Agricultural Technology Innovation Research Articles

Climate change, agricultural transformation and climate smart agriculture development in China

Against the backdrop of increasingly severe global climate change, it has become an inevitable choice to promote the transformation of agriculture oriented to climate smart agriculture. The objective of this paper is to demonstrate realistic problem faced by China's agricultural transformation and identify key factors affecting agricultural development under the background of climate change. The paper constructs a linear econometric model and employes time series data from 1990 to 2019 to empirically test the impact of climate change and agricultural investment on agriculture in China. The results indicate that climate change has a negative impact on agriculture, while agricultural investment has a positive impact. Therefore, it is necessary to promote agricultural transformation oriented to climate smart agriculture. To this end, China must vigorously promote agricultural system reform, accelerate agricultural technological innovation, accelerate the development of agricultural big data and informatization, and strengthen financial support for agriculture.

Urban–Rural Integration and Agricultural Technology Innovation: Evidence from China

Urban and rural relations are important for the sustainable development of a nation or region. Urban and rural integration (URI), as a conceptual framework and strategic tool for managing urban–rural relations, is supposed to play a role in linking urban and rural socio-economic systems to promote the two-way flow of resources, facilitate spatial restructuring and functional transformation, enhance resource allocation efficiency, and shorten the gap between urban and rural areas. This article takes agriculture technological innovation in China as an example, and discuss whether and how URI can promote agricultural technological innovation (ATI). Based on a dataset of 288 prefecture-level cities spanning the years from 1999 to 2018, this article analyzes the mechanism and effect of URI on the development of ATI by using the negative binomial regression model and other models based on measuring the level of URI in the municipal area. The main conclusions are as follows. (1) URI positively promotes the development of ATI and exhibits significant heterogeneity across various dimensions, regions, and agricultural production areas. (2) The mechanism analysis shows that governance systems and mature markets positively moderate the impact of URI on promoting ATI, which also exhibits heterogeneous characteristics across various regions. (3) The impact of URI on the development of ATI exhibits a double threshold effect, and its marginal impact on the development of ATI increases with the deepening of URI. Moreover, the threshold effect of URI demonstrates significant heterogeneity. In central and urban areas, after surpassing the threshold value, the influence of URI on agricultural technological innovation becomes greater. Therefore, it is crucial to continuously deepen the development of URI, smooth the channels of factor flow, enhance resource sharing, break down the urban and rural barriers, and create a new engine for ATI development.

An Evaluation Scheme Driven by Science and Technological Innovation—A Study on the Coupling and Coordination of the Agricultural Science and Technology Innovation-Economy-Ecology Complex System in the Yangtze River Basin of China

This study focuses on 19 provinces in the Yangtze River Basin of China. It gathers relevant data indicators from 2010 to 2021 and constructs an evaluation index system centered on agricultural science and technology innovation. The study evaluates the relationship between agricultural “science and technology innovation-economy-ecology” systems and identifies key obstacle factors using the obstacle degree model. The study draws the following conclusions: Firstly, the comprehensive development level index of the agricultural science and technology innovation system shows an overall linear upward trend (values range from 0.121 to 0.382). Secondly, the comprehensive development level index of the agricultural economic system exhibits an upward trend but with a relatively small overall magnitude (values range from 0.248 to 0.322). Thirdly, the comprehensive development level index of the agricultural ecological system demonstrates significant overall fluctuations, with notable regional disparities (values range from 0.384 to 0.414). Fourthly, the overall agricultural SEE (Science and technological innovation, Economy, Ecology) complex system exhibits a characteristic of “high coupling, low coordination”, identifying the main obstacle factors influencing agricultural SEECS based on a formulated approach. Subsequently, the following policy recommendations are proposed: Firstly, enhance the agricultural technological innovation system and promote green and efficient agricultural technology research and development. Secondly, to accelerate the transformation and upgrading of modern agriculture, achieving green and high-quality development of the agricultural economy. Thirdly, to strengthen agricultural ecological environment protection, laying a solid foundation for the healthy and sustainable development of agriculture.

