Future Of Agriculture Research Articles

The Impact of Machine Learning in Agriculture: Revolutionizing Farming Practices

Machine learning (ML) and big data analytics have emerged as transformative technologies in the agricultural sector, offering innovative solutions to enhance productivity, sustainability, and profitability. This abstract provides an overview of how ML and big data are revolutionizing traditional farming practices by leveraging advanced algorithms and data analytics. It discusses the potential of ML and big data in optimizing crop yield and quality, promoting sustainable practices, enabling precision agriculture, and addressing crop diseases through advanced predictive modelling. The abstract emphasizes the potential of ML and big data to reshape the future of agriculture, fostering resilience and prosperity for farmers and stakeholders worldwide. The application of machine learning in the field of agriculture and its correlation with weather patterns delves into various ways in which machine learning techniques can be used to analyze and predict the impact of weather conditions on agricultural productivity. The paper discusses specific machine learning algorithms and models that have been successfully applied in this domain, providing insights into the potential benefits and challenges of integrating machine learning into agricultural practices.

Are Biofungicides a Means of Plant Protection for the Future?

Biofungicides are prepared based on living micro/organisms or on matters prepared from them. They are based on the antagonism of fungal pathogens and their antagonists. Their effect depends on weather conditions (temperature and moisture) in comparison with chemical fungicides which are effective in all conditions but they let the residues in plants, animals, and men. The future of agriculture will be pure food without chemicals.

Smart Agriculture and Greenhouse Gas Emission Mitigation: A 6G-IoT Perspective

Smart farming has emerged as a promising approach to address the agriculture industry’s significant contribution to greenhouse gas (GHG) emissions. However, the effectiveness of current smart farming practices in mitigating GHG emissions remains a matter of ongoing debate. This review paper provides an in-depth examination of the current state of GHG emissions in smart farming, highlighting the limitations of existing practices in reducing GHG emissions and introducing innovative strategies that leverage the advanced capabilities of 6G-enabled IoT (6G-IoT). By enabling precise resource management, facilitating emission source identification and mitigation, and enhancing advanced emission reduction techniques, 6G-IoT integration offers a transformative solution for managing GHG emissions in agriculture. However, while smart agriculture focuses on technological applications for immediate efficiency gains, it also serves as a crucial component of sustainable agriculture by providing the tools necessary for long-term environmental supervision and resource sustainability. As a result, this study also contributes to sustainable agriculture by providing insights and guiding future advancements in smart farming, particularly in the context of 6G-IoT, to develop more effective GHG mitigation strategies for smart farming applications, promoting a more sustainable agricultural future.

Economic Supply Chain Management System of Farm Products Industry Based on the Belt and Road Under the Background of Big Data

Under the strategic background of "One Belt, One Road", the future farm products industry will also become part of the country's core competitiveness. Promoting the transformation of enterprises in the supply chain is conducive to cultivating the country's core competitiveness and creating good output conditions for my country's manufacturing industry. Under the national strategy of "One Belt, One Road", the agricultural product industry is facing new development opportunities. This paper analyzes the background, significance and existing basic problems of the "One Belt, One Road". Starting from the economic development of the national agricultural product industry, it uses the cluster analysis algorithm to analyze the current overall economic situation of the agricultural product industry, points out the current problems in the industry, and believes that the future farm The product industry is facing greater pressure for transformation and upgrading. Finally, combined with the strategic background of "One Belt One Road", from the perspectives of society, government and enterprises, analyze its supply chain process, extract relevant problems, and put forward relevant countermeasures and suggestions from the perspective of intra-chain, extra-chain and their interactive integration .

