Food Output Research Articles

The Potential Bioactive Components in Fruits Wastes as Value-Added Products from the Fruit Processing Industry: A Review

Fruits are an important source of nutrition as most of them contain sufficient amounts of minerals, vitamins, and antioxidants. However, processing these fruits results in enormous waste, accounting for around 40% of total food output. Fruit processing industry waste includes discarded pods, peels, pulp, stones, and seeds all of which can pose environmental and sustainability challenges. Importance of discovering the potential of fruit processing waste into value added products lies in the fact that the fruit processing industries generates substantial waste, contributing to environmental pollution, greenhouse gas emissions, and resource depletion. The solid waste generated by the fruit processing industry may include toxic biological components that contaminate aqueous media, degrade drinking water quality, and cause gastrointestinal disorders in animals. However, Fruit processing waste or byproducts have the potential to be converted into value-added goods of economic importance like candied peel, oils, pectin, reformed fruit fragments, enzymes, and wine/vinegar candied peel. Converting this waste into valuable products can mitigate these issues, while also creating new revenue streams, jobs, and industries. Furthermore, value-added products from fruit processing waste can enhance food security, nutrition, and sustainability, supporting the United Nations' Sustainable Development Goals (SDGs). Additionally, research in this area can also lead to technological innovations, improved efficiency and scalability, enabling companies to gain a competitive edge in the growing market for sustainable products. This article provides an overview of all common and possible consumer friendly uses of fruit processing wastes and deriving value added products out of it.

A Generalised Additive Model and Deep Learning Method for Cross-Validating the North Atlantic Oscillation Index

This study introduces an innovative analytical methodology for examining the interconnections among the atmosphere, ocean, and society. The primary area of interest pertains to the North Atlantic Oscillation (NAO), a notable phenomenon characterised by daily to decadal fluctuations in atmospheric conditions over the Northern Hemisphere. The NAO has a prominent impact on winter weather patterns in North America, Europe, and to some extent, Asia. This impact has significant ramifications for civilization, as well as for marine, freshwater, and terrestrial ecosystems, and food chains. Accurate predictions of the surface NAO hold significant importance for society in terms of energy consumption planning and adaptation to severe winter conditions, such as winter wind and snowstorms, which can result in property damage and disruptions to transportation networks. Moreover, it is crucial to improve climate forecasts in order to bolster the resilience of food systems. This would enable producers to quickly respond to expected changes and make the required modifications, such as adjusting their food output or expanding their product range, in order to reduce potential hazards. The forecast centres prioritise and actively research the predictability and variability of the NAO. Nevertheless, it is increasingly evident that conventional analytical methods and prediction models that rely solely on scientific methodologies are inadequate in comprehensively addressing the transdisciplinary dimension of NAO variability. This includes a comprehensive view of research, forecasting, and social ramifications. This study introduces a new framework that combines sophisticated Big Data analytic techniques and forecasting tools using a generalised additive model to investigate the fluctuations of the NAO and the interplay between the ocean and atmosphere. Additionally, it explores innovative approaches to analyze the socio-economic response associated with these phenomena using text mining tools, specifically modern deep learning techniques. The analysis is conducted on an extensive corpora of free text information sourced from media outlets, public companies, government reports, and newspapers. Overall, the result shows that the NAO index has been reproduced well by the Deep-NAO model with a correlation coefficient of 0.74.

A long-term perspective on collecting Indian agricultural statistics: reviewing the purposes, methods, and implications for Indian development policy

This article adopts a long-term perspective to examine Indian agricultural data: from crop yields for estimating food output, the examination of food prices and wages for calculating income and living standards, to the status of food consumption to ascertain hunger and malnutrition. The focus will be on the evaluation of how the new statistical data collection methods that emerged in the early decades of twentieth century have impacted the field of development economics in India. It makes the case that the emergence of survey methods data that were initially created by officials and later on by professionals employed in institutions in India were novel and began to gain attention in the international sphere. It proceeds to underline the importance of these statistical databases for identifying the minimum standard of living to escape poverty as an important measure for ascertaining how national development strategy could reduce poverty and hunger and also shows that these databases proved to be a core resource for unpicking recent puzzles regarding food and nutrition levels among the poorer sections of society. Additionally, the article engages with more recent estimation methods where survey-based agricultural statistical databases have been incorporated in multi-scalar indices alongside meteorological and ecological data to create more informative agricultural databases to advance the objective of sustainable agricultural development in India.

