Smart Farming for Sustainable Rice Production:An Insight into Application,Challenge,and Future Prospect

Rice has a huge impact on socio-economic growth,and ensuring its sustainability and optimal utilization is vital.This review provides an insight into the role of smart farming in enhancing rice productivity.The applications of smart farming in rice production including yield estimation,smart irrigation systems,monitoring disease and growth,and predicting rice quality and classifications are highlighted.The challenges of smart farming in sustainable rice production to enhance the understanding of researchers,policymakers,and stakeholders are discussed.Numerous efforts have been exerted to combat the issues in rice production in order to promote rice sector development.The effective implementation of smart farming in rice production has been facilitated by various technical advancements,particularly the integration of the Internet of Things and artificial intelligence.The future prospects of smart farming in transforming existing rice production practices are also elucidated.Through the utilization of smart farming,the rice industry can attain sustainable and resilient production systems that could mitigate environmental impact and safeguard food security.Thus,the rice industry holds a bright future in transforming current rice production practices into a new outlook in rice smart farming development.

Technical efficiency of cocoa farms at varying elevation levels in Davao City, Philippines: implications to sustainable upland farming systems

Elevation is one of many components that influence agriculture, and this in turn affects the level of both inputs and outputs of farmers. This article focuses on the productivity and technical efficiency of 100 cocoa farms using cross-sectional data from areas ranging from 190 to 1021 m above sea level which were classified as low, medium, and high elevation in Davao City, considered as the chocolate capital of the Philippines. Using stochastic frontier analysis, the results showed that the cost of inputs per ha and the number of cocoa trees per ha significantly increase yield. Farms at high elevations were less technically efficient, as this entails lower temperatures and increased rainfall, and cocoa farming in those areas and conditions can be more challenging, especially with changes in farming practices, terrain, and distance to markets. Other significant variables were age of cocoa farms, married farmers, and age of the farmers. Older farms may be more developed, farmers who are married benefit from their spouses being able to readily contribute as farm labor, and lastly, older farmers' inefficiency may likely stem from nonadaptation of newer farming practices. With an average technical efficiency of 0.61, 0.63, and 0.26 in low, medium, and high elevation areas, respectively, farmers therefore have an incentive to improve farm practices and consider topographical variations found in high elevation areas. Recommendations for the improvement of technical efficiency of cocoa farms are better connectivity to markets, enhancing farm practices, and continuation and improvement of government programs on cocoa with an added emphasis on research. For farmers in high elevation areas, mitigating solutions such as sustainable agriculture practices and ecolabelling are key to improving efficiency and minimizing the potential negative impact on upland farming systems. Moreover, such adaptation measures may also contribute to sustainability of cocoa farming in high elevation areas.

Agricultural water-saving potential and feasibility of developing semi-dryland farming in Henan Province

Based on the collected data in the current status of developing and utilizing water resources and imple- menting water-saving agriculture in Henan Province, and taking into account the influence of engineering, agro- nomic and management measures, the water-saving potential in past years and the feasibility of implementing semi-dryland farming were analyzed in Henan Province. Finally, specific technical measures of developing semi- drvland farming in different areas of Henan Province were orooosed.

The role of time preferences in contract breach:Evidence from Chinese poultry farmers participating in contract farming

Farmers’contract breach behavior is cited as one of the major stumbling blocks in the sustainable expansion of contract farming in many developing countries.This paper examines farmers’contract breach decisions from the perspective of time preferences.The empirical analysis is based on a household survey and economic field experiments of poultry households participating in contract farming conducted in Jiangsu Province,China.A discounted utility model and a maximum likelihood technique are applied to estimate farmers’time preferences and the effect of time preferences on contract breach in the production and sales phases are explored with a bivariate probit model.The results show that,on average,the poultry farmers in the sample are generally present biased and impatient regarding future utility.The regression results show that farmers with a higher preference for the present and a higher discount rate are more likely to breach contracts,and time preferences play a greater role in the production phase than in the sales phase.When considering heterogeneity,specific investments and transaction costs promote contract stability only for farmers with a low degree of impatience.Moreover,compared with large-scale farmers,small-scale farmers’contract breach decisions are more significantly affected by their time preferences.These results have implications for contract stability policies and other issues that are impacted by the linking of behavioral preferences to agricultural decisions.