How and why we built our Smart Farm

The Smart Farm of Bioeconomy Campus project (2021–2023) developed a unique hub of Smart Farming technology. The resulting Smart Farm aims to accelerate the adoption of smart technologies in farms according to the United Nations Sustainable Development Goals (SDGs). Therefore, at the Smart Farm, near-market technologies and services are tested, developed, and demonstrated. The aim is to remove barriers to their adoption and accelerate innovation in the sector, significantly increasing the benefits for farmers and the related agricultural industry. The foundation of the Smart Farm is based on processing various types of data. In the project, data was intensively collected from 16 hectares of test plots where barley was cultivated. Regular measurements were taken from the soil, crops, and from machinery and tractors equipped with ISOBUS technology. Measurements included the use of wireless soil sensors (20 units), drone imaging (RGB, multispectral, and thermal cameras), satellite images, and tractor telematics data. Additionally, the usability of the 5G signal in machine guidance was measured. Based on the collected data, precision farming was planned and implemented. Automated field navigation with headland automation was compared to traditional manual driving methods. Using GIS, maps such as profitability and energy consumption maps were generated from the data. The project developed a Farmer's Data Repository, through which a farmer can license their data to the desired destination via a data delivery service. The project also demonstrated the operation of such a system, compliant with EU data regulations, in collaboration with partner companies. A comprehensive project, the Finnish Future Farm (2023–2026), has begun based on the foundation of the Smart Farm, involving companies, educational and research organizations, farmers, and stakeholders. The project will build a digital twin of the physical Smart Farm, both of which will be utilized in R&D, experimentation, and education.

COVID-19, the Russia–Ukraine war and the connectedness between the U.S. and Chinese agricultural futures markets

This study focuses on how recent global crises such as the COVID-19 pandemic and the Russia–Ukraine war have affected the relationship between the U.S. and Chinese agricultural futures markets. By applying wavelet coherence analysis (WCA) and time-varying parameter vector autoregression (TVP-VAR), we obtain the following findings. First, both events have changed the correlation and lead–lag comovement between U.S. and Chinese soybean and corn futures returns but have little impact on the comovement between the two cotton futures returns. Second, U.S. agricultural markets transmit more volatility risk to Chinese markets than the risk spillover from the reverse direction. Third, the risk spillover enhancement effect from the war is stronger than that from the pandemic, which is obvious in both the soybean and corn futures markets but not in the cotton market. Our paper has implications for policy makers seeking to stabilize agricultural commodity prices during global crisis episodes and for designing strategies for cross-market hedging of spillover risks among commodity markets for international investors.

Water, Wealth, and Engineering Wisdom: Shaping Tucumán's Agricultural Future, 1890-1910.

Focusing on Argentina's sugarcane province of Tucumán from 1870 to 1910, this article examines the processes of engineering professionalization in Argentina and its application to pressing environmental problems. Engineers were central to the processes through which elites in Latin America sought to attract foreign investment in agriculture, integrate their countries into the global economy, and provide expertise that enabled states to advance a techno-scientific imaginary based on liberal economic progress. Progressive bureaucrats and engineers, such as civil engineer Carlos Wauters, believed that they could use hydraulic infrastructure to transform Tucumán from an agricultural monoculture to a polyculture; others believed that infrastructure should be used to support a sugar monoculture. In exploring this issue, this article bridges the fields of engineering history, agricultural history, and environmental history. It also incorporates Latin America into global scholarship on the emergence and evolution of professional engineering.

Advance agriculture system using artificial intelligence: Way towards future farming

Advance agriculture system using artificial intelligence: Way towards future farming

Optical flow of temperature reveals climate change patterns for agriculture and forestry