Impact of Crop Production on Economic Growth in Nigeria, 1981 – 2018

This study examined the impact of crop production on economic growth in Nigeria utilizing annual time series data from 1981 to 2018. The study used Johansen cointegration test, Vector Error Correction test, the impulse response functions and variance decomposition for data analysis after the data series were checked against unit root problem using the Augmented Dickey Fuller unit root test. The study tested and found that increase in the production of cassava, cocoyam, groundnut, guinea corn, maize, millet, rice and yam spurs economic growth in Nigeria in the long-run except for cotton production and with only groundnut production contributing significantly to the output of the Nigerian economy in the short- run. The study, therefore recommends that the Nigerian government should encourage farmers in the production of crops through supply of fertilizer to increase their productivity and food output while improving their use of modern seedlings and technology in the production that may in turn increase the crop production while farmers should be encouraged to adapt best practices in farming to maximize crop yield.

Deciphering the impact of cold-adapted bioinoculants on rhizosphere dynamics, biofortification, and yield of kidney bean across varied altitudinal zones

Agriculture stands as a thriving enterprise in India, serving as both the bedrock of economy and vital source of nutrition. In response to the escalating demands for high-quality food for swiftly expanding population, agricultural endeavors are extending their reach into the elevated terrains of the Himalayas, tapping into abundant resources for bolstering food production. Nonetheless, these Himalayan agro-ecosystems encounter persistent challenges, leading to crop losses. These challenges stem from a combination of factors including prevailing frigid temperatures, suboptimal farming practices, unpredictable climatic shifts, subdivided land ownership, and limited resources. While the utilization of chemical fertilizers has been embraced to enhance the quality of food output, genuine concerns have arisen due to the potential hazards they pose. Consequently, the present investigation was initiated with the objective of formulating environmentally friendly and cold-tolerant broad ranged bioinoculants tailored to enhance the production of Kidney bean while concurrently enriching its nutrient content across entire hilly regions. The outcomes of this study unveiled noteworthy advancements in kidney bean yield, registering a substantial increase ranging from 12.51 ± 2.39 % to 14.15 ± 0.83 % in regions of lower elevation (Jeolikote) and an even more remarkable surge ranging from 20.60 ± 3.03 % to 29.97 ± 5.02 % in higher elevated areas (Chakrata) compared to the control group. Furthermore, these cold-tolerant bioinoculants exhibited a dual advantage by fostering the enhancement of essential nutrients within the grains and fostering a positive influence on the diversity and abundance of microbial life in the rhizosphere. As a result, to effectively tackle the issues associated with chemical fertilizers and to achieve sustainable improvements in both the yield and nutrient composition of kidney bean across varying elevations, the adoption of cold-tolerant Enterobacter hormaechei CHM16, and Pantoea agglomerans HRM 23, including the consortium, presents a promising avenue. Additionally, this study has contributed significant insights-into the role of organic acids like oxalic acid in the solubilization of nutrients, thereby expanding the existing knowledge in this specialized field.

The Economic Value of Pollination Services for Seed Production: A Blind Spot Deserving Attention

AbstractAnimal-mediated pollination is important for agricultural seed and crop production, and critical to overall ecosystem health. However, the scientific literature focused on the economic valuation of pollination services has thus far neglected the role of pollination services in seed production. The marketed food output of many crops is not dependent on pollination services, but these crops indirectly depend on pollination services with respect to their seed production. This study proposes a partial equilibrium framework for identifying the value of pollination services. Using Germany as a case study, we find the value of pollination services is about 33% higher when seed production is considered. This increased valuation is driven by two effects: higher seed costs due to high dependence on pollination services, and a higher demand for seeds due to the land expansion needed to mitigate a potential pollinator collapse. This study demonstrates that more sophisticated approaches are needed to estimate the economic value of pollination services more accurately.