THE REALITY OF DATE PRODUCTION IN ALGERIA AND ITS RELATIONSHIP WITH NATIONAL INCOME: AN ECONOMETRIC STUDY USING THE AUTOREGRESSIVE DISTRIBUTED LAG (ARDL) MODEL DURING THE PERIOD 2000-2019

Objective: The objective of this study is to investigate the evolution of date production in Algeria and its impact on the country's national income (GDP) from 2000 to 2019. This research aims to analyze the short-term and long-term relationships between date production and national income, assessing the significance of agricultural policies in enhancing the productivity and economic contribution of the date sector. Ultimately, the study seeks to highlight the role of date production in diversifying Algeria's exports and contributing to sustainable economic growth. Theoretical Framework: This study explores the relationship between date production and national income in Algeria, using agricultural productivity theory, endogenous growth theory, export diversification theory, and the ARDL model. It aims to understand the impact of agricultural policies, human capital investment, and export diversification on Algeria's date production, highlighting the potential for economic diversification. Method: This study analyzes the relationship between date production and Algeria's GDP from 2000 to 2019, using the Autoregressive Distributed Lag (ARDL) model. Data sources include Algeria's GDP and date production data. The study uses unit root tests, bounds testing, and error correction models to estimate the relationship. Statistical software like EViews or R is used for analysis. Results and Discussion: The study reveals a positive relationship between date production and national income in Algeria from 2000 to 2019. The data suggests that agricultural policies have led to an upward trajectory in date production, while GDP has grown significantly. The study also highlights the importance of date production in Algeria's economy, particularly in diversifying exports. The long-term relationship suggests sustained investment in date production will yield continued benefits for national income. Research Implications: The study highlights the importance of supporting agricultural policies that enhance productivity in Algeria's date sector. It suggests broader export diversification strategies, infrastructure development, and economic development. Strengthening date production can create job opportunities, stimulate rural development, and contribute to food security. Future research should explore other agricultural products, climate change, and technological innovations to improve yields and sustainability in the sector. Originality/Value: The study explores Algeria's date production and national income relationship, providing insights for policymakers to diversify the economy beyond hydrocarbons and foster sustainable growth.

Stimulating innovation or enhancing productivity? The impact of environmental regulations on agricultural green growth

A comprehensive understanding of the impact of environmental regulations on agricultural green growth is crucial for formulating appropriate policy designs and achieving effective climate change mitigation. This study, based on the Porter Hypothesis framework, aims to propose our hypotheses in the influence of environmental regulations on agricultural green growth. This study first utilizes the Super-SBM model combined with the global Malmquist-Luenberger index to measure agricultural green growth in G20 countries. Subsequently, the study analyzes the impact of environmental regulations on agricultural technological innovation and green growth, highlighting variations among countries with different development levels. The results indicate: (1) Significant differences exist in agricultural green growth among G20 countries, generally exhibiting an increasing trend. (2) Environmental regulations have a significant impact on both agricultural green growth and agricultural technological innovation, but the effects of different types of environmental regulations are various. Furthermore, different conclusions are observed for countries with heterogeneous development levels. (3) There is a lag effect in the impact of environmental regulations on agricultural green growth and technological innovation, highlighting the importance of pre-establishing an environmental policy framework. (4) An inverted U-shape relationship exists between environmental regulations and agricultural green growth, while no such relationship is found in agricultural technological innovation.

Driving Effects and Spatial Spillover of Technological Innovation on the Carbon Balance of Agriculture

<p style="text-align:justify"><span style="font-size:10.5pt"><span style="line-height:150%"><span style="font-family:Calibri,sans-serif"><span lang="EN-US" style="font-size:12.0pt"><span style="line-height:150%"><span style="font-family:"Times New Roman",serif">There is significant controversy over the overall effects of technological innovation on carbon balance. Based on panel data of agriculture from 28 provinces in China from 2009 to 2021, utilizing ArcGIS and spatial Durbin model, the spatial-temporal patterns of agricultural technological innovation and carbon balance are analyzed, and furtherly the spatial effects of technological innovation on carbon balance are examined. The results indicate: (1) the overall agricultural carbon surplus in Chinese provinces has been continuously increasing from 2009 to 2021; (2) the direct effect of technological innovation on the carbon surplus in agriculture is significantly positive, while the spatial spillover effect is significantly negative; (3) the mediating effect results show that technological innovation affects the output of the agricultural sector and thereby influences agricultural carbon balance; (4) the impact of technological innovation on agricultural carbon balance exhibits an asymmetric spatial spillover effect. Advantageous regions suffer from the positive effects of spatial spillover, while non-advantageous regions benefit from the negative impacts of spillover, resulting in an overall negative effect. This study's findings play an important role in addressing the existing controversy over the overall effects of technological innovation on carbon balance and provide important insights for formulating policies on low-carbon technological innovation in agriculture.</span></span></span></span></span></span></p>