The study of climate change's impact on terrestrial ecosystems is crucial for achieving societal and economic sustainability and human well-being. Domains like agriculture and forestry respond to climate change similarly to other terrestrial natural biomes, experiencing reductions in productivity, migration, and loss of biodiversity. However, there is a research gap in understanding climate velocity for different land cover and land use types, which could reveal vulnerability patterns in agriculture and forestry. To address this gap, we present the first study of climate velocity for major land cover and land use classes with a focus on major crop growing regions and functional forest types at the continental level. To enable this analysis, we introduce a new approach for estimating climate velocity based on optical flow. This approach allows us to conduct a global study, improve ecosystem interconnection representation, and reduce the model's sensitivity to noisy data and parametrization. Our study highlights the significant vulnerability to climate change of agriculture as a land cover type with a climate velocity 15% higher than of other vegetation land cover classes. Furthermore, the analysis of climate velocity for functional tree types reveals the highest expected impact on deciduous and evergreen needleleaf trees primarily concentrated in regions with the highest climate velocity, especially in sub-polar and temperate climates. Conversely, tropical forests dominated by evergreen broadleaf tree types exhibits marked variations in climate velocity with the highest rates in South America and Africa and three times lower in Asia. Additionally, our analysis of climate velocity for agriculture shows that North American croplands experience the highest climate velocity exceeding the global average by 32%. This trend is consistent for major crop types such as soybean that have higher climate velocity than globally for 49%, rapeseed and sunflower for 39%, cotton for 35%, maize for 32%, and wheat for 30%. Notably, areas dedicated to cereals, sugar beet, and sunflowers are located in zones with climate velocities higher than the global average by 15%, 12%, and 9%, respectively. These patterns indicate potential shifts in the agriculture and forestry structures in the near future that coherent with recent climate change studies and will have significant impact on the socio-economic dimension of humanity sustainability.

Enhancing Papaya Leaf Disease Detection with CNN and Transfer Learning Fusion for Precise Disease Diagnosis

Papaya cultivation plays a vital role in global agriculture, providing a crucial source of nutrition and economic stability. However, the threat of diseases poses a significant challenge to papaya plant health. To address this challenge, we proposed an innovative approach for enhancing the detection of papaya leaf diseases using Convolutional Neural Networks (CNNs) and transfer learning fusion. Our proposed framework leverages the strengths of CNNs, known for their ability to extract intricate features from images, and the transfer learning approach combines these techniques to create a robust and efficient model for accurate papaya leaf disease diagnosis. Transfer learning approach with two pre-trained models: VGG-16 and ResNet-50 are taken to detect papaya leaf diseases. The training process involves utilizing a pre-trained model and fine-tuning it on a specific papaya leaf disease dataset. For experimentation, five different classes of papaya leaves such as Fresh Papaya Leaf, Papaya Black Spot, Papaya Leaf Curl, Papaya Ringspot, and Powdery Mildew of Papaya are considered. Experimental results demonstrate the effectiveness of our proposed approach in accurately identifying and classifying various papaya leaf diseases. The proposed model has received an accuracy of 99.79% using ResetNet-50 followed by CNN whose accuracy was 99.42% and VGG-16, whose accuracy was 98.25%. This fusion approach aims to create a robust and efficient model capable of distinguishing subtle patterns indicative of various papaya leaf diseases. This method not only improves the efficiency of disease detection but also demonstrates the adaptability of the model to various disease patterns. Our study contributes to the advancement of automated papaya leaf disease detection, providing a reliable and precise tool for early diagnosis. Overall, our approach offers a promising solution for improving papaya cultivation and ensuring a sustainable future for global agriculture.

Designing and implementing a synchronized multi‐university cover crops course

AbstractThe increasing attention around cover crops in US agriculture to address many sustainability challenges necessitates targeted educational approaches to support future agriculture professionals. This article describes the process by which a team of educators, with different areas of expertise and from different regions, collaborated to design and implement a multi‐university, semester‐long, hybrid course titled “Cover Crops in Agroecosystems.” Course development occurred over 2 years during which the format, learning outcomes, as well as synchronized topics and activities were discussed, negotiated, and finalized. The course consisted of weekly pre‐recorded lecture videos, in‐person labs, and remote synchronous multi‐site meetings. An end‐of‐semester survey administered at six universities in fall 2022 found that students valued the multi‐regional dimension of the course, including learning from different instructors and interacting with students from other institutions. The survey also found broad student agreement that learning objectives were achieved, particularly around topics of cover crop agronomy and management. The team's experience with course development highlights how designing and implementing a multi‐university course where students report a quality learning experience is achievable. However, course execution ultimately required considerable effort, more than 2 years of planning, as well as ongoing team discussions and modifications that did not significantly reduce the time needed to prepare a more traditional individual instructor‐led course. Course execution also required considerable flexibility in decision‐making that aligned with different university constraints, instructor styles, and student needs. This effort and flexibility resulted in a quality student experience on a topic area of importance for advancing sustainable agriculture.

