Ph.D. (Geography) / Skillful management and planning of the earth's natural resources and of agricultural production necessitates a great deal of Information regarding the resources and relevant soclo-economlc factors, as well as better Information on crop yield expectations throughout the year. These Intricate processes can often be simplified Into models. Most of Nature's systems (for example climatic systems) are, however, too complex to allow realistic models to be evaluated numerically and are therefore examined by means of simulation models through which the Interaction with time between physical processes Is established. The purpose Is to understand these processes In order to forecast the results of these changes and Interactions. The aim of this study Is to construct a composite climate model that (1) computes missing climate values, and (2) extrapolates climate values until the expected date of harvesting, by simulation using the random sampling of values from reference ("look-up") tables, In order that (3) the climate files, with simulated climate values, could be used with the parameter files as Input files for the CERES-Maize model. The CERES-Maize model uses dally values to simulate the growth, development and yield of the maize plant. The respective crop forecasting results obtained for actual and simulated climate values are then to be evaluated. Climate files, with four variables, were obtained on magnetic computer tape from the South African Weather Bureau for the study area In the Eastern Transvaal. The preliminary processing was done by the use of SA5-programmes and these files were then exported from the mainframe computer to a personal computer and stored on floppy disks. Climate reference flies were compiled from the original climate flies by sorting the climate data according to the Julian date. The missing values In the climate reference flies and the original climate files were restored from the files of neighbouring weather stations, as calculated orestimated values by.means of a suitable method of computation. Some of the methods used, were derived after comparing the graphs of the time-series of a number of climate files. Aclimate simulation model was compiled In which climatic elements were simulated by sampling values a set number of times randomly from the climate reference files. The mean of these sampled values were adjusted by multiplying It with a factor representing the climatic change over time. A climate file, also containing simulated values, and a theoretical parameter Input file were then used as the Input flies for a revised edition of the CERES-Maize model. A comparison of the results obtained for the 1986/87 growing season when the climate files, with actual and simulated values respectively, were used as Inputs for the CERES-Maize model, Indicated very promising results. The values predicted for two climate flies (1962-1987) differed by about 18%, whereas a difference of only about 8% between those predicted for two smaller climate files (actual and simulated values respectively), representing only the 1986/87 season, was recorded. The difference between values predicted for the climate file, mentioned last, and consisting only of simulated climate values, and those forecasted for the original and complete climate data file, was only 5%. As Indicated by the arithmetic mean, there is again a tendency towards the mean values.

No description available.

The purpose of this study was to assess the present land resource surveying methods and techniques used in Eritrea, to evaluate different methods and techniques of land resource surveying which are currently in use in various parts of the world, to design improved methods and techniques of land resource surveying for Eritrea and to indicate the importance of cost-effective ways of land resource surveying in achieving optimal land use. A literature survey of methods and techniques of soil, rangeland, and agro-climatic survey was done in-depth. An analysis was conducted on the present resource surveying methodologies and techniques used in Eritrea. International publications on land resource surveying methods and techniques were studies and evaluation of their appropriateness for Eritrea was conducted. Finally an appropriate and affordable set of land resource surveying methodologies and techniques are proposed for Eritrea. The main conclusion of the study is to adapt international methods and techniques of resource surveying which are appropriate under the country’s socio-economic and technical conditions. Developing local methods and techniques under present condition is not possible due to various reasons. / Dissertation (M Inst Agrar (Land-use Planning))--University of Pretoria, 2007. / Agricultural Economics, Extension and Rural Development / unrestricted