Agricultural Science and Technology Innovation and Agricultural Green Development: China’s Experience

Agricultural Science and Technology Innovation and Agricultural Green Development: China’s Experience

Chatbot for Promoting Best Crop Management Practices to Rice Farmers in Odisha, India

The increasing penetration of mobile phones has made it easier to reach remote farmers and bridge the knowledge gap between them and researchers/extension workers and agricultural stakeholders. Farmers need concise, context-specific, information to address the challenges which arise due to lack of near-real time updates. Scaling up innovative agricultural technologies is essential to ensure these advancements reach the farmers for their benefit. Chatbots represent an informative tool for delivering the latest agricultural developments to farmers in a user-friendly and interactive format. Chatbots can deliver information on best agronomic practices, weather forecast, market information thereby improving the decision-making capacity of farmers. This paper discusses the development of a rule-based chatbot under a pilot-study and presents findings from a user study conducted with 102 farmers in Odisha, India in 2021. The content topics were decided using a participatory approach wherein farmers selected the topic of interests and information required. The content was developed using the digital tools Rice Crop Manager (RCM), Seed Cast and Rice Knowledge Bank (RKB), developed and deployed by International Rice Research Institute (IRRI). The chatbot is hosted on Facebook messenger platform and needs the internet support to operate. Farmers having this app can use the chatbot without any additional expense. The results indicate that farmers acknowledged and appreciated interactive features of chatbot and the content but expressed a desire for more comprehensive information on insect-pest management and other crop management practices. The findings highlight the potential of chatbot as a complementary channel to disseminate agro advisories, complementing the existing agricultural extension services. The study has its limitations in terms of small sample size which may or may not represent the variability of the entire population of the area.

Key Technologies for Autonomous Fruit- and Vegetable-Picking Robots: A Review

With the rapid pace of urbanization, a significant number of rural laborers are migrating to cities, leading to a severe shortage of agricultural labor. Consequently, the modernization of agriculture has become a priority. Autonomous picking robots represent a crucial component of agricultural technological innovation, and their development drives progress across the entire agricultural sector. This paper reviews the current state of research on fruit- and vegetable-picking robots, focusing on key aspects such as the vision system sensors, target detection, localization, and the design of end-effectors. Commonly used target recognition algorithms, including image segmentation and deep learning-based neural networks, are introduced. The challenges of target recognition and localization in complex environments, such as those caused by branch and leaf obstruction, fruit overlap, and oscillation in natural settings, are analyzed. Additionally, the characteristics of the three main types of end-effectors—clamping, suction, and cutting—are discussed, along with an analysis of the advantages and disadvantages of each design. The limitations of current agricultural picking robots are summarized, taking into account the complexity of operation, research and development costs, as well as the efficiency and speed of picking. Finally, the paper offers a perspective on the future of picking robots, addressing aspects such as environmental adaptability, functional diversity, innovation and technological convergence, as well as policy and farm management.

Impacts and mechanisms of digital village construction on agricultural green total factor productivity

Green is the foundation of agricultural development. By leveraging modern information technology, digital village construction injects new momentum into the green growth of agriculture. Using balanced panel data from 31 provinces in China from 2011 to 2022, this paper employs the entropy approach and SBM-GML index to measure the indicators of digital village construction and agricultural green total factor productivity (AGTFP). The impacts and mechanisms of digital village construction on AGTFP are examined through fixed effect, mediating effect, and threshold models. The findings are as follows: (1) AGTFP in China shows an overall increasing trend from 2011 to 2022, with significant growth in the western region compared to the eastern and central regions. (2) Digital village construction significantly accelerates the improvement of AGTFP, a conclusion supported by robustness tests. (3) Mechanism analysis indicates that digital village construction advances AGTFP by fostering agricultural technology innovation, enhancing agricultural human capital, and improving agricultural productive services. (4) Digital village construction has a more significant impact on major grain-producing areas, economically underdeveloped areas, and northern region compared to other regions. (5) Environmental regulation acts as a threshold effect on the impact of digital village construction on AGTFP. Digital village construction substantially impacts AGTFP when environmental regulation is below the threshold, with the positive effect continuing beyond this point but at a reduced intensity.

