EFFECT OF CLIMATE CHANGE ON ARABLE CROPS RICE AND MAIZE USING ARTIFICIAL NEURAL NETWORK

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EFFECT OF CLIMATE CHANGE ON ARABLE CROPS RICE AND MAIZE USING ARTIFICIAL NEURAL NETWORK

ABSTRACT

This study examines the effect of climate change on arable crops; rice and maize using artificial neural network. The study revealed that there is a relationship between climate change and the yields of rice and maize. Unfavorable climatic conditions or climate change impacted negatively on grain yields; if there is drought, excessive temperature, and low rainfall there will be a decline in yield resulting to a decline in productivity as well. Therefore, it was inferred that climate change has a great effect on arable crops yields in Nigeria and the world at large. On the premise of result obtained in this study, it is recommended that the government should understand that there is a great loss of crop yield as a result of climate change and should help the crop farmers with effective adaptation strategies such as providing irrigation facilities to cope with the challenges of inadequate rainfall. There is also a need for better policies and investments on infrastructure to facilitate technology adoption and adaptation. These include investments on irrigation, roads, storage facilities and improved access to markets. Extension agent should provide vital information on crop varieties with increased tolerance to climate change and other climate-smart agricultural practices that will enable farmers mitigate against climate change.

CHAPTER ONEINTRODUCTION 1.1              BACKGROUND OF THE STUDYAgriculture is an economic activity that produces food necessary for human livelihood and it is highly dependent upon weather and climate; however, agriculture is also likely to be very vulnerable to climate variability and change. Agriculture constitutes the principal livelihood for 70 percent of the poor throughout the world, many of the poor and hungry being smallholder farmers, herders, fishermen and forest-dwellers, as well as indigenous people living in climate sensitive and vulnerable areas (Feldstein, 2017).  Climate change is a widespread challenge affecting many parts of the world (Feldstein, 2017). This changes will not occur without marked impacts upon various sectors of our environment, and consequently of our society (Elum and Momodu, 2017). The changes in climate will appear and will have an important impact on land suitability and in particular, for rainfed crop production. Climate change has raised much concern regarding its impacts on future global agricultural production, varying by region, time, and socio-economic development path (Zoellick and Robert, 2018).Agriculture is extremely vulnerable to climate change and higher temperatures eventually reduce crop yields without discouraging weed, disease and pest challenges. Changes in precipitation patterns increase the likelihood of short-term crop failures and long-term declines in production (Ceccarelli, et al., 2020) Although there will be gains in some crops in some regions of the world, the overall impact of climate change on agriculture is expected to be negative, threatening global food security (Nelson et al., 2019). Food insecurity would probably increase under climate change, unless early warning systems and development programmes are used more effectively (Brown & Funk 2008). Currently, millions of hungry people subsist on what they produce. If climate change reduces production while populations increase, there is likely to be more hunger. Lobellet al. (2018) showed that increasing temperatures and declining precipitation over semi-arid regions are likely to reduce yields of maize, wheat, rice and other primary crops in the next two decades. Even though climate change is one of the major current global concerns, it is not new. Several climate changes have occurred before, with dramatic consequences. Among them is the decrease in CO₂ content, which took place 350 million years ago and which is considered to be responsible for the appearance of leaves, the first plants were leafless and it took 40–50 million years for leaves to appear (Lobellet al., 2018). Relatively scarcity of land resources for agriculture and insufficient food security of the world’s population require that the land be used in an optimum way in the context of climate change. With the increase of demand for arable crops such as rice and maize and the crunching effects of climate change on their production, it has become more important as people strive to make better use of the available methods to increase yields(Lobellet al., 2018)..   Artificial intelligence or Artificial Neural Network is the development of software that combines problem-solving and decision-making to achieve goals through replicating the process of ‘sense, decide and act’. It is developed to be used by computers and machines (Alen, 2016). Among other definitions, Machine Learning (ML) is defined as the scientific field that gives machines the ability to learn without being strictly programmed (Samuel, 2019). Artificial intelligence uses machine learning algorithm in which the system generates some adaptive learning approaches in order to achieve some goal of environment, artificial intelligence is a vast field comprising large areas like logical reasoning, computation, and probability.  Arable crop breeding such as rice and maize can employ the use of Artificial Intelligence to address climate change-related stakes by; helping enable farmers to avoid crop losses related to climate change to the degree that it results in crop varieties that are more resilient to the effects of climate change and helping reduce greenhouse gas emissions from agriculture by preventing further land conversion to agriculture Glaszmann (2015). Climate changes are predicted to have adverse impacts on food production, food quality (Atkinson et al., 2008) and food security. One of the most recent predictions is that the number of undernourished people would have increased by 150% in the Middle East and North Africa and by 300% in sub-Saharan Africa by the year 2080, compared to 1990 as such this review will substantively outline and elucidate the importance of Artificial Neural Network in breeding with the aim of combating climate change through state of the art tools, applications, and techniques.   