[PDF] The Use Of Satellite Derived Data And Neural Network Analysis To Examine Variation In Maize Yield Under Changing Climate eBook

Author : Adisa Omolola Mayowa
Publisher :
Page : 0 pages
File Size : 43,53 MB
Release : 2019
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ISBN :

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Climate change and variability is foreseen to have direct and indirect effects on the existing agricultural production systems potentially threatening local, regional and/or global food security depending on the spatial scale of the change. The trend and level of impact caused by climate change and/or variability is region dependent and adaptive capacity. Climate change is projected to have more adverse impact in high vulnerability areas of sub-Saharan Africa. This study aimed to examine the variation in maize yield and develop a framework for predicting maize yield in response to climate change. To achieve this aim, this study has analyzed the impact of agro-climatic parameters on maize production across the major four maize producing provinces of South Africa. This study went further to investigate changes in the satellite derived phenological parameters and its relationship with maize production. In addition, the influence of drought (a derivative of climate change) on maize production was investigated. The study concluded by integrating all datasets used in each objective to develop an empirical predicting model using artificial neural network. Previous studies have quantified the impact of climatic variables on maize and at a small geographic area. Attempts to predict maize yield have been minimal and the use of artificial intelligence such as the artificial neural network has not been conducted. In this study, alternative sources of climatic and environmental data have been employed using remotely sensed data which offers possibilities of collecting continuous data over a large area (including remote areas) through the use of satellite. The analysis of agro-climatic variables (precipitation, potential evapotranspiration, minimum and maximum temperatures) spanning a period of 19860́32015, over the North West, Free State, Mpumalanga and KwaZulu-Natal (KZN) provinces, indicated that there is a negative trend in precipitation for North West and Free State provinces and positive trend in maximum temperature for all the provinces over the study period. Further more, the result showed that one or more different agro-climatic variables has more influence on maize across the provinces. Analysis of the phenological parameters of maize indicated that climate change and climate variability affect plant phenology largely during the vegetative and reproductive stages. NDVI values exhibited a decreasing trend across the maize producing provinces of South Africa. The results further demonstrate that the influences of climate variables on phenological parameters exhibit a strong space-time and common covariate dependence. Agro-climatic variables can predict about 46% of the variability of phenology indicators and about 63% of the variability of yield indicators for the entire study area. The study also illustrated the spatial patterns of drought depicting drought severity, frequency, and intensity which has the potential to influence crop yield. The study found that maize yield is most sensitive to 3-month timescale coinciding with maize growing season (r = 0.59; p

Author : Ibrahim Sirkeci
Publisher : Transnational Press London
Page : 289 pages
File Size : 12,93 MB
Release : 2020-11-13
Category : Social Science
ISBN : 1912997886

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This is the first volume of the Proceedings of The Migration Conference 2020. The Migration Conference 2020 was held online due to COVID-19 Pandemic and yet, in over 80 parallel sessions and plenaries key migration debates saw nearly 500 experts from around the world engaging. This collection contains contributions mainly dealing with migration and integration debates. These are only a subset of all presentations from authors who chose to submit full short papers for publication after the conference. Most of the contributions are work in progress and unedited versions. The next migration conference is going to be hosted by Ming-Ai Institute in London, UK. Looking forward to continuing the debates on human mobility after the Pandemic. | www.migrationconference.net | @migrationevent | fb.me/MigrationConference | Email: [email protected]

Author : Ibrahim Sirkeci
Publisher : Transnational Press London
Page : 257 pages
File Size : 11,7 MB
Release : 2020-11-13
Category : Political Science
ISBN : 1912997894

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This is the second volume of the Proceedings of The Migration Conference 2020. The Migration Conference 2020 was held online due to COVID-19 Pandemic and yet, in over 80 parallel sessions and plenaries key migration debates saw nearly 500 experts from around the world engaging. This collection contains contributions mainly dealing with migration and integration debates. These are only a subset of all presentations from authors who chose to submit full short papers for publication after the conference. Most of the contributions are work in progress and unedited versions. The next migration conference is going to be hosted by Ming-Ai Institute in London, UK. Looking forward to continuing the debates on human mobility after the Pandemic. | www.migrationconference.net | @migrationevent | fb.me/MigrationConference | Email: [email protected]