Digital inclusive finance and agricultural green development in China: A panel analysis (2013–2022)

Digital inclusive finance breaks financial barriers to promote agricultural green development in rural areas. We investigate the impact of digital inclusive finance on agricultural green development based on panel data from 2013 to 2022 in China. The results show that digital inclusive finance can promote the agricultural green development by improving agricultural technology innovation and alleviating agriculture-related financing constraints. The coverage breadth of digital inclusive finance has the greatest effect on agricultural green development, and the promoting effect of digital inclusive finance on agricultural green development is more pronounced in the Eastern region and in non-major grain producing areas. Overall, our findings provide timely policy evaluations on new policy trends in China.

Exploring the "Double-Edged Sword" effect of the digital economy on sustainable agricultural development: Evidence from China

The prolonged reliance on excessive resource utilization and unsustainable production methods has resulted in significant environmental issues in China, posing serious challenges to the sustainable development of agriculture. While the digital economy offers new opportunities for enhancing agricultural sustainability, its rapid and unregulated growth could also lead to negative effects. Therefore, exploring the relationship between the digital economy and sustainable agriculture development (SAD), along with clarifying the mechanisms involved, is of paramount importance. This study uses the EBM-GML index to calculate AGTFP as a representation of SAD and empirically tests the impact and mechanism of the digital economy on SAD. Additionally, it explores the moderating role of government behavior, specifically Agricultural fiscal support and digital policies. The main findings are as follows: (1) During the observation period, SAD showed a steady upward trend, but regional disparities widened, forming a contiguous agglomeration pattern. The decentralization of the digital economy weakened, trending towards unipolarization and homogenization, with a north-to-south diffusion. (2) The impact of the digital economy on SAD presents an inverted "U" shape, indicating a "double-edged sword" effect. This relationship remains significant after robustness and endogeneity tests. Additionally, the digital economy exhibits significant spatial effects, maintaining an inverted U-shaped relationship with SAD in neighboring areas. (3) Regional heterogeneity shows that coastal provinces are in digital economy suppression or alert areas, while inland regions are in enhancement areas. During the study period, the number of suppression and alert areas did not significantly increase, but there was a trend of shifting from coastal to inland regions. (4) When below the threshold, the digital economy promotes agricultural green technology innovation, optimizes resource allocation, and fosters synergetic agglomeration of agriculture with the secondary and tertiary industries, enhancing SAD. However, surpassing the threshold negatively impacts these mechanisms. (5) Fiscal support for agriculture smooths the inverted "U" curve, mitigating the negative impact of excessive digital economy growth on SAD. Conversely, digital policies steepen the inverted "U" curve, exacerbating these negative effects. The findings highlight the need for balanced digital economic development to support sustainable agricultural growth.

Impacts and threshold effects of urban–rural integration on the transition of arable land use functions

This study examines the impact of urban–rural integration (URI) on the transition of arable land use functions (TALUF), providing both theoretical and empirical insights into how URI can promote TALUF. Utilizing panel data from 41 cities in the Yangtze River Delta (YRD) from 2003 to 2022, this research employs the entropy method, Durbin model, and spatial threshold model to assess the levels of URI and TALUF in the region and examine the direct impact, spatial spillover effects, and threshold effects of URI on TALUF. The findings are: (1) During the study period, both TALUF and URI levels in the YRD showed continuous improvement, although significant spatial imbalances persisted, with regional differentiation gradually diminishing. (2) URI exerts a significant inhibitory effect on TALUF and produces a negative spatial spillover effect. (3) URI exerts a single threshold effect on TALUF: below the economic development threshold of 0.733, URI and its spatial term inhibit TALUF, whereas above this threshold, they promote it. Consequently, fostering high-quality regional economic development, enhancing intercity cooperation, driving agricultural technological innovation, and increasing agricultural input intensity are essential strategies for arable land use policies in the context of URI.

Impact of agricultural science and technology innovation resources allocation on rural revitalization

China’s reform began in the countryside, and rural reform began in Anhui. This study uses Anhui Province, China as an example in analyzing the impact of agricultural science and technology innovation resource allocation on rural revitalization, particularly by constructing an econometric model. Results indicate that the allocation of agricultural science and technology innovation resources has a steady positive impact on rural revitalization. Improvements in the allocation of agricultural science and technology innovation resources can effectively promote the development of rural revitalization and the spatial spillover of rural development, thereby facilitating the promotion of the spillover of neighboring areas to local rural development. A nonlinear relationship exists between allocation of agricultural science and technology innovation resources and rural revitalization. Lastly, rural revitalization can only be promoted substantially when the allocation of agricultural science and technology innovation resources reaches a certain condition.