Influences of large-scale farming on carbon emissions from cropping:Evidence from China

Reducing agricultural carbon emissions is important to enable carbon emission peaking by 2030 in China.However,China's transformation towards large-scale farming brings uncertainties to carbon emission reduction.This study quantifies the carbon emissions from cropping based on life cycle assessment and estimates the effects of farm size on carbon emissions using a fixed effects model.Furthermore,the variations of the carbon emissions from cropping driven by the changes in farm size in future years are projected through scenario analysis.Results demonstrate an inverted U-shaped change in total carbon emission from cropping as farm size increases,which is dominated by the changes in the carbon emission from fertilizer.Projections illustrate that large-scale farming transformation will postpone the peak year of total carbon emission from cropping until 2048 if the change in farm size follows a historical trend,although it is conducive to reducing total carbon emission in the long run.The findings indicate that environmental regulations to reduce fertilizer usages should be strengthened for carbon emission abatement in the early stage of large-scale farming transformation,which are also informative to other developing countries with small farm size.

Effect of Combined Application of Organic Farming Aid (OFA) and Inorganic Fertilizers on the Growth and Yield of Maize and Soil Microbial Properties in the Guinea Savannah Agro-Ecological Zone of Ghana

The aim of the study was to evaluate the effect of different rates of organic farming aid (OFA) and inorganic fertilizer on the productivity of maize, chemical and microbial properties of soil for higher economic value. Field experiments were conducted during the 2020 and 2021 cropping seasons at the research fields of CSIR-Savanna Agricultural Research Institute located at Nyankpala in the Guinea savannah agroecology of Ghana. The study consisted of five treatment combinations: full rate of OFA, full rate of NPK, 1/2 OFA + 1/2 NPK, full OFA + 1/2 NPK and a control (no OFA and no NPK) which were arranged in a randomized complete block design with four replications. Analysis of variance indicated significant (P 0.05) treatment and year interaction effect for all the growth parameters except for plant height, leaf area and leaf area index. Apart from hundred seed weight, treatment and year interaction effect for all the yield and yield components was significant (P 0.05). Application of full rate of NPK (90:60:60) resulted in the highest grain yield of 4960 kg·ha-1, however it was statistically similar to those obtained by the combined application of full rate of OFA (250 ml·ha-1) + 1/2 NPK and 1/2 OFA + 1/2 NPK with grain yield of 4856 kg·ha-1 and 4639 kg·ha-1 respectively. There was also a yield advantage of 197. 5%, 191.3%, 178.3 and 79.1% over the control for full NPK rate, full OFA rate + 1/2 NPK rate, 1/2 OFA rate + 1/2 NPK and full OFA rate respectively. Application of full OFA rate + 1/2 NPK enhanced soil basal respiration (evolved CO2) and mineralizable C, implying that, combination of full OFA rate and NPK fertilizer would be necessary to boost soil microbial activity and soil labile nutrient pool (labile C pool). This suggests that combined use of full OFA rate + 1/2 NPK fertilizer can be a better strategic tool for improving soil quality. The highest benefit cost ratios (BCR) of 2.58 and 3.77 were obtained following the application of full OFA rate + 1/2 NPK and 1/2 OFA rate + 1/2 NPK respectively. Hence, it could be concluded that complementary use of OFA and NPK is more profitable than using single inputs (either OFA or NPK). Thus, in promoting technology packages to farmers, development practitioners must carefully consider the complementary of inputs that are cost-effective but economically rewarding.