Technical Efficiency Evaluation of Maize Production in Ghana: An Application of Three-Stage Data Envelopment Analysis

Purpose: In Ghana, maize constitutes the primary source of revenue and food for small-scale farmers, and to attain maize self-sufficiency, appropriate production techniques must be employed, coupled with the scaling up of production efficiency to supplement our food output. The objectives of the study are to estimate the technical efficiency and analyze the trend in total factor productivity of maize production. Method: This study focuses on forty-eight (48) cities in six (6) selected regions of Ghana: Northern, Upper East, Upper West, Central, Eastern, and Volta. Secondary data on maize production was obtained from the Ministry of Food and Agriculture from the 2010/11 to 2020/21 cropping seasons. Environmental variables (amount of rainfall and temperature) were sourced from the Ghana Meteorological Agency (GMA) from 2010/11 to 2020/21. Results and Conclusion: The results displayed a fairly high level of TE, and environmental factors significantly influence production efficiency. The study revealed that the TE of maize cultivation was 0.752 and 0.853 for the first and third stages, respectively. From the findings, the efficiency scores attained after the adjustments to the input in the third stage of the DEA model were greater than those in the first stage. Also, the results indicated the eastern region had the highest TE scores, showed an increment in productivity, and serves as the finest region for maize cultivation. Overall, the technical performance of maize cultivation had shown an upward trend in productivity, and the eastern region recorded the highest efficiency performance and technological progress. Implications of the Research: The research highlights the significance of the maize crop to the Ghanaian economy and how environmental factors impact farmers' production efficiency. It also emphasizes the benefits of resource use efficiency and its implied effects on the total factor productivity of maize cultivation. Policy suggestions include sustainable production practices primarily for farmers living in susceptible areas to enhance productivity and improve their food security status. Originality/Value: The gap discovered in the examined literature was that there exists a substantial deficiency in the usage of the three-stage DEA Malmquist model for analyzing technical efficiency and limited studies on the estimation of the total factor productivity (TFP) changes, specifically focusing on maize farming. This paper adds to the body of the existing literature by utilizing a three-stage DEA model to evaluate the TE of maize production. This is entirely limited both in theory for annual crops and methodologically in the Ghanaian literature on efficiency studies.

CROP PREDICTION

Cereal crops such as rice, wheat, and different pulses account for the majority of India's food output. Predicting crop yields far ahead of harvest would assist policymakers and farmers in making informed decisions about agronomy, crop selection, and agricultural planning. Such forecasts will also assist related sectors in planning their logistical operations. The goal of the research is to create a machine learning model that can generate such predictions. The model is trained using a dataset that incorporates soil data from the previous decade, with features such as Ph value, temperature, and crop name, as well as labels such as crop yield. The model learns the link between the yield and variables such as soil type, location, and forecast using appropriate machine learning techniques.

Examining the U-shaped relationship between non-agricultural sources water pollution and the urban-rural income gap.

Determining how the economy and society interact with the environment of water quality is essential to determining the financial impact of green development. Based on China's provincial panel data from 2010 to 2021, this research considers non-agricultural sources of water pollution (NASWP) as a negative factor of production, investigates its influence on the urban-rural divide, and explains the mechanism of action. The empirical results show that there is a significant correlation between NASWP and the urban-rural gap, with a "U-shaped" relationship between the two. Water pollution first reduces and then increases the urban-rural income gap, and the results are robust after considering endogeneity. Mechanistic research demonstrates that NASWP cause a loss in food output, which in combination with changes in food prices and food subsidy programs impacts the incomes of rural dwellers, thereby having an influence on the urban-rural income gap. Using the threshold effect model, it is discovered that under the combined influence of agricultural mechanization and food subsidy policy, the relationship between NASWP and urban-rural income divide exhibits an U-shape in areas with high agricultural mechanization and an "inverted U" shape in areas with low agricultural mechanization.