Variability is inherent to any living system, and adaptation, or changing one's behavior in response to variability, is an important way to reduce or eliminate possible adverse consequences of change. Adaptation is particularly important to consider in the face of contemporary climate change, as individuals and communities may be able to adapt their behavior in response to weather variability and reduce or possibly eliminate predicted adverse impacts. To gain a more mechanistic understanding of which factors may lead to enhanced adaptive capacity of individuals and communities to future change, this dissertation uses a multi-disciplinary and multi-scale approach to broadly examine which social, economic, biophysical, and perceptional factors are associated with agricultural adaptation to current weather variability. The results from this dissertation generally show how adapting agricultural practices, like changing cropping patterns or increasing irrigation, can reduce the vulnerability of farmers to weather variability. Importantly, however, we show that adaptation is not simply about adopting appropriate technical solutions like sowing weather-appropriate crops or irrigating optimally, it is also about the complex set of economic, social, and perceptional factors that influence farmer decision-making and adaptive capacity. A global literature review highlights important biases and gaps in our current knowledge about climate change adaptation research in the agricultural sector. Based on these findings, we offer recommendations for future research that may result in a more process-based understanding of adaptation, including conducting multi-disciplinary studies that simultaneously consider the social, economic, biophysical, and perceptional factors that are associated with adaptation, and understanding how weather variability and change influence well-being to more accurately identify which individuals, households, or communities are best able to adapt. Using these recommendations, we design a case study that examines how farmers alter their cropping strategies in response to monsoon variability in Gujarat, India. Much of our research is focused on India given that over 50% of the nation practices smallholder agriculture and is particularly sensitive to climate variability and change. Through this work, we find that farmers altered their cropping decisions in response to a delayed monsoon onset, by increasing irrigation, switching crop type, and/or delaying crop sowing, and these strategies, particularly increasing irrigation, were adaptive considering yield and profit in the year of our study. These results highlight the importance of considering farmer behavior and decision-making in models that estimate future weather and climate impacts on agricultural production. While household-level surveys allow one to assess individual-level decision-making, they are difficult to implement over large spatial and temporal scales. Thus we develop a remote sensing algorithm that quantifies cropped area of smallholder farms over large spatial and temporal scales using readily-available MODIS imagery. Given the importance of irrigation as an adaptation strategy, we link these cropped area maps with rainfall and irrigation data at the village scale across all of India to assess the relative impact of different types of irrigation (e.g. groundwater versus canal) on winter cropped area and its sensitivity to rainfall variability. Overall, we find that deep well irrigation is both associated with the greatest amount of winter cropped area, and is also the least sensitive to monsoon and winter rainfall variability. However, the relative benefit of deep well irrigation varies across India, with the largest benefits seen in the regions that are facing the greatest levels of groundwater depletion. This work highlights the critical importance of groundwater for agriculture in India, and suggests that future work should identify ways to use groundwater more efficiently, increase the recharge rate of groundwater, or improve the performance of canal irrigation in order to maintain similar levels of production in the face of climate variability and change over the upcoming decades. While this dissertation focuses on agricultural adaptation to weather variability, the methods and implications derived from this dissertation are applicable more broadly to the study of resilience and adaptive capacity of social-ecological systems to global environmental change. In a rapidly changing global system, using a multi-disciplinary, multi-scale, and coupled systems approach similar to the one employed in this dissertation will help better understand and identify possible ways to enhance the ability of societies to adapt to global environmental change.

The El Niño – Southern Oscillation (ENSO) has far-reaching impacts across the globe and provides the most reliable source of seasonal to interannual climate prediction over North America. Though numerous studies have discussed the impacts of ENSO teleconnections on North America during boreal winter, it is becoming more and more apparent that the regional impacts of ENSO teleconnections are highly sensitive to the seasonal evolution of ENSO events. Also, the significant impacts of ENSO are not limited to the boreal winter seasons. To address these knowledge gaps, this thesis examines the seasonal dependence of ENSO teleconnections and impacts on North American surface climate, focusing on two examples. Chapter 1 examines the relationship between El Niño – California winter precipitation. Results show that the probability of the anomalous statewide-wetness increases as El Niño intensity increases. Also, the influences of El Niño on California winter precipitation are statistically significant in late winter (Feb-Apr), but not in early winter even though that is when El Niño usually reaches its peak intensity. Chapter 2 further investigates why the strong 2015/16 El Niño failed to bring above normal winter precipitation to California, focusing on the role of westward shifted equatorial Pacific sea surface temperature anomalies (SSTAs) based on two reasons: the maximum equatorial Pacific SSTAs was located westward during the 2015/16 winter compared to those during the 1982/83 and 1997/98 winters, both of which brought extremely wet late winters to California. Also, the North American Multi-Model Ensemble (NMME) forecasts overestimated the eastern tropical Pacific SSTAs and California precipitation in the 2015/16 late winter, compared to observations. The Atmospheric General Circulation Model (AGCM) experiments suggested that the SST forecast error in NMME contributed partially to the wet bias in California precipitation forecast in the 2015/16 late winter. However, the atmospheric internal variability could have also played a large role in the dry California winter during the event. ENSO also exerts significant impacts on agricultural production over the Midwest during boreal summer. Chapter 3 examines the physical processes of the ENSO summer teleconnection, focusing on the summer when a La Niña is either transitioning from an earlier El Niño winter or persisting from an existing La Niña winter. The results demonstrate that the impacts are most significant during the summer when El Niño is transitioning to La Niña compared to that when La Niña is persisting, even though both can loosely be defined as developing La Niña summer. During the transitioning summer, both the decaying El Niño and the developing La Niña induce suppressed deep convection over the tropical Pacific and thereby the corresponding Rossby wave propagations toward North America, resulting in a statistically significant anomalous anticyclone over northeastern North America and, therefore, a robust warming signal over the Midwest. These features are unique to the developing La Niña transitioning from El Niño, but not the persistent La Niña. In Chapter 4, we further evaluate the performance of NCAR CAM5 forced with historical SSTA in terms of the La Niña summer teleconnections. Though the model ensemble mean well reproduces the features in the preceding El Niño/La Niña winters, the model ensemble mean has very limited skill in simulating the tropical convection and extratropical teleconnections during both the transitioning and persisting summers. The weak responses in the model ensemble mean are attributed to large variability in both the tropical precipitation, especially over the western Pacific, and atmospheric circulation during summer season. This thesis synthesizes the physical processes and assessments of climate models in different seasons to establish the sensitivity of regional climate to the seasonal dependence of ENSO teleconnections. We demonstrate that the strongest impacts of ENSO on North American regional climate might not be necessarily simultaneous with maximum tropical Pacific SST anomalies. We also emphasize the importance of the multi-year ENSO evolutions when addressing the seasonal impacts on North American summertime climate. The findings in this thesis could benefit the improvement of seasonal hydroclimate forecasting skills in the future.