Information-Centric IoT-Based Smart Farming with Dynamic Data Optimization

Smart farming has become a strategic approach of sustainable agriculture management and monitoring with the infrastructure to exploit modern technologies,including big data,the cloud,and the Internet of Things(IoT).Many researchers try to integrate IoT-based smart farming on cloud platforms effectively.They define various frameworks on smart farming and monitoring system and still lacks to define effective data management schemes.Since IoT-cloud systems involve massive structured and unstructured data,data optimization comes into the picture.Hence,this research designs an Information-Centric IoT-based Smart Farming with Dynamic Data Optimization(ICISF-DDO),which enhances the performance of the smart farming infrastructure with minimal energy consumption and improved lifetime.Here,a conceptual framework of the proposed scheme and statistical design model has beenwell defined.The information storage and management with DDO has been expanded individually to show the effective use of membership parameters in data optimization.The simulation outcomes state that the proposed ICISF-DDO can surpass existing smart farming systems with a data optimization ratio of 97.71%,reliability ratio of 98.63%,a coverage ratio of 99.67%,least sensor error rate of 8.96%,and efficient energy consumption ratio of 4.84%.

Gender Perspectives of Vulnerability to Climate Change: A Descriptive Evidence from Farming Households at Ikpayongo Community in Gwer Lga, Benue State, Nigeria

Climate change impact and risks on agricultural livelihood affect women and men disproportionately and often to the disadvantage of women and girls. Consequently, this study assessed gender perspectives of vulnerability to climate change of farming households at Ikpayongo community in Gwer local government area, Benue State, Nigeria using descriptive approach. The study identified a total of 120 male-headed and female-headed farming households across four neighbourhoods and administered structured questionnaire on them using simple random sampling method, while data analysis was done using descriptive statistics. The results indicate lower education and income status among female-headed households, though male-headed households have high household size. Both sexes have relatively equal access to land for farming, however men have large farm size compared to women. The major crops cultivated by men were rice and yam, while women cultivated largely groundnut and cassava. Women are more exposed and sensitive to climate-related hazards such as floods and heat stress due to the location of their farms. The result further shows that males possess better adaptive capacity given their higher incomes, social networks and more access to training/capacity building programmes and credit facilities. The study concludes that female-headed farming households are more vulnerable to climate change and variability than male-headed farming households due to higher exposure and a lower adaptive capacity. Programme and policies to improve women access to credit facilities and relevant training to boost their adaptive capacity and build resilience are highly recommended. This would also limit exposure with attendant reduction in vulnerability.

Can Natural Farming Help to Combat Climate Variability? A Comparison of Natural and Chemical Farming in Andhra Pradesh, India

A study was conducted to compare the potential of natural farming versus conventional chemical farming to withstand adverse climate effects. The study investigated two cyclones, namely cyclone Pethai and cyclone Titli of 2018, which caused significant damage to Andhra Pradesh’s coastal corridor. In addition, the impact of heavy rainfall in 2021 on two different farming systems was studied. The worst-affected Paddy crop regions where these cyclones caused severe damage were surveyed. Multiple linear regression was utilized to investigate predictors including crop damage, wind damage, submergence, and yield loss in these two agricultural systems during these two cyclonic and heavy rainfall events. The study indicated that natural farming practices were more resilient to climate variability than conventional chemical farming techniques. The study showed a statistically significant difference (p < 0.001) between these two farming systems in terms of damage caused due to climate variabilities.

Enhanced Water Quality Control Based on Predictive Optimization for Smart Fish Farming

The requirement for high-quality seafood is a global challenge in today’s world due to climate change and natural resource limitations.Internet of Things(IoT)based Modern fish farming systems can significantly optimize seafood production by minimizing resource utilization and improving healthy fish production.This objective requires intensive monitoring,prediction,and control by optimizing leading factors that impact fish growth,including temperature,the potential of hydrogen(pH),water level,and feeding rate.This paper proposes the IoT based predictive optimization approach for efficient control and energy utilization in smart fish farming.The proposed fish farm control mechanism has a predictive optimization to deal with water quality control and efficient energy consumption problems.Fish farm indoor and outdoor values are applied to predict the water quality parameters,whereas a novel objective function is proposed to achieve an optimal fish growth environment based on predicted parameters.Fuzzy logic control is utilized to calculate control parameters for IoT actuators based on predictive optimal water quality parameters by minimizing energy consumption.To evaluate the efficiency of the proposed system,the overall approach has been deployed to the fish tank as a case study,and a number of experiments have been carried out.The results show that the predictive optimization module allowed the water quality parameters to be maintained at the optimal level with nearly 30%of energy efficiency at the maximum actuator control rate compared with other control levels.