“Farmers need the Land”: Georgia's agricultural future in the patchy Anthropocene

This paper examines the ways that food systems experts (n = 9) and farmers working with conventional and alternative methods (n = 21) in the U.S. state of Georgia articulate connections between climate change, agriculture, and land loss. While originally seeking to understand differences in the perspectives on climate and food system futures articulated by stakeholders with varying approaches to agriculture, this paper explores the thematic convergences that emerged among participants as we discussed their visions of the future. Steering interviews away from purely technical or environmental dimensions of climate change, our conversations with participants began looping back to anxieties about the future of agricultural land loss in the region. These coalesced around land loss driven by development of solar fields, continued (sub)urban sprawl, and investment in farmland from entities outside the state. These environmental, economic, and social transformations, we argue, are inherently interconnected and invite analysis and solutions-oriented thinking to contextualize the entanglements between our global moment and the needs and wants of food systems stakeholders in specific places. To this end, we engage the lens of the “patchy Anthropocene” (Tsing et al., 2019) to understand the interconnectivities that food systems stakeholders articulate between land loss and food futures. Approaching the socio-environmental entanglements of climate change and agricultural futures through a patchy Anthropocene approach, we note, can help re-imagine how policies related to climate smart agriculture can be enriched to meet the futures our participants were worried about.

Selection of some important tobacco genotypes against waterlogging in Indonesia

Stagnant water is an environmental challenge that can have a negative impact on tobacco growth and productivity. Study on waterlogging-tolerant tobacco is still limited. Flood-resistant tobacco genotypes are needed for future agriculture. The aim of this study is to select tobacco genotypes that are tolerant to waterlogging. This study used a factorial randomized block design. The control treatment was 100% waterlogging, with subsequent treatments escalating to 120%, 140%, and 160% (Filed capacity). Waterlogging was given for 75 hours. The observation indicator is the level of wilting of tobacco leaves. Based on the stress index values obtained from the thirty-three tested genotypes, there are three categories of ability to withstand waterlogging, namely, tolerant, moderate and sensitive. Based on the results of the study analysis, it can be interpreted that the Bojonegoro 1 genotype is the only genotype that is included in the category of tolerant to waterlogging. Genotype 1 Bojonegoro was able to survive up to 73 hours with stress sensitivity index score of 0.21.

Nexus between climate change, agricultural output, fertilizer use, agriculture soil emissions: Novel implications in the context of environmental management

In evaluating the influence of greenhouse gases (GHGs) on climate change, the effectiveness of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) is intricately tied to their atmospheric turnover rates, which play a crucial role in their heat-trapping capacity. Understanding the dynamics of how these gases cycle through the atmosphere is essential for assessing their respective contributions to the greenhouse effect and, consequently, their impact on global warming and climate change. The prime objective of this research is to examine the role of climatic change, agriculture output, and fertilizer use on the agriculture soil's greenhouse gas emissions. In doing so, the present study has focused on the temperature of land, fertilizer consumption, crop and livestock production, and energy used in agriculture soils on the pollution level of agricultural soils. The study further delineates the intricate interdependencies between climate change factors and GHG emissions using novel econometric methodologies, specifically the PMG-ARDL, SC-ARDL, and Dumitrescu Hurlin Panel Causality frameworks. In doing so, we use a large panel dataset spanning 1990 to 2020. The estimations show that climate change, as measured by variations in terrestrial temperature, has a discernible and positive impact on GHG emissions over the short and long term. Energy consumption and livestock production positively correlate with GHG emissions, with the former having a more pronounced effect. The implications of fertilizer usage and overall crop yield become noticeably significant in the long term. It emphasizes the importance of using a diachronic perspective when assessing GHG emissions in the agricultural sector. It is also worth noting that agricultural land use appears to negatively impact GHG emissions, emphasizing the importance of implementing sustainable land management practices to mitigate adverse environmental consequences. The study also explores the causality between climate change, agricultural practices, and GHG emissions, revealing a bidirectional association between climatic change and soil emissions. Additionally, unidirectional causation is observed from fertilizer consumption and crop production to emissions, underscoring the importance of adopting sustainable agricultural practices to reduce emissions. The findings offer valuable insights for governments and researchers to create sustainability-related strategies for dealing with climate change issues, safeguarding natural resources, and ensuring a sustainable future for agriculture.