Author : Lwandle Mqadi
Publisher :
Page : pages
File Size : 36,32 MB
Release : 2013
Category :
ISBN :

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Maize production accounts for about 40% of the entire area cultivated in South Africa and is highly sensitive to climate variability. Maize is thus conservatively a staple food for more than 70% of the South African population whilst the maize industry stimulates the economy directly by providing secondary industries with over a billion worth of business each year. This study used the production function approach to evaluate likely impacts of climate change on maize production in South Africa. Data for this study have been obtained from experimental research sites in the 19 main maize producing regions in South Africa. The estimated coefficients of the production function model were used to derive measures of elasticity and optimal climate damage points as well as to simulate partial and total impacts of changes in levels of climate variables on maize yield. The Inter-Governmental Panel on Climate Change (IPCC) benchmark predictions of global warming for Southern Africa indicates that with the doubling of carbon dioxide in the atmosphere, a hotter and drier climate for the western semi-arid regions of Southern Africa and a hotter and slightly wetter climate for the eastern sub-tropical regions of Southern Africa are anticipated. Results indicated that rainfall and net solar radiation diffused within the maize crop have a non-linear and significant impact on average maize yield. Solar radiation rather than temperature was included in the regression analysis as temperature measures did not perform well. The results illustrated that increasing rainfall levels in all three main growth stages (sowing to emergence, juvenile to tassel initiation, and tassel initiation to grain filling growth stages) would increase maize yields whilst increases in solar radiation particularly during tassel initiation to grain filling would decrease maize yield. These results suggest that farmers could adopt a number of adaptation options including manipulation of planting dates, introduction of heat tolerant maize varieties and other options to mitigate the negative impacts of highlighted increases in solar radiation levels. Results also showed that for the semi-dry regions of South Africa, early growth stages of the maize crop would be mostly affected by decreases in rainfall whilst for the wet eastern regions the forecasted drier conditions would affect mostly the late maize growth stages. To capture the cumulative impact of increasing solar radiation and rainfall amounts marginally across all growth stages, a climate simulation analysis whereby the two main IPCC warming scenarios predicted for the Southern Africa region were used. In the partial effects analysis rainfall and solar radiation changes were simulated separately for each growth stage at a time, whereas in the total effects analysis rainfall and solar radiation changes were simulated simultaneously across all growth stages. Results of these analyses suggest that the west semi-dry regions of South Africa might benefit from the forecasted decreases in both rainfall and solar radiation, especially if sensitivity of the maize crop during its second growth stage is mitigated through the introduction of irrigation. This study also illustrated that maize production in the wet east regions might benefit in all its three growth stages from the forecasted increases in rainfall and solar radiation, especially if sensitivity of the first growth stage is reduced through the possible shifting of planting dates to mitigate the effects of increased rainfall forecasted for this region. One should note however, that the maize crop has the ability to agronomically adapt easily to drier conditions. Other attributes which further assists the resistance of the maize crop to climate changes, include extensive conservation soil tillage farming practices which could be applied to optimise soil infiltration rates whilst minimising evaporation rates, thus reducing soil erosion. The above results highlight the need for investments in improving the adaptive capacity of farmers, especially small-scale farmers who are severely restricted by their heavy reliance on natural climate factors and at the same time lack complementary inputs and institutional support systems. The existence of institutional support systems may assist farmers in further understanding anticipated climate changes and available conservation agricultural practices e.g. cost effective irrigation control systems. Other adaptation options include improved capacity of all the stakeholders involved in maize production (farmers, processors, marketers, exporters etc.) to better the ability to cope with the adversities of climate change through the use of farm planning, available crop insurance systems with regards to floods and droughts, improved weather and climate monitoring and forecasting. At a regional scale, extensive agricultural planning and risk reduction programmes may assist with spreading losses over larger regional areas, which may serve to reduce overall risk to growers. One important limitation of this study was that the analyses focused on the experimental sites only and hence did not consider all maize production areas across the country (which includes sites under small-scale farming). Also, the model adopted for this study also did not include the effects of carbon dioxide fertilisation and price movements, which are crucial. In conclusion, then, there is an urgent need for the South African National Department of Agriculture to look at how maize farmers (and especially small-scale farmers) could be assisted in adapting their traditional cropping methods to the forecasted changes in climate, whilst taking into consideration all the options presented above.