Current Implementation and Experience of Natural Farming in Japan

The natural farming in Japan mainly includes Mokich Okadas natural farming theory and Masanobu Fukuokas natural farming theory in terms of genre.Natural farming was proposed and implemented to deal with the anabatic soil degradation and food safety problems.Among themꎬMokich Okadas natural farming theory advocates the restoration of the agricultural tradition of the past and combination with modern agricultural science and technologyꎻMasanobu Fukuokas natural farming theory advocates a complete return to natural and traditional farming and puts forward a set of green agricultural cultivation methods focusing on injurious insect control with beneficial insectꎬweed control with green herbaceous plantsꎬsoil reclamation with grassꎬnontoxic and cost￣reducing method based on the Japanese ancient agricultural books.Their natural farming theories have been widely popularized and applied in Japanꎬand its successful experience is worthy of study and reference for China.

Cultivation of Core Competencies of History in Middle Schools:Taking the Lesson of Primitive Farming Life as an Example

The cultivation of history teaching in middle schools involves many aspects and multi-level teaching factors such as classroom teaching objectives,teaching methods and teaching means.Taking the Lesson of Primitive Farming Life in the seventh grade volume 1 of People’s Education Press as an example,this paper discussed the cultivation of core competencies of history in middle schools,to improve the implementation process of history courses and promote the cultivation of students’discipline competencies.

On the Rice Farming in the Shu Area During the Can Cong Period

The Bashu area was one of the most civilized agricultural areas in ancient China.Since the late Eastern Han Dynasty,it has been reputed as the“Land of Abundance”due to its abundant resources.Archaeological data show that the Shu area cultivated millet as early as the prehistoric period.The legend goes that Can Cong was the first ruler of the Shu area during the pre-Qin period,indicating that agricultural production at that time had made great progress compared with the prehistoric period.During this period,the agricultural pattern of the Shu area experienced profound changes,from dryland millet farming originating from the Western Sichuan Plateau to rice farming that is more suitable for Sichuan Basin,especially for the Chengdu Plain,where there are abundant rainfalls and high temperatures.Since then,rice farming has become a long-established farming tradition in the Shu area.The agricultural production tools and grain crop seeds unearthed from the Baodun site,where the archaeological remains of Can Cong Culture were discovered,show the prosperity of early agriculture in the Shu area.During the Baodun Culture period,the accelerated economic and social development in the Shu area promoted complicated and hierarchical changes in the social organization structure of settlements,which gradually evolved into early states.

Mechanized cover crop farming:Modern methods,equipment and technologies

Cover crops are important for covering the soil and fixing nutrients in order to manage soil erosion and improve soil quality.This paper discusses some of the modern methods,equipment and technologies used in carrying out cover crop farming.This is important because modern agricultural and farming operations work a lot differently from those of the past as a result of the improvements in technological advancements over the past decades.The paper explains the usefulness of the various classes of cover crops known as grasses,legumes,brassicas and non-legume broadleaves.It also explains the role of smart farming technologies such as artificial intelligence and big data,satellites,drones and robotic systems as well as Internet of Things(IoT).Some of the benefits of these advanced technologies are high crop productivity,efficient use of water,fertilizer and pesticides,and improvement in farmer safety and growing conditions,as well as reduced impact on the natural environment and ecosystem.Mechanized cover crop farming equipment are also discussed,and grouped into preparation,planting,termination and incorporation equipment,based on the stage they are used during the cover crop farming process.Clearly,these advanced equipment and technologies are aiding the cover crop farming process by making it safer and environmentally friendly while helping farmers to be more efficient.