The Impact of Innovative Research Methods for Enhancing Agricultural Plants for Sustainable Development in The Future

In the pursuit of sustainable agricultural practices, the role of innovative research methods in enhancing agricultural plants is paramount. This abstract delineates the profound impact of such methodologies on sustainable development in the foreseeable future. Cutting-edge techniques such as genomic sequencing, molecular markers, and genome editing technologies revolutionize plant breeding by identifying and manipulating genes responsible for desirable traits, including disease resistance and drought tolerance. Omics technologies, encompassing transcriptomics, proteomics, and metabolomics, provide comprehensive insights into gene expression patterns and metabolic pathways, thereby facilitating targeted breeding strategies aimed at enhancing plant resilience and productivity. High-throughput phenotyping platforms enable rapid and accurate characterization of plant traits, empowering breeders to select superior varieties with enhanced performance under diverse environmental conditions. Concurrently, bioinformatics tools and data analytics play a pivotal role in deciphering complex genomic datasets, aiding in the identification of key genes and regulatory networks governing important agronomic traits. Biotechnology and synthetic biology approaches offer novel avenues for genetic manipulation, allowing for the design and engineering of plants with optimized traits, such as improved nutritional content and stress tolerance. Additionally, the integration of climate modeling with systems biology provides valuable insights into the interaction between genotype and environment, facilitating the development of climate-resilient crop varieties tailored to specific agroecosystems. Through the synergistic application of these innovative research methods, agricultural scientists and breeders can accelerate the pace of crop improvement while simultaneously promoting sustainability by fostering resilience, resource efficiency, and environmental stewardship. This transformative paradigm in agricultural research promises to address the complex challenges of food security, climate change, and environmental degradation, thereby laying the foundation for a more sustainable and resilient agricultural future.

Microbial Biotechnology: A Key Tool for Addressing Climate Change and Food Insecurity

Amidst escalating climate change and food insecurity concerns, exploring the potential of microbes offers a promising and sustainable solution. This review delves into the complex interplay between microbial communities and the dual challenge of environmental crisis and food security.
 Ubiquitous microorganisms – from bacteria to fungi and archaea – shape our planet's ecosystems, playing a crucial role in soil health, nutrient cycling, and plant-microbe interactions. This review dissects diverse microbial habitats, highlighting their remarkable adaptability to varied environments.
 It then underscores the reciprocal impacts of human-induced environmental changes on microbes and their habitats. Addressing these challenges, the review presents microbes as powerful allies in mitigating climate change. Their ability to sequester carbon, reduce greenhouse gas emissions, and enhance soil fertility is explored. Innovations like biofertilizers and biopesticides demonstrate the potential of microbial technologies to revolutionize agriculture and ensure global food security.
 Concluding, the review emphasizes the symbiotic link between microbes and sustainable food production. Microbial technologies can adapt agriculture to changing climate conditions, addressing water scarcity and enhancing soil moisture retention. Their potential to boost productivity in both traditional and precision agriculture under diverse climatic conditions is highlighted.
 This review calls for the urgent recognition and harnessing of microbial power for a sustainable future. Embracing microbial technologies not only fosters environmental stewardship but also paves the way for a resilient and resource-efficient agricultural future.