Digital Economy Development, Agricultural Technology Innovation and Agricultural Economy Resilience

Based on the provincial data of 2013-2022, this paper discusses the mechanism of agricultural economy resilience affected by the development of digital economy. The results are obtained by using fixed effect, intermediary effect and threshold effect as follows: (1) the development of digital economy can improve the resilience of agricultural economy, and the conclusion passes the robustness test. (2) Mechanism test shows that agricultural technology innovation indirectly acts between the digital economy and the resilience of the agricultural economy. (3) Through the threshold model, it is found that the non-linear characteristics of agricultural technology innovation appear. Therefore, in order to improve the resilience of agricultural economy, the paper puts forward suggestions on strengthening regional infrastructure construction and enhancing the support for agricultural technology innovation.

Efficiency and Driving Factors of Agricultural Carbon Emissions: A Study in Chinese State Farms

Promoting low-carbon agriculture is vital for climate action and food security. State farms serve as crucial agricultural production bases in China and are essential in reducing China’s carbon emissions and boosting emission efficiency. This study calculates the carbon emissions of state farms across 29 Chinese provinces using the IPCC method from 2010 to 2022. It also evaluates emission efficiency with the Super-Slack-Based Measure (Super-SBM model) and analyzes influencing factors using the Logarithmic Mean Divisia Index (LMDI) method. The findings suggest that the three largest carbon sources are rice planting, chemical fertilizers, and land tillage. Secondly, agricultural carbon emissions in state farms initially surge, stabilize with fluctuations, and ultimately decline, with higher emissions observed in northern and eastern China. Thirdly, the rise of agricultural carbon emission efficiency is driven primarily by technological progress. Lastly, economic development and industry structure promote agricultural carbon emissions, while production efficiency and labor scale reduce them. To reduce carbon emissions from state farms in China and improve agricultural carbon emission efficiency, the following measures can be taken: (1) Improve agricultural production efficiency and reduce carbon emissions in all links; (2) Optimize the agricultural industrial structure and promote the coordinated development of agriculture; (3) Reduce the agricultural labor scale and promote the specialization, professionalization, and high-quality development of agricultural labor; (4) Accelerate agricultural green technology innovation and guide the green transformation of state farms. This study enriches the theoretical foundation of low-carbon agriculture and develops a framework for assessing carbon emissions in Chinese state farms, offering guidance for future research and policy development in sustainable agriculture.

ICS-ResNet: A Lightweight Network for Maize Leaf Disease Classification

The accurate identification of corn leaf diseases is crucial for preventing disease spread and improving corn yield. Plant leaf images are often affected by factors such as complex backgrounds, climate, light, and sample data imbalance. To address these issues, we propose a lightweight convolutional neural network, ICS-ResNet, based on ResNet50. This network incorporates improved spatial and channel attention modules as well as a deep separable residual structure to enhance recognition accuracy. (1) The residual connections in the ResNet network prevent gradient loss during deep network training. (2) The improved channel attention (ICA) and spatial attention (ISA) modules fully utilize semantic information from different feature layers to accurately localize key features of the network. (3) To reduce the number of parameters and lower computational costs, we replace traditional convolutional computation with a depth-separable residual structure. (4) We also employ cosine annealing to dynamically adjust the learning rate, enhancing the network’s training stability, improving model convergence, and preventing local optima. Experiments on the corn dataset in Plant Village compare the proposed ICS-ResNet with eight popular networks: CSPNet, InceptionNet_v3, EfficientNet, ShuffleNet, MobileNet, ResNet50, ResNet101 and ResNet152. The results show that the ICS-ResNet achieves an accuracy of 98.87%, which is 5.03%, 3.18%, 1.13%, 1.81%, 1.13%, 0.68%, 0.44% and 0.60% higher than the other networks, respectively. Furthermore, the number of parameters and computations are reduced by 69.21% and 54.88%, respectively, compared to the original ResNet50 network, significantly improving the efficiency of corn leaf disease classification. The study provides strong technical support for sustainable agriculture and the promotion of agricultural science and technology innovation.