The Impacts and Causes of Land Fragmentation on Farm Productivity: Case Review of East African Countries

This report provides an overall assessment of land fragmentation problems in East Africa. Many parts of East Africa have become highly fragmented, putting development systems and activities in these areas at risk of complete collapse. Land fragmentation occurs when land gets converted for agriculture, industrialization, or urbanization, invaded by non-local plants, or enclosed for individual use and by subdividing farmlands into subsequent smaller units called parcels with varying average farm sizes. Fragmentation results from inappropriate agricultural development processes and ineffective land use planning that fails to recognize how farmland is used, and the importance of its interconnected areas. Insecurity of tenure and resource rights are key factors in making this possible. Land fragmentation is one of the key reasons why the ability of most resources in East Africa becomes scarcer, and those remaining become “privatized” by more powerful community members—keen to maintain their access to them. Such individualistic attitudes are new and disadvantage the poorest even further by affecting the traditional customary safety nets and agricultural outputs. Neither the government nor customary governance systems effectively protect resource access for the poorest. This review summary report identifies the key causes, measures, and implications, government interventions, and the common remedies to land fragmentation problems in the East African Countries of Kenya, Uganda, Rwanda, and Tanzania including neighboring Ethiopia, and the Sudan. The findings indicated from 2005 to 2015, the population kept increasing for all the named countries in East Africa with Rwanda and Uganda having a substantial increase in population density. The study review further explores the trend in the performance of agriculture by average farm sizes within the intervals of five years by highlighting their strong linkages and found that the average farm size has declined drastically, especially for Kenya. This can only mean that small farms kept becoming smaller and smaller and that there were more small-scale farmers. The results further depicted that the major and commonly cultivated food crops among the East African countries include maize, sorghum, rice, cassava, sweet potatoes, bananas, Irish potatoes, beans, peas, etc., with maize yields (Mt/ha) in 2003 for Uganda being the highest (1.79 Mt/ha) and the lowest in Rwanda (0.77 Mt/ha) respectively. Therefore, from the review results, recommendations are being made as to how the negative impacts of land fragmentation on agricultural productivity can be reduced or mitigated. One way is by community sensitization and awareness about the importance of land consolidation and its proposition on farm productivity.

Wind farms increase land surface temperature and reduce vegetation productivity in the Inner Mongolia

Wind power has been developing rapidly as a key measure to mitigate human-driven global warming.The under-standing of the development and impacts of wind farms on local climate and vegetation is of great importance for their rational use but is still limited.In this study,we combined remote sensing and on-site investigations to identify wind farm locations in Inner Mongolia and performed landscape pattern analyses using Fragstats.We explored the impacts of wind farms on land surface temperature(LST)and vegetation net primary productivity(NPP)between 1990 and 2020 by contrasting these metrics in wind farms with those in non-wind farm areas.The results showed that the area of wind farms increased rapidly from 1.2 km2 in 1990 to 10,755 km2 in 2020.Spatially,wind farms are mainly clustered in three aggregation areas in the center.Further,wind farms increased nighttime LST,with a mean value of 0.23℃,but had minor impacts on the daytime LST.Moreover,wind farms caused a decline in NPP,especially over forest areas,with an average reduction of 12.37 GC/m^(2).Given the impact of wind farms on LST and NPP,we suggest that the development of wind farms should fully consider their direct and potential impacts.This study provides scientific guidance on the spatial pattern of future wind farms.