1.2              STATEMENT OF THE PROBLEMClimate change are the main causes of stress on food production and availability. Depending on the level of development, roughly 20 to 80 percent of the inter-annual variability of yields is caused by the changes in weather and 5 to 10 percent of national agricultural production is lost annually due to climate. Chronic losses and indirect negative effects (i.e. diseases, pests…) exceed by far the effects of extreme (i.e. statistically rare) climatic events. In fact, production losses due to pests, diseases and weeds are estimated at 26 to 30 percent for sugar beet, barley, soybean, wheat and cotton, and 35, 39 and 40 percent for maize, potatoes and rice, respectively (Obrist, 2016). Post-harvest losses are also of the same order of magnitude.   Nevertheless, agricultural productivity in Africa falls far behind standards in the developed world. Over 90 percent of agriculture depends on rainfall without the aid of artificial irrigation. Only 5 percent of the cultivated land in Africa is irrigated and the majority of the farmers depend on rainfall, while in Asia, 38 percent of the arable land is irrigated. The techniques used to cultivate soil in Africa still fall far behind those that have been adopted in Asia and the Americas because of the lack of the benefits of irrigation, fertilisers, pesticides and access to high-yield seeds as well. Agriculture is extremely vulnerable to climate change and higher temperatures eventually reduce crop yields without discouraging weed, disease and pest challenges. Changes in precipitation patterns increase the likelihood of short-term crop failures and long-term declines in production Ceccarelli (2010). Although there will be gains in some crops in some regions of the world, the overall impact of climate change on agriculture is expected to be negative, threatening global food security (Nelson et al., 2019). Food insecurity would probably increase under climate change, unless early warning systems and development programmes are used more effectively (Nelson et al., 2019).  It is against this problems that this study seek to examine the effect of climate change on arable crops; rice and maize using artificial neural network.  1.3              OBJECTIVE OF THE STUDY The objective of this study is to examine the effect of climate change on arable crops; rice and maize using artificial neural network. The specific objectives of the study is to: i.        Identify the causes of climate changeii.      Determine the effect of climate change on arable crops production such as rice and maize iii.    To ascertain how the effect of climate change can be combated using artificial neural network 1.4              RESEARCH QUESTION 1.      What are the causes of climate change 2.      What are the effects of climate change on arable crop production? 3.      How can the effect of climate change on crop production be combated using artificial neural network? 1.5              SIGNIFICANCE OF THE STUDY The use artificial Intelligence (Artificial Neural Network) in agriculture has the potential to deliver much-needed solution. AI- based technological solutions has enabled the farmers to produce more output with less input and even improved the quality of output, also ensuring faster go-to- market for the yielded crops. The findings of this study on the effect of climate change on arable crops; rice and maize using artificial neural network will serve as an opener to farmers, government, policy makers and the general public on the effects of climate change on the available of arable crops such as maize and rice. It will enlighten them on how to use artificial intelligence in agricultural practice or arable crop production in particular to combat the effects of climate change on food security.  1.6              SCOPE AND LIMITATION OF THE STUDYThe scope of this study is limited to the effect of climate change on arable crops such as rice and maize using artificial neural network. The study will be based on how rainfall and temperature affects the production of arable crops (Rice and Maize) in Nigeria and Nasarawa state in particular.In the course of carrying out this research, the following problems were faced which posed some limitations to the study: 1.      Financial constraint– Insufficient fund tends to impede the efficiency of the researcher in sourcing for the relevant materials, literature or information and in the process of data collection (internet, questionnaire and interview).2.      Time constraint– The researcher will simultaneously engage in this study with other academic work. This consequently will cut down on the time devoted for the research work. 1.7              DEFINITION OF TERMS Climate Change: Climate change refers to long-term shifts in temperatures and weather patterns. These shifts may be natural, such as through variations in the solar cycle. But since the 1800s, human activities have been the main driver of climate change, primarily due to burning fossil fuels like coal, oil and gas.Arable Crops: Arable crops are crops that complete their life cycle, from germination to seed production, within one year. There are various types of arable crops depending on the type of their use. These include crops such as maize, rice, millet, lentil, beans, peas etc.Artificial Neural Network: Artificial neural networks, usually simply called neural networks  is a computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs.

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