying some of its inputs and then simulations of the soil water balance components were evaluated against actual measurements. For this purpose half of the data available was used for calibration and the other half for verification. Model simulations of soil water content (150 to 300 mm and 450 to 600 mm) improved significantly after calibration. In addition, simulations of leaf area index (LA!) were satisfactory. Simulated evapotranspiration (E1) had certain deviations from the measured ET because the latter calculated ET by multiplying the potential ET with constant crop multiplier so-called the crop coefficient. Sensitivity analysis and long-term risk assessments for yield, runoff and drainage and other model outputs were carried out for soil, plant and weather conditions at Ukulinga. For this purpose, some of the input parameters were varied individually to determine the effect on seven model output parameters. In addition, long-term weather data was used to simulate yield, biomass at harvest, runoff and drainage for various initial soil water content values. The sensitivity analysis gave results that conform to the current understanding of the soil-plant atmosphere system. The long-term assessment showed that it is risky to grow tomatoes during the winter season at Ukulinga irrespective of the initial soil water content unless certain measures are taken such as the use of mulching to protect the plants from frost. The CROPGRO-Soya bean model was used to evaluate the soil water balance and gro'W1:h routines for soil, plant and weather conditions at Cedara. In addition, cultural practices such as row spacing, seeding rate and cultivars were also evaluated using longterm weather data. Simulations of soil water content were unsatisfactory even after calibration of some of the model parameters. Other model parameters such as LAI, yield and flowering date had satisfactory agreement with observed values. Results from this study suggest that the model is sensitive to weather and cultural practices such as seeding rates, row spacing and cultivar maturity groups. The general use of decision support systems is limited by various factors. Some of the factors are: unclear definition of clients/end users; no end user input prior to or during the development of the DSS; DSS does not solve the problems that the client is experiencing; DSS do not match their decision-making style; producers see no reason to change the current management practices; DSS does not provide benefit over current decision-making system; limited computer ownership amongst producers; lack of field testing; producers do not trust the output due to the lack of understanding of the underlying theories of the models utilized; cannot access the necessary data inputs; lack of technical support; lack of training in the development ofDSS software; marketing and support constraints; institutional resistances; short shelf-life of DSS software; technical constraints, user constraints and other constraints. For successful use of DSS, the abovementioned constraints have to be solved before their useful impacts on farming systems could be realized. This study has shown that the DSSAT v3.5 model simulations of the soil water balance components such as evapotranspiration and soil water content were unsatisfactory while simulations of plant parameters such as leaf area index, yield and phonological stages were simulate to a satisfactory standard. Sensitivity analysis gave results that conform to the current understanding of the soil-plant -atmosphere system. Model outputs such as yield and phonological stages were found to sensitive to weather and cultural practices such as seeding rates, row spacing and cultivar maturity groups. It ha been further investigated that the model could be used for risk assessment in various crop management practices and evaluation of cultural practices. However, before farmers can use DSSAT v3.5, several constraints have to be solved

Land disputes between villages in the highland of Eritrea : the case of Guaquat and Geddele villages.

Thesis (M.A.)-University of KwaZulu-Natal, Durban, 2004.This thesis is an examination into the problem of land disputes between villages in the highland (kebessa) area of Eritrea. Through a case study of the dispute between the villages of Guaquat and Goddele, which are located in the district of Mereta Keih, Southern Zone, this study explores the causes, nature and consequences of land disputes and the mechanisms by which they are settled. It interprets the land dispute by placing it within its historical, social, and political contexts and in the land tenure systems in the area, establishing the complex nature of the case study in particular and land disputes in the highland in general. In this area of the country, where the society is made up of settled peasant cultivators, the village is the basic land owning-community in which land is communally owned. For almost all of rural Eritreans land remains the sole means of subsistence, hence the means of life. Yet, over the decades, because of high population density land resource became extremely scarce. As a result land became a source of competition and struggle for existence. It is a kind of property that must be jealously defended. While scarcity of land is the underlying cause of land disputes, other immediate causal factors have been identified, which result from tenural arrangements, unclear boundaries between villages, trespassing, etc. The disputes manifest themselves through endless litigation processes and with clashes between disputant villages. The long-established permanent village settlements, which go back for centuries, created a strong and inextricable link between land and communities. Land is, thus, a source of dignity and identity. Over the years this strong link between land and society intensified people's attachment to land, which in tum resulted in the development of significant social and cultural value to land. All these factors added fuel to the struggle for the vital resource of land. The study also shows that the new land proclamation, which puts land in the hands of the state , cannot eliminate land disputes between communities to the extent is expected, but, rather adds another dimension to the problem of land disputes

Scene complexity modulates degree of feedback activity during object detection in natural scenes

Selective brain responses to objects arise within a few hundreds of milliseconds of neural processing, suggesting that visual object recognition is mediated by rapid feed-forward activations. Yet disruption of neural responses in early visual cortex beyond feed-forward processing stages affects object recognition performance. Here, we unite these discrepant findings by reporting that object recognition involves enhanced feedback activity (recurrent processing within early visual cortex) when target objects are embedded in natural scenes that are characterized by high complexity. Human participants performed an animal target detection task on natural scenes with low, medium or high complexity as determined by a computational model of low-level contrast statistics. Three converging lines of evidence indicate that feedback was selectively enhanced for high complexity scenes. First, functional magnetic resonance imaging (fMRI) activity in early visual cortex (V1) was enhanced for target objects in scenes with high, but not low or medium complexity. Second, event-related potentials (ERPs) evoked by target objects were selectively enhanced at feedback stages of visual processing (from ~220 ms onwards) for high complexity scenes only. Third, behavioral performance for high complexity scenes deteriorated when participants were pressed for time and thus less able to incorporate the feedback activity. Modeling of the reaction time distributions using drift diffusion revealed that object information accumulated more slowly for high complexity scenes, with evidence accumulation being coupled to trial-to-trial variation in the EEG feedback response. Together, these results suggest that while feed-forward activity may suffice to recognize isolated objects, the brain employs recurrent processing more adaptively in naturalistic settings, using minimal feedback for simple scenes and increasing feedback for complex scenes

A supervised clustering approach for fMRI-based inference of brain states

We propose a method that combines signals from many brain regions observed in functional Magnetic Resonance Imaging (fMRI) to predict the subject's behavior during a scanning session. Such predictions suffer from the huge number of brain regions sampled on the voxel grid of standard fMRI data sets: the curse of dimensionality. Dimensionality reduction is thus needed, but it is often performed using a univariate feature selection procedure, that handles neither the spatial structure of the images, nor the multivariate nature of the signal. By introducing a hierarchical clustering of the brain volume that incorporates connectivity constraints, we reduce the span of the possible spatial configurations to a single tree of nested regions tailored to the signal. We then prune the tree in a supervised setting, hence the name supervised clustering, in order to extract a parcellation (division of the volume) such that parcel-based signal averages best predict the target information. Dimensionality reduction is thus achieved by feature agglomeration, and the constructed features now provide a multi-scale representation of the signal. Comparisons with reference methods on both simulated and real data show that our approach yields higher prediction accuracy than standard voxel-based approaches. Moreover, the method infers an explicit weighting of the regions involved in the regression or classification task