"March 2003" / Bibliography: leaves 195-209. / Systems requirements: IBM PC or compatible; CD-ROM drive. / x, 209, A4 leaves : ill. (some col.) ; 30 cm. + 1 CD-ROM (4 3/4 in.) / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Thesis (Ph.D.)--University of Adelaide, Dept. of Geographical and Environmental Studies, 2003

published_or_final_version / Applied Geosciences / Master / Master of Science

Agricultural scientists are faced with the challenge of producing enough food for the increasing world population. Hence the need to develop tools for managing soil and plant systems to increase food production in order to meet the world food demand in the future. Crop simulation models have become promising tools in predicting yield and related components fi'om a set of weather, soil, plant and management data inputs. This study describes the estimation of solar radiant density, a crucial input in crop simulation models; calibration and validation of a soil-plant growth simulator, CropSyst, for management purposes; and generation of weather data for assessment of crop production under possible climate changes in the future. Daily solar radiant density, an input required by most crop simulation models, is infiequently observed in many stations. This may prevent application of crop simulation models for specific locations. Long-term data records of daily minimum and maximum air temperatures, precipitation, sunshine hours and/or solar radiant density were obtained for Cedara, Durban, Seven Oaks and Ukulinga in KwaZulu-Natal, South Africa. Solar radiant density was estimated fi'om sunshine hours using the Angstrom equation and ten other models that involved daily minimum and maximum air temperatures and/or precipitation along with extratelTestrial radiant density. Coefficients for the Angstrom equation and one of the other ten models were specifically developed for South African conditions; the remaining models required fitting coefficients using the available data for all locations. The models were evaluated using (i) conventional statistics that involved, root mean square elTor (RMSE) along with its systematic and unsystematic components, slope, intercept, index of agreement (d), and coefficient of determination (R\ and (ii) a fuzzy expert system that involved a single modular indicator (Ira d) aggregated from the modules of accuracy (aggregation of the indices relative RMSE, model efficiency and I-student probability), con'elation (Pearson's correlation coefficient) and pattem (aggregation of pattem index vs day of year and pattem index vs minimum air temperature). For each index, two functions describing membership to the fuzzy subsets Favourable (F) and Unfavourable (V) were defined. The expelt system calculates the modules according to both the degree of membership and a set of decision rules. Solar radiant density estimated from sunshine hours for the Durban station resulted in R2 , RMSE (MJ m,2) and d index of 0.90, 2.32 and 0.97 respectively. In the absence of observed solar radiant density data, estimations from sunshine hours were used for derivation of coefficients as well as evaluation of the models. For Durban, the performance of the models was generally poor. For Cedara, Seven Oakes and Ukulinga two of the models resulted in a high d index and smallest systematic RMSE. The solar radiant density estimated from each model was also used as an input to simulate maize grain yields using the soil-plant growth simulator, CropSyst. The models were ranked according to their ability to simulate grain yields that match those obtained from using the observed solar radiant density. The rankings according to crop simulation, conventional statistics and expert system were compared. The CropSyst model was also evaluated for its ability to simulate crop water-use of fallow and cropped (oats, Italian ryegrass, rye and maize) plots at Cedara, KwaZulu-Natal, South Africa. Soil characteristics, initial soil water conditions, irrigation and weather data were inputted to CropSyst. Crop input parameters for oats, Italian ryegrass and rye were used, with little modifications, as determined from field experiments conducted at Kromdraai open cast mine, Mpumalanga province, South Africa. Crop input parameters for maIze were either determined fi'om field experiments or taken from CropSyst crop input parameters documentation and adjusted within a narrow specification range of values as dictated by CropSyst. The findings indicated that CropSyst was generally able to simulate reasonably well the water-use of fallow and cropped (oats, Italian ryegI°ass, rye and maize) plots; leaf area index and crop evapotranspiration of rye; and grain yield and developmental stages of maize. The validated CropSyst model was also used to simulate timing and amount of irrigation water, and investigate incipient water stress in oats, Italian ryegrass and rye. The CropSyst model was used to investigate potential effects of future climate changes on the productivity of maize grain yields at Cedara, KwaZulu-Natal, South Africa. The effect of planting date (local planting date, a fortnight earlier and a fortnight later) was also included in the study. A 30-year baseline weather data input series were generated by a stochastic weather generator, ClimGen, using 30 years of observed weather data (l971 to 2000). The generated weather data series was compared with the observed for its distributions of daily rainfall and wet and dry series, monthly total rainfall and its variances, daily and monthly mean and variance of precipitation, minimum and maximum air temperature, and solar radiant density. Four months of the year failed to reproduce distributions of wet and dry series, daily precipitation, and monthly variances of precipitation of the observed weather data series. In addition, Penman-Monteith reference evaporation (ETa) was calculated using the observed and generated data series. Cumulative probability function of ETa calculated using the generated weather data series followed the observed distribution well. Moreover, maize grain yields were simulated using the generated and observed weather data series with local, a fortnight earlier and a fortnight later planting dates. The mean simulated grain yields for the respective planting dates were not statistically different from each other; the grain yields simulated using the generated weather data had significantly smaller variance than the grain yields simulated using the observed weather data series. When the generated weather data series was used an input, the early planting date as compared to the locally practiced and late planting dates resulted in significantly greater simulated grain yields. The grain yields simulated using the observed weather data for the early and local planting dates were not statistically different from each other. The baseline period was modified by synthesized climate projections to create future climatic scenarios. The climate changes considered corresponded to doubling of [C02] from 350 to 700 ~t1 ,-I without air temperature and water regime changes, and doubling of [C02] accompanied by increases in mean air temperature and precipitation changes of 2 (lC and 10%, 2 (le and 20%>, 4 °c and 10%, and 4 (lC and 20% respectively. Solar radiant density was also estimated from daily air temperature range for all scenarios that involved change in mean air temperature. In addition, input crop parameters of radiation-use and biomass transpiration efficiencies were modified for maize, in CropSyst, to accommodate changes in elevated levels of [C02]. Equivalent doubling of [C02], without air temperature or water regime changes, resulted in increased simulated grain yields as compared to the baseline period. Adding 2 QC to the mean daily temperature and 10% to the daily precipitation of a [C02] elevated atmosphere reduced the grain yield but still kept it above the level of the baseline period grain yield. Adding 4 QC to the mean daily temperature and 10% to the daily precipitation fLllther decreased the yield. Increasing the daily precipitation by 20% instead of 10% did not change the simulated grain yield as compared to the 10% increments. Early planting date, for all scenarios, also resulted in higher yields, but the relative increment in grain yield was higher for the late planting dates with scenarios that involved increment in mean air temperature. In general, this study confi1l11ed that doubling of [C02] increases yield but the accompanied increase in mean air temperature reduces yield. / Thesis (M.Sc.)-University of Natal, Pietermaritzburg, 2003.