Text‐based corn futures price forecasting using improved neural basis expansion network

AbstractThe accurate forecasting of agricultural futures prices is critical for ensuring national food security. Therefore, this study proposes a text‐based deep learning forecasting model. This model first uses the ChineseBERT + a text convolution neural network to classify Weibo text and obtain a raw sentiment index. Then, complete ensemble empirical mode decomposition with adaptive noise, variational mode decomposition, correlation coefficient, and sample entropy are combined to decompose and reconstruct the raw sentiment index and obtain a denoised sentiment index. Subsequently, the neural basis expansion analysis with exogenous variables is improved by designing a weight coefficient and Optuna is used to optimize the designed weight coefficient and the hyperparameters. Finally, the SHapley Additive exPlanations value is used to increase the interpretability of prediction results. Corn futures prices for the Dalian Exchange are used in forecasting to validate the accuracy and stability of the proposed model. Experimental results show that the proposed denoising sentiment index contributes more to the improvement of predictive model performance than the raw sentiment index. The proposed text‐based deep predictive model demonstrates strong predictive ability for prediction horizons of 30 and 60 days. SHapley Additive exPlanations value analysis shows that the three features with greater effects on corn futures prices are as follows: “Corn Spot Price of Zhengzhou market,” “CBOT_corn_futures_price,” and “Pork futures price.”

Extreme risk spillovers between US and Chinese agricultural futures markets in crises: A dependence-switching copula-CoVaR model.

The linkages between the US and China, the world's two major agricultural powers, have brought great uncertainty to the global food markets. Inspired by these, this paper examines the extreme risk spillovers between US and Chinese agricultural futures markets during significant crises. We use a copula-conditional value at risk (CoVaR) model with Markov-switching regimes to capture the tail dependence in their pair markets. The study covers the period from January 2006 to December 2022 and identifies two distinct dependence regimes (stable and crisis periods). Moreover, we find significant and asymmetric upside/downside extreme risk spillovers between the US and Chinese markets, which are highly volatile in crises. Additionally, the impact of international capital flows (the financial channel) on risk spillovers is particularly pronounced during the global financial crisis. During the period of the COVID-19 pandemic and the Russia-Ukraine 2022 war, the impact of supply chain disruptions (the non-financial channel) is highlighted. Our findings provide a theoretical reference for monitoring the co-movements in agricultural futures markets and practical insights for managing investment portfolios and enhancing food market stability during crises.

The Omnibus Law on Job Creation and its potential implications for rural youth and future farming in Indonesia

Indonesian rural youth face challenges accessing farmland and sustaining an agricultural livelihood while their labour is not necessarily absorbed by other sectors. In that context, the Omnibus Law on Job Creation (Law 11/2020) promises to liberalise trade and investment across multiple sectors, including agriculture and food security. Combining legal research and political economy approaches to youth and agrarian challenges, we identify amendments to legislation that reduce safeguards for the environment, workers' and farmers' rights and their livelihoods. If fully implemented, the legislative amendments could further narrow youth's options both for secure formal work and futures in farming by accelerating the expansion of infrastructure, industrial plantations and extractive industries that utilise low‐wage labour and huge areas of land. This exposes inconsistencies in the government's approach to increase future food security by promoting intensification of agriculture and attracting youth to farming, while enabling agro‐ and resource extraction that absorbs land yet offers limited and precarious employment prospects.

Agrarökonomie und Agrarpolitik: Herausforderungen und Lösungsansätze

Abstract The planned cuts to agriculture in the wake of the budget crisis have sparked fierce protests across Germany. Although the proposed cuts were not very large in themselves, some speculated that they would finally “break the camel’s back”. The authors of this Zeitgespräch shed light on the development to date, the current situation and the future of agriculture in Germany and also critically assess the agricultural policy measures in Germany and the EU. The German agricultural industry is facing a major transformation and the question arises as to how it can be managed.