Author : Ratikanta Maiti
Publisher : CRC Press
Page : 292 pages
File Size : 41,75 MB
Release : 2021-08-30
Category : Science
ISBN : 1000210561

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This new volume offers a multi-pronged perspective on maize science, bringing together important recent research advances from several disciplines. The volume covers maize from origin to biotechnology. It provides an overview of recent world maize production along with technological advancements and green strategies in maize science. The authors cover the background of maize, its origin and domestication, ideotypes, botany, taxonomy, physiology of crop growth, methods of cultivation, production, nutritional functions, biotic and abiotic stress impacts, postharvest management and technology, maize grain quality, and advances in breeding and biotechnology, filling a gap in the literature of maize.

Author : Michael P. Keane
Publisher :
Page : 0 pages
File Size : 22,11 MB
Release : 2020
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Predicting the impact of climate change on crop yield is difficult, in part because the production function mapping weather to yield is high dimensional and nonlinear. We compare three approaches to predicting yields: (i) deep neural networks (DNNs), (ii) traditional panel-data models, and (iii) a new panel-data model that allows for unit and time fixed-effects in both intercepts and slopes in the agricultural production function - made feasible by a new estimator developed by Keane and Neal (2020) called MO-OLS. Using U.S. county-level corn yield data from 1950-2015, we show that both DNNs and MO-OLS models outperform traditional panel data models for predicting yield, both in-sample and in a Monte Carlo cross-validation exercise. However, the MO-OLS model substantially outperforms both DNNs and traditional panel-data models in forecasting yield in a 2006-15 holdout sample. We compare predictions of all these models for climate change impacts on yields from 2016 to 2100.

Author : Christopher Alfons Seifert
Publisher :
Page : pages
File Size : 39,71 MB
Release : 2018
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As our species moves deeper into an era in which we have an increasing influence over the climate and health of our planet, it is important to examine the likely effects of our activities as well as the tools we can use to adapt to coming changes. Occupying more land area than any other human activity and employing biological systems vulnerable to extreme heat, agriculture is chief amongst vital industries impacted by a changing climate. Previous work has focused on those impacts, finding potentially drastic effects for countries like the United States, the world's largest producer of maize and soybean, whose major production regions are fortuitously positioned near a climate optimum for those key crops. This dissertation examines various specific practices that could be deployed to build resilience and prevent the degradation of the U.S. agricultural system under potential 21st century climate regimes. Double cropping, crop rotation, cover cropping, and irrigation all have their place as potential adaptations. This work uses mechanistic and statistical models as well as newly available datasets and data processing methodologies to explore the expansion of suitability, the spatial variability, the yield effects, and the temporal trends in adoption of these practices respectively. Chapter 1 runs mechanistic phenological models for winter wheat and soybean under recent and future climate scenarios, finding that even small increases in expected temperature and growing season length can lead to large increases in double crop suitability. These changes in suitability have already been occurring over the last few decades and appear poised to accelerate along with our changing climate. While the increase the area suitable for this cropping practice is large, especially later in this century, the implied increase in agricultural production that accompanies it is substantially smaller than potential yield losses. Building on the first chapter but exploring inter-yearly crop rotation patterns versus intra-yearly patterns, Chapter 2 uses a large dataset of field-level yields to examine the yield penalties seen in continuous maize and soybean fields. Yield loss from continuous cropping found in the model was broadly consistent with findings from field trials. Additionally, the spatial breadth and temporal depth of the dataset enabled us to find that areas with large negative yield anomalies see worse yield penalties for continuous cropping, as do soybean crops grown in areas or years with low early season vapor pressure deficit and maize crops grown in areas or years with low early or late minimum temperatures. Chapter 3 examines another promising crop configuration with potential to serve as a climate adaptation; cover crops. In it, we build a cover crop classifier based on remotely sensed data and cross the classifier's output with already existing soil quality as well as maize and soybean yield maps. The raw classifier output shows that, as intended, cover crops are more likely to be found on poorer soils in the Midwest. Contrary to other sources, however, yield benefits for adopters of the practice are quite mild, even after a number of years following the practice. Combining this conclusion with the currently high cost of cover crop adoption, continued expansion of government funding for cover cropping appears necessary to propagate the practice. Chapter 4 uses methods built in Chapter 3, but with a different aim in mind -- mapping irrigation and its adoption in two key states in the western U.S. maize-soybean belt. Here we find that irrigation has indeed been on the increase over the last decade and a half in Nebraska, though no definitive trends were seen in Iowa. The increase in Nebraska does not appear to be driven by changes in the difference between irrigated and dryland yields, and irrigation adoption was more likely to be undertaken on higher quality land from 2003-2017 versus earlier in the practice's history.

