IMPLICATIONS OF NEURAL NETWORK AS A DECISION-MAKING TOOL IN MANAGING KAZAKHSTAN’S AGRICULTURAL ECONOMY

This study investigates the application of Artificial Neural Networks (ANN) in forecasting agricultural yields in Kazakhstan, highlighting its implications for economic management and policy-making. Utilizing data from the Bureau of National Statistics of the Republic of Kazakhstan (2000-2023), the research develops two ANN models using the Neural Net Fitting library in MATLAB. The first model predicts the total gross yield of main agricultural crops, while the second forecasts the share of individual crops, including cereals, oilseeds, potatoes, vegetables, melons, and sugar beets. The models demonstrate high accuracy, with the total gross yield model achieving an R-squared value of 0.98 and the individual crop model showing an R value of 0.99375. These results indicate a strong predictive capability, essential for practical agricultural and economic planning. The study extends previous research by incorporating a comprehensive range of climatic and agrochemical data, enhancing the precision of yield predictions. The findings have significant implications for Kazakhstan's economy. Accurate yield predictions can optimize agricultural planning, contribute to food security, and inform policy decisions. The successful application of ANN models showcases the potential of AI and machine learning in agriculture, suggesting a pathway towards more efficient, sustainable farming practices and improved quality management systems

Extrapolation for Time-Series and Cross-Sectional Data

Extrapolation methods are reliable, objective, inexpensive, quick, and easily automated. As a result, they are widely used, especially for inventory and production forecasts, for operational planning for up to two years ahead, and for long-term forecasts in some situations, such as population forecasting. This paper provides principles for selecting and preparing data, making seasonal adjustments, extrapolating, assessing uncertainty, and identifying when to use extrapolation. The principles are based on received wisdom (i.e., experts’ commonly held opinions) and on empirical studies. Some of the more important principles are:• In selecting and preparing data, use all relevant data and adjust the data for important events that occurred in the past.• Make seasonal adjustments only when seasonal effects are expected and only if there is good evidence by which to measure them.• In extrapolating, use simple functional forms. Weight the most recent data heavily if there are small measurement errors, stable series, and short forecast horizons. Domain knowledge and forecasting expertise can help to select effective extrapolation procedures. When there is uncertainty, be conservative in forecasting trends. Update extrapolation models as new data are received.• To assess uncertainty, make empirical estimates to establish prediction intervals.• Use pure extrapolation when many forecasts are required, little is known about the situation, the situation is stable, and expert forecasts might be biased

A Case Study for Decentralized Heat Storage Solutions in the Agroindustry Sector Using Phase Change Materials

The development of thermal energy storage solutions (TES) in agroindustry allows reduction of production costs and improvement of operation sustainability. Such solutions require high storage capacity and the ability to adapt to existing equipment. The use of phase change materials (PCMs), which are able to store thermal energy as latent heat, creates new opportunities for heat storage solutions (LHS, latent heat storage) with higher energy density and improved performance when compared to sensible heat storage. New architectures are envisaged where heat storage is distributed throughout the production chain, creating prospects for the integration of renewable generation and recovery of industrial heat waste. This work aims to investigate the benefits of decentralized thermal storage architecture, directly incorporating PCM into the existing equipment of an agroindustry production line. To assess the feasibility and potential gain in the adoption of this TES/LHS distributed solution, a tempering and mixing equipment for food granules is selected as a case study, representing a larger cluster operating under the operation paradigm of water jacket heating. The behavior of the equipment, incorporating an inorganic PCM, is modeled and analyzed in the ANSYS Fluent software. Subsequently, a prototype is instrumented and used in laboratory tests, allowing for data collection and validation of the simulation model. This case study presents a demonstration of the increase in storage capacity and the extension of the discharge process when compared to a conventional solution that uses water for sensible heat storage.publishersversionpublishe

Business begins at home

One of the most significant trends in the post-industrial era has been for the home to become an important focus for work. The boundaries between work and home are now increasingly blurred, reversing the forces of the industrial era in which places deemed suitable for each were clearly demarcated and physically separate. The most recent published figures available from the Labour Force Survey (2005)1 indicate that 3.1m people now work mainly from home, 11% of the workforce. This represents a rise from 2.3m in 1997 (9% of the workforce), a 35% increase. The majority of homeworkers (2.4m or 77% of the total) are 'teleworkers' – people who use computers and telecommunications to work at home. The number of teleworkers has increased by 1.5m between 1997 and 2005, a 166% increase. Clearly, it is the growth in the number of teleworkers which is driving the increase in homeworking