Socioeconomic factors influencing the uptake of tissue culture banana technology in Kisii County, Kenya

Many nations have worked hard over the years to provide food security for there entire country, albeit with various degrees of success. The intensification of agriculture has been significant in order to feed the growing population. The banana tissue culture technology is one of the technologies used to aid the process of agricultural intensification. Due to its long history of food production, including the cultivation of bananas, the Kisii region is a significant contributor to Kenya's food security. However, because of issues brought on by social and economic considerations, the region's food output has been declining. Despite efforts to distribute this technology to small-scale farmers, majority of research studies in Kisii County show poor rates of technology adoption. The objective of this study was to examine the socioeconomic factors affecting implementing tissue culture bananas in Kisii County. The research used a descriptive study design. Two hundred respondents were chosen at random from the sample to participate in the study. Survey forms, interview schedules, and observation checklists were used for data collection. The means between adoption categories were declared at p < 0.05 in t-tests between tissue culture banana adoption and numerical factors. Chi-square tests were performed between adoption and categorical factors, and p < 0.05 was used to determine whether there were significant connections between the variables. The study adopted a logistic regression model with maximum likelihood estimation to calculate the likelihood that farmers will adopt tissue culture bananas as impacted by various socioeconomic factors. Results showed that the availability of extension services (p = 0.000), cost of seedlings (p = 0.000, x2=79.1), ability to purchase land (p = 0.006, x2=16.3), access to financing (p = 0.007, x2=7.468), education level (p = 0.015), ability to afford seedlings (p = 0.000, x2=17.6), labour availability (p = 0.005, x2=10.735), availability of farm inputs (p = 0.000, x2=35.9) and the size of household (p = 0.05, Std=1.8) were significant to tissue culture banana adoption. Socio-economic factors ought to be taken into account in order to assist a number of stakeholders in boosting banana output and enhancing food security.

Glimpse of Artificial Intelligence in the Field of Agriculture

Artificial intelligence in agriculture is a developing topic with enormous potential to transform the way we produce and manage food. With the world's population growing and the need for more sustainable and effective farming practises, AI technologies have the potential to improve crop yields, reduce waste, and optimise resource allocation. The chapter will argue that incorporating AI into agriculture can lead to major improvements in output, sustainability, and security of food. The review paper intends to illustrate the relevance of embracing this transformational approach by evaluating the current issues encountered by the agriculture business and the anticipated benefits of AI technologies. As we progress through the chapter, we will look at specific uses of AI in agriculture and examine its significance for the future of farming.

Future Food Production Prediction Using AROA Based Hybrid Deep Learning Model in Agri-Sector

Policymaking and administration of national tactics of action for food security rely heavily on advances in models for accurate estimation of food output. In several fields, including food science and engineering, machine learning (ML) has been established to be an effective tool for data investigation and modelling. There has been a rise in recent years in the application of ML models to the tracking and forecasting of food safety. In our analysis, we focused on two sources of food production: livestock production and agricultural production. Livestock production was measured in terms of yield, number of animals, and sum of animals slaughtered; crop output was measured in terms of yields and losses. An innovative hybrid deep learning model is proposed in this paper by fusing a Dense Convolutional Network (DenseNet) with a Long Short-Term Memory (LSTM) to do production analysis. The hybridised algorithm, or A-ROA for short, combines the Arithmetic Optimisation Algorithm (AOA) and the Rider Optimisation Algorithm (ROA) to determine the ideal weight of the LSTM. The current investigation focuses on Iran as a case study. Therefore, we have collected FAOSTAT time series data on livestock and farming outputs in Iran from 1961 to 2017. Findings from this study can help policymakers plan for future generations' food safety and supply by providing a model to anticipate the upcoming food construction.

IoT based tracking cattle healthmonitoring system using wireless sensors

Controlling the health of dairy cows is crucial for increasing global dairy food output. Dairy farmers are losing faith in the sector as cows suffer from a range of health issues, disease outbreaks that are unpredictable, and expensive breeding expenses. As a consequence, farmers must use efficient technology techniques for cow health monitoring in order to enhance milk output. This study looked at a range of wireless sensor-based automated dairy cow health monitoring systems. The fundamental purpose of wireless sensor network (WSN) based smart surveillance systems in agricultural optimization is to follow the health of dairy cows on a continual basis. This monitoring gadget must be installed in both local and remote farm areas so that interested farmers may monitor their cattle's movements throughout the day from several places. The data collected by the automated system would be kept in a database. Farmers may then obtain data using farm automation to execute effective farm management techniques. Furthermore, WSN is a low-cost device created exclusively for identifying illnesses in dairy cows. This achievement in sophisticated technology agricultural automation would assist to boost productivity by reducing human involvement. This article summarises all livestock tracking techniques, as well as the issues and challenges that they confront.