Author : Christian Brünner
Publisher : Springer
Page : 288 pages
File Size : 38,94 MB
Release : 2018-09-11
Category : Law
ISBN : 331974805X

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The book focuses on the topic of trends and challenges with regards to satellite-based earth observation. Contributors include legal experts in the field and representatives from institutions such as the European Space Agency, the European Space Policy Institute, academia and the private sector.

Author : Denis Rugege
Publisher :
Page : 134 pages
File Size : 13,63 MB
Release : 2002
Category : Corn
ISBN :

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Repeated measurements during the crop cycle allow monitoring of the sufficiency of actual management practices. Introducing estimated or forecast weather data in crop growth calculations for the remainder of the crop cycle permits to make repeated estimates of anticipated crop production and to timely signal a need for remedial action.

Author : Daniel Woodford Urban
Publisher :
Page : pages
File Size : 35,23 MB
Release : 2015
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Sustaining crop yield increases to meet the world's rapidly growing food demand will be a vital challenge of the 21st century, and one made more difficult by rising temperatures and more frequent weather extremes related to anthropogenic global warming. While much scientific research has focused on links between climate change and mean yield impacts, far less has addressed the likeliest consequences for interannual yield variability, a key driver of global price volatility and food security. As the supplier of nearly 40% of the world's maize and soybean, the United States is a critical component of this picture, and its extensive historical yield and weather data make it exceptionally well suited to empirically study climatic effects on yields. Centered on the U.S. and especially the Corn Belt, this dissertation investigates the mean and variability impacts of projected average temperature and precipitation changes, extreme precipitation during the planting season, modulation of heat stress by moisture level, and improved transpiration efficiency through elevated CO2. In all of these aspects, this dissertation seeks not only to quantify effects where the existing literature is sparse, but also to improve the skill and sophistication of statistical approaches to these questions. Chapter 1 applies a statistical model to U.S. county-level maize yields since 1950 based on growing season average temperature (T) and precipitation (P). While these seasonal predictors mask monthly and daily weather variability, they nonetheless explain a large amount of variance. The model assumes a quadratic yield response to T and P, and this nonlinearity implies an increase in yield variability for increases in either the mean or variance of either weather variable. To assess the potential impact of climate change in this region, we predict yields under a range of future projected T and P values from a suite of climate models, and find remarkably strong model agreement toward yield mean decrease and variability increase. Chapter 2 examines the effects of heavy precipitation resulting in excess soil moisture during the planting season. Starting with a model similar to that in Chapter 1, it utilizes extreme precipitation and hydrologic model-derived soil moisture indices constructed from daily time series. In so doing, it extends the work of Chapter 1 by examining weather-related variation outside of the growing season, and in capturing variation related to daily-scale events that may not necessarily correlate strongly with seasonal averages. While the county-level impacts of a moderately wet year are small, counties' wettest years can account for 6-8% yield loss, or roughly the equivalent of a one standard deviation temperature increase. Chapter 3 likewise deepens and extends the insights from Chapter 1, by using more targeted measures of evaporative demand and soil moisture supply than seasonal T and P averages. A more detailed dataset allows for vapor pressure deficit (VPD) and precipitation measures in the critical period of 61 to 90 days after planting, and we find that the yield response to high VPD is indeed significantly ameliorated when moisture levels are high and exacerbated when low. We then examine the potential of CO2 to reduce evaporative demand through improved transpiration efficiency, and quantify the yield mean and variability implications under both a high and low emissions scenario. We find that while the demand-reducing effect of CO2 significantly improves mean yields and reduces variability, the damage due to exceptionally high demand in the high-emissions scenario outweighs its larger CO2 benefit, such that the low-emission scenario is clearly preferable by the end of the 21st century.