Internet of Things Applications in Precision Agriculture: A Review

The goal of this paper is to review the implementation of an Internet of Things (IoT)-based system in the precision agriculture sector. Each year, farmers suffer enormous losses as a result of insect infestations and a lack of equipment to manage the farm effectively. The selected article summarises the recommended systematic equipment and approach for implementing an IoT in smart farming. This review's purpose is to identify and discuss the significant devices, cloud platforms, communication protocols, and data processing methodologies. This review highlights an updated technology for agricultural smart management by revising every area, such as crop field data and application utilization. By customizing their technology spending decisions, agriculture stakeholders can better protect the environment and increase food production in a way that meets future global demand. Last but not least, the contribution of this research is that the use of IoT in the agricultural sector helps to improve sensing and monitoring of production, including farm resource usage, animal behavior, crop growth, and food processing. Also, it provides a better understanding of the individual agricultural circumstances, such as environmental and weather conditions, the growth of weeds, pests, and diseases

Interannual variability in carbon dioxide fluxes and flux–climate relationships on grazed and ungrazed northern mixed-grass prairie

The annual carbon (C) budget of grasslands is highly dynamic, dependent on grazing history and on effects of interannual variability (IAV) in climate on carbon dioxide (CO2) fluxes. Variability in climatic drivers may directly affect fluxes, but also may indirectly affect fluxes by altering the response of the biota to the environment, an effect termed ‘functional change’

Ag-IoT for crop and environment monitoring: Past, present, and future

CONTEXT: Automated monitoring of the soil-plant-atmospheric continuum at a high spatiotemporal resolution is a key to transform the labor-intensive, experience-based decision making to an automatic, data-driven approach in agricultural production. Growers could make better management decisions by leveraging the real-time field data while researchers could utilize these data to answer key scientific questions. Traditionally, data collection in agricultural fields, which largely relies on human labor, can only generate limited numbers of data points with low resolution and accuracy. During the last two decades, crop monitoring has drastically evolved with the advancement of modern sensing technologies. Most importantly, the introduction of IoT (Internet of Things) into crop, soil, and microclimate sensing has transformed crop monitoring into a quantitative and data-driven work from a qualitative and experience-based task. OBJECTIVE: Ag-IoT systems enable a data pipeline for modern agriculture that includes data collection, transmission, storage, visualization, analysis, and decision-making. This review serves as a technical guide for Ag-IoT system design and development for crop, soil, and microclimate monitoring. METHODS: It highlighted Ag-IoT platforms presented in 115 academic publications between 2011 and 2021 worldwide. These publications were analyzed based on the types of sensors and actuators used, main control boards, types of farming, crops observed, communication technologies and protocols, power supplies, and energy storage used in Ag-IoT platforms

Sustainable Artificial Intelligence Solutions for Agricultural Efficiency and Carbon Footprint Reduction in India

By boosting productivity, cutting waste, and raising yields, Artificial Intelligence (AI) has the potential to revolutionise Indian agriculture. It is crucial to consider the complete life cycle of AI systems to reduce the carbon footprint of AI in agriculture. Though the impact of artificial intelligence (AI) is always assumed to be positive in reducing carbon emissions, the forecasting analysis based on the exponential smoothing model and life cycle assessment (LCA) predicts that AI will decrease carbon emission in agriculture by 2030. To move forward, some policy recommendations include promoting energy-efficient AI hardware, adoption of renewable energy, optimizing AI algorithms for energy efficiency, supporting precision agriculture (PA), and embracing circular economy practices. The way to achieve sustainable agriculture with the combination of smart agriculture is through Precision farming, which has the potential to transform Indian agriculture, enhance food security and help farmers adapt to climate change while increasing efficiency. Data-driven decision in crop management can lessen climate change effects and reduce vulnerability to extreme weather events. Overall, lowering the carbon footprint of AI in agriculture would necessitate a combination of legislative initiatives that support energy-saving technologies, renewable energy, and environmentally friendly farming methods

Influence of climate change and variability on Coffea arabica in the East African highlands

A thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy (Agroclimatology) at the University of Witwatersrand, 2017.Plant development is inherently linked to meteorological variability. The phenology, distribution and production of crops and wild relatives has already altered in response to climate change. Recent years have produced the warmest mean annual global temperatures since 1880, with 2016 setting the highest record thus far. Such profound changes have sparked investigations into the impact of temperature and rainfall on crop development, particularly those with profound economic importance such as coffee (C. arabica). The crop is a fundamental source of income for smallholder farming communities and governments throughout the tropical highlands. However, the impact of climate change on C. arabica has yet to be quantified using empirical data in East Africa, leaving uncertainty in the cultivable future of the crop. Therefore, the objective of this thesis is to investigate the influence of climate change and variability on C. arabica yields and phenology in East Africa. Using a spatio-temporal approach, trends and relationships between coffee performance and meteorological variables were analysed at different scales and time periods ranging from the macroclimatic national scale (49 year), to the meso- and microclimatic farm level (3 year) scale, and finally to the microclimatic canopy and leaf level (hourly) scales. Data from all three climatic continua reveal for the first time that temperatures, and particularly rapidly advancing night time temperatures, are having a substantial negative impact on C. arabica yields. Forecasting models based on these biophysical relationships indicate that by the year 2050, smallholder farmers would on average harvest approximately 50% of the yield they are achieving today. Warming night time temperatures are also responsible for advancing ripening and harvest phenology. As a result, bean filling and development time is reduced, thereby potentially resulting in lower quality coffee. Trends in precipitation do not appear to have any substantial impact on C. arabica yields or harvest phenology, however, it is proposed that rainfall would act synergistically with temperatures to influence plant development and other phenological phases such as flowering. Finally, thermography is introduced as a novel complementary technique to rapidly analyse the suitability of different agroecological systems on coffee physiology at the leaf level. High temporal resolution (hourly) data, illustrate the success of the method in variable meteorological and environmental conditions. The findings contribute to advancing the protocol for use at the canopy and plantation level on coffee, so that appropriate microenvironment designs and adaptation mechanisms be put in place to accommodate climatic change. Avoiding increments in night time temperatures is key to maintaining or improving yields and fruiting development. Farming at higher altitudes and novel agroforestry systems may assist in achieving lower night time temperatures. Importantly, data reveal that careful analysis of various cropping systems, particularly at lower altitudes, is critical for providing suitable microenvironments for the crop.XL201

Using Micrometeorology to Gauge Agriculture\u27s Potential to Sequester Soil Carbon

In addition to reducing carbon dioxide (CO2) emissions from fossil fuel combustion, removing atmospheric CO2 may be critical to limit global warming to less than two degrees Celsius above pre-industrial levels recommended by leading experts. Since cropland occupies 11% of the earth’s land and is intensively managed, cropland agriculture provides one approach for removing CO2 from the atmosphere to mitigate climate change. However, current assessments indicate agriculture is a net emitter of CO2 and other greenhouse gases, and it is unclear how soil management can effect carbon sequestration.In this work micrometeorological methods are used to measure the exchange (flux) of CO2 between the surface and atmosphere and can assess whether an agricultural ecosystem is a source or sink for carbon. Three studies were performed using micrometeorology to understand agriculture’s potential to sequester carbon.Using Bowen Ratio Energy Balance (BREB) micrometeorological methods, the first study measured CO2 flux from a maize crop grown on no-till and tilled soils to determine tillage effects on CO2 emissions during 104 days of the 2015 maize growing season in north central Ohio. During this period, the no-till plot sequestered CO2, while the tilled plot was a net emitter.A second study determined if industrial biotechnology waste reutilization in agriculture could reduce CO2 emissions and generate environmental benefits, while meeting farmer yield expectations. Using both BREB and eddy covariance (EC) micrometeorological methods, CO2 flux was measured over maize where heat-inactivated, spent microbial biomass (SMB) amendment was land applied and compared with typical farmer practices from October 2016 to October 2017 in Loudon, Tennessee. While treatments with SMB emitted more CO2 than farmer practices, the SMB applications produced yields similar to farmer practices.Using BREB micrometeorology methods, the third study measured CO2 emissions over conservation agriculture (CA) practices as compared to conventional tillage from June 2013 to May 2016 in central Zimbabwe. The CA practices of no-till and cover crops produced significantly fewer CO2 emissions than conventional tillage.These studies demonstrate that micrometeorology can detect short- and long-term differences in CO2 flux between practices, providing data supporting agriculture’s potential to reduce CO2 emissions and sequester carbon