Enhancing Essential Grains Yield for Sustainable Food Security and Bio-Safe Agriculture through Latest Innovative Approaches

A key concern in agriculture is how to feed the expanding population and safeguard the environment from the ill effects of climate change. To feed a growing global population, food production and security are significant problems, as food output may need to double by 2050. Thus, more innovative and effective approaches for increasing agricultural productivity (hence, food production) are required to meet the rising demand for food. The world’s most widely cultivated grains include corn, wheat, and rice, which serve as the foundation for basic foods. This review focuses on some of the key most up-to-date approaches that boost wheat, rice, corn, barley, and oat yields with insight into how molecular technology and genetics may raise the production and resource-efficient use of these important grains. Although red light management and genetic manipulation show maximal grain yield enhancement, other covered strategies including bacterial-nutrient management, solar brightening, facing abiotic stress through innovative agricultural systems, fertilizer management, harmful gas emissions reduction, photosynthesis enhancement, stress tolerance, disease resistance, and varietal improvement also enhance grain production and increase plant resistance to harmful environmental circumstances. This study also discusses the potential challenges of the addressed approaches and possible future perspectives.

Risk state evaluation model for China's food import using G1-LS and variable weight SPA based on bottom-line thinking

PurposeTo maintain the bottom line of food import risk in China, this paper proposes a novel risk state evaluation model based on bottom-line thinking after analyzing the decision-making ideas embedded in the bottom-line thinking method.Design/methodology/approachFirst, the order relation analysis method (G1 method) and Laplacian score (LS) are applied to calculate the constant weights of indexes. Then, the worst-case scenario of food import risk can be estimated to strive for the best result, so the penalty state variable weight function is introduced to obtain variable weights of indexes. Finally, the study measures the risk state of China's food import from the overall situation using the set pair analysis (SPA) method and identifies the key factors affecting food import risk.FindingsThe risk states of food supply in eight countries are in the state of average potential and partial back potential as a whole. The results indicate that China's food import risks are at medium and upper-medium risk levels in most years, fluctuating slightly from 2010 to 2020. In addition, some factors are diagnosed as the primary control objects for holding the bottom line of food import risk in China, including food output level, food export capacity, bilateral relationship and political risk.Originality/valueThis paper proposes a novel risk state evaluation model following bottom-line thinking for food import risk in China. Besides, SPA is first applied to the risk evaluation of food import, expanding the application field of the SPA method.

Asymmetric effects of climate variability on food security in Morocco: evidence from the nonlinear ARDL model

PurposeThis study aims to examine the asymmetric effects of average temperature (TP) and rainfall (RF) on the Moroccan food security, measured by the food production index (FPI), using annual data from 1961 to 2020.Design/methodology/approachThe study uses the Climate Change and Food Security Framework (CCFS) developed by the Food and Agriculture Organization (FAO) and employs the nonlinear auto-regressive distributed lag (NARDL) model and various econometric techniques to show the effects of climate variability in the short and long-term. It also examines if the impacts on Moroccan food security are asymmetric by analyzing the positive and negative partial sums of mean temperature and rainfall.FindingsThe study shows that RF has a long-term relationship with FPI, with increased RF leading to increased FPI and decreased RF leading to decreased FPI. FPI responds more strongly and persistently to a positive shock in RF than to an adverse shock. The study also identifies an asymmetric relationship between FPI and RF, with increased TP enhancing food output in the long run and a decrease reducing food production in the long run.Research limitations/implicationsThe current study could have some limitations. For instance, there are several other non-climate factors that might potentially impact food security. In particular, CO2 emissions which from the literature is a key variable that represent climate change impact on food security, was not included. The present research has not included those factors mainly because adding more variables to the model reduces the degree of freedom available to estimate the parameters, resulting in inaccurate results.Originality/valueThis paper contributes to the food security literature by utilizing the latest asymmetry methodology to decompose climate changes into their positive and negative trends and examining the contrasting impacts food production.