Ammonia Emissions and Building-Related Mitigation Strategies in Dairy Barns: A Review

In this systematic review, the PRISMA method was applied to examine publications from the last two decades that have investigated the noxious gaseous emissions from dairy barns. The aim was to analyse the outcomes from literature studies estimating the quantities of polluting gases produced in dairy barns, with a specific focus on ammonia (NH3) emissions. Various studies, among those reviewed, have used mixed effects models, mass balance approaches and dispersion methods, revealing significant variability due to different experimental protocols and environmental contexts. Key challenges include the lack of standardised measurement techniques and the limited geographical coverage of research, particularly in climatically extreme regions. This review also explores proposed methods to reduce the associated effects through mitigation strategies. Estimation of NH3 emissions is significantly influenced by the complex interactions between several factors; including animal management practices, such as controlling animal behavioural activities; manure management, like utilising practices for floor manure removal; the type of structure housing the animals, whether it is naturally or mechanically ventilated; and environmental conditions, such as the effects of temperature, wind speed, relative humidity, and ventilation rate on NH3 release in the barn. These influential components have been considered by researchers and targeted mitigation strategies have been identified. Despite growing attention to the issue, gaps in the scientific literature were identified and discussed, particularly regarding the analysis of mitigation strategies and their long-term impacts (i.e., environmental, economic and productivity-wise). The purpose of this review is to help improve research into sustainable agricultural practices and technological innovations, which are fundamental to reducing NH3 emissions and improving air quality in agricultural environments.

Exploring sustainable agriculture: Investigating the impact of controlled release fertilizer damage through bonded particle modeling

Controlled release technology of chemical fertilizers is an important cornerstone for the efficient development of sustainable agriculture. Damage to the coating layer and fragmentation of particles during the application of controlled release fertilizers can lead to the loss of their controlled release properties, making the development of effective damage reduction operations a key focus in the study of such agricultural materials. Therefore, this paper employs the bonded particle model (BPM) method within the discrete element method (DEM) framework to investigate the mechanism of coating damage in controlled-release fertilizers. It establishes a classification of damage types based on the level of damaged particles as an effective analytical approach. Furthermore, a comparative analysis is conducted between the discrete element particle model (DEPM) method and the bonded particle model method in terms of their performance in discharge mechanics and flow characteristics. The results indicate that the breakage rate of fertilizer discharge is influenced by both the formation of a steady state and the depletion of the fertilizer load within the container, with an average breakage rate of 1.511% over the total period. Differences in the average maximum compression force and mass flow rate between the discrete element particle model group and the bonded particle model group were 60.355 N and 1.998 g/s, respectively, reflecting limitations imposed by the fertilizer models on expected damage deformation behaviors. This study elucidates the process and potential impacts of fertilizer particle damage and deformation, revealing the strengths and limitations of two simulation model approaches under different application conditions, providing insights for sustainable agricultural production and technological innovation in fertilization equipment.

ANALYSING THE NEXUS OF WEATHER ON COTTON PRODUCTION IN SINDH PAKISTAN: USING ARDL BOUND TEST

Climate change is expected to have impacts on plants, crops, and insect pests, and pose substantial pressures to sustainable food security. This research investigates the impact of weather changes on the production of cotton crops in Sindh, Pakistan by analyzing both short-term and long-term effects. Hence by employing annual time-series data of 58 years from 1961 to 2018 to explore the causal relationship between climate variables and specifically cotton production. For this purpose the Augmented Dickey-Fuller (ADF) test was applied and was significantly stationary at 1st difference, ensuring robust econometric analysis. In addition to understanding the long-run and short-run effects, the Autoregressive Distributed Lag (ARDL) bounds test was applied. Short-term findings reveal that a 1% increase in logged annual cotton area (CA) leads to a 0.671% rise in cotton production (CP), and a similar 1% increment in logged annual rainfall in kharif (R) results in a 0.224% increase in cotton production. The long-term relationships between weather variables and cotton yields were confirmed through the Johansen cointegration test, suggesting persistent climate impacts over time. It is recommended that authorities identify and promote the cultivation of temperature variation-tolerant cotton varieties and enhance the focus on agricultural research and technological innovation. To support these initiatives, timely and farm-specific climate change information should be disseminated. Adoption of advanced, cost-effective agricultural technologies can significantly improve crop yields. These strategic responses are essential to mitigate the adverse effects of climate variability on agriculture in Sindh, ensuring sustainable cotton production and economic stability for cotton farming communities.