Farm buildings and agri-food transitions in Southern France:Mapping dynamics using a stakeholder-based diagnosis

This study's goal is to present a dynamic portrait of the farm-buildings environment in Occitania,in Southern France,in order to better identify the transitions underway in agri-food chains.To this end,we undertook a ter-ritorial diagnosis based on actor statements,using 28 semi-structured interviews across Occitania.This diagnosis was enriched by graphic modelling,which enabled the spatialization of the dynamics described.We show that the process of standardisation of farm buildings prevails in the majority of the territories studied.This phenomenon has intensified in recent years with the development of vast photovoltaic-roofed sheds,accentuating the farm-land conversion and soil sealing.At the same time,in areas with strong environmental,landscape and heritage contexts,a'new adventure in farm buildings'(2022 survey)is taking shape.It is primarily driven by local short food chains,which rely on self-construction,repurposing and refurbishment,the sharing of tools and equipment,and which favour the use and reuse of local resources.This study shows that farm-buildings dynamics crystallise many challenges confronting the reterritorialisation of agriculture and food production.

Mobile genetic elements facilitate the transmission of antibiotic resistance genes in multidrug-resistant Enterobacteriaceae from duck farms

Multidrug-resistant(MDR)Enterobacteriaceae critically threaten duck farming and public health.The phenotypes,genotypes,and associated mobile genetic elements(MGEs)of MDR Enterobacteriaceae isolated from 6 duck farms in Zhejiang Province,China,were investigated.A total of 215 isolates were identified as Escherichia coli(64.65%),Klebsiella pneumoniae(12.09%),Proteus mirabilis(10.23%),Salmonella(8.84%),and Enterobacter cloacae(4.19%).Meanwhile,all isolates were resistant to at least two antibiotics.Most isolates carried tet(A)(85.12%),blaTEM(78.60%)and sul1(67.44%)resistance genes.Gene co-occurrence analysis showed that the resistance genes were associated with IS26 and integrons.A conjugative IncFII plasmid pSDM004 containing all the above MGEs was detected in Proteus mirabilis isolate SDM004.This isolate was resistant to 18 antibiotics and carried the blaNDM-5 gene.MGEs,especially plasmids,are the primary antibiotic resistance gene transmission route in duck farms.These findings provide a theoretical basis for the rational use of antibiotics in farms which are substantial for evaluating public health and food safety.

Transient Stability Preventive Control of Wind Farm Connected Power System Considering the Uncertainty

To address uncertainty as well as transient stability constraints simultaneously in the preventive control of windfarm systems, a novel three-stage optimization strategy is established in this paper. In the first stage, the probabilisticmulti-objective particle swarm optimization based on the point estimate method is employed to cope with thestochastic factors. The transient security region of the system is accurately ensured by the interior point methodin the second stage. Finally, the verification of the final optimal objectives and satisfied constraints are enforcedin the last stage. Furthermore, the proposed strategy is a general framework that can combine other optimizationalgorithms. The proposed methodology is tested on the modified WSCC 9-bus system and the New England 39-bussystem. The results verify the feasibility of the method.

GNN Representation Learning and Multi-Objective Variable Neighborhood Search Algorithm for Wind Farm Layout Optimization

With the increasing demand for electrical services,wind farm layout optimization has been one of the biggest challenges that we have to deal with.Despite the promising performance of the heuristic algorithm on the route network design problem,the expressive capability and search performance of the algorithm on multi-objective problems remain unexplored.In this paper,the wind farm layout optimization problem is defined.Then,a multi-objective algorithm based on Graph Neural Network(GNN)and Variable Neighborhood Search(VNS)algorithm is proposed.GNN provides the basis representations for the following search algorithm so that the expressiveness and search accuracy of the algorithm can be improved.The multi-objective VNS algorithm is put forward by combining it with the multi-objective optimization algorithm to solve the problem with multiple objectives.The proposed algorithm is applied to the 18-node simulation example to evaluate the feasibility and practicality of the developed optimization strategy.The experiment on the simulation example shows that the proposed algorithm yields a reduction of 6.1% in Point of Common Coupling(PCC)over the current state-of-the-art algorithm,which means that the proposed algorithm designs a layout that improves the quality of the power supply by 6.1%at the same cost.The ablation experiments show that the proposed algorithm improves the power quality by more than 8.6% and 7.8% compared to both the original VNS algorithm and the multi-objective VNS algorithm.