Russia’s Not-So-Secure Food Security

ABSTRACT The dominant approach to food security emphasizes food production and supply. This approach is too narrow. In reality, inputs are as important as actual food output. Without critical inputs, food production is constrained. Russia’s invasion of Ukraine illustrates the narrowness of the food production approach to food security. Russia is experiencing “input insecurity” which was revealed after its invasion of Ukraine. Specifically, the article explores Russia’s dependence on foreign farm machinery, seeds, and pedigree livestock. Russia’s input insecurity may not be long-term, but even so the dependence on foreign inputs means that Russia’s food security is not as secure as previously believed.

Quantifying the impact of Russia–Ukraine crisis on food security and trade pattern: evidence from a structural general equilibrium trade model

PurposeConsidering the importance of Russia and Ukraine in agriculture, the authors quantify the potential impact of the Russia–Ukraine conflict on food output, trade, prices and food security for the world.Design/methodology/approachThe authors mainly use the quantitative and structural multi-country and multi-sector general equilibrium trade model to analyze the potential impacts of the conflict on the global food trade pattern and security.FindingsFirst, the authors found that the conflict would lead to soaring agricultural prices, decreasing trade volume and severe food insecurity especially for countries that rely heavily on grain imports from Ukraine and Russia, such as Egypt and Turkey. Second, major production countries such as the United States and Canada may even benefit from the conflict. Third, restrictions on upstream energy and fertilizer will amplify the negative effects of food insecurity.Originality/valueThis study analyzed the effect of Russia–Ukraine conflict on global food security based on sector linkages and the quantitative general equilibrium trade framework. With a clearer demonstration of the influence about the inherent mechanism based on fewer parameters compared with traditional Global Trade Analysis Project (GTAP) models, the authors showed integrated impacts of the conflict on food output, trade, prices and welfare across sectors and countries.

Food price and Inflation Adjustments in Nigeria: Evidence from Threshold Cointegration

This paper investigates two stages of transmission through which inflation stabilization policy affects headline inflation by estimating a threshold cointegration model. The paper finds that asymmetric monetary shocks passing-through food prices exerts inflationary pressure on consumer prices, and is characterized by deep asymmetric movement, large degree of stickiness in adjusting downward, while exhibiting upward momentum in correcting food price fall. In addition, asymmetries were found in consumer price adjustment as food price shock transmits substantial inflationary pressure to consumer prices, while disinflationary effect originating from a fall in food price is inconsequential. The coefficients of adjustment were found to be robust in the sub-sample. This paper concludes that an unconventional monetary policy intervention that aims at stabilizing headline inflation by means of stimulating food supply could unavoidably inflate food prices more than it could deflate it especially if the elasticity of domestic food output to money supply growth is less compared to response of food price to money supply. Hence, caution must be taken on the size differential of output response to the net rise in money supply and price adjustment to the expansion in money supply.

Impact of agricultural production outsourcing services on carbon emissions in China.

Increasing food output while reducing agricultural carbon emissions (ACE) is a major challenge to achieving green agriculture in China. Previously unexplored research regarding the relationship between agricultural production outsourcing services (APOS) and ACE is investigated using household microsurvey data in China and econometric methods, and the influencing mechanism is also tested empirically. The results reveal that APOS increases the total ACE and reduces ACE per unit area. The mechanism test shows that APOS are beneficial to the expansion of rice planting areas and farmers' adoption of agricultural green production techniques. The increasing effect of rice planting area on ACE was greater than the decreasing effect of green production techniques. In the future, China should accelerate the selection and breeding of rice varieties with low carbon emissions to reduce the increase of ACE caused by large-scale land management in the process of APOS development. Policy support and subsidies should be strengthened for APOS organizations to purchase green production techniques, so as to further drive farmers to adopt green production techniques and reduce ACE. It is also necessary for China to encourage small-scale farmers to outsource their agricultural production activities to APOS organizations. The role of collective action consciousness of agricultural production in increasing the supply capacity of APOS organizations should be enhanced.