Climate change resulting from the augmented "greenhouse effect" is likely to have significant effects on the terrestrial hydrological system and the social and ecological systems linked to it. Climate change could potentially affect inputs to the agrohydrological system such as rainfall, temperature and potential evaporation; processes within the system such as vegetation dynamics and crop production; and hydrological responses such as runoff, recharge of soil water into the vadose zone and net irrigation demand. This study outlines the use of a daily water budget model, ACRU, and SCENGEN, a climate change scenario generator, to assess potential impacts of global climate change on agricultural production and hydrological responses in southern Africa. This study also considers potential impacts of climate change on plant response which may determine the extent of potential impacts of climate change on agricultural production and hydrological response. Two approaches to climate change impact studies are adopted for use in this study. The first, and more conventional approach considers the impact of a specified climate change scenario, in this case developed with the use of SCENGEN, on the terrestrial hydrological system. The second approach considers the degree of climate change, in this case precipitation change, required to perturb the hydrological system significantly in the various climate regimes found in southern Africa. A comparative analysis of the sensitivity of selected hydrological responses to climate change produced the following results, in ascending order of sensitivity: net irrigation demand < stormflow response < runoff < recharge into the vadose zone. The impacts of a specific climate scenario change on hydrological responses produced unexpected results. A general decrease in mean annual precipitation over southern Africa is predicted for the future with SCENGEN. However, widespread simulated increases in runoff, soil moisture content in the A- and B-horizon and recharge into the vadose zone are obtained. These increases are a product of the CO2 "fertilisation" feedback, which is incorporated as a maximum transpiration suppression routine, in the ACRU model. Net irrigation demand, which is not linked to this routine, is simulated to increase in the future. / Thesis (M.Env.Dev.)-University of Natal, Pietermaritzburg, 1997.

Abstract available in PDF file. / Thesis (M.Sc.Agric.)-University of KwaZulu-Natal, Pietermaritzburg, 2014.