Abstract Climate alteration poses a consistent threat to food security and agriculture production system. Agriculture sector encounters severe challenges in achieving the sustainable development goals due to direct and indirect effects inflicted by ongoing climate change. Although many industries are confronting the challenge of climate change, the impact on agricultural industry is huge. Irrational weather changes have raised imminent public concerns, as adequate output and food supplies are under a continuous threat. Food production system is negatively threatened by changing climatic patterns thereby increasing the risk of food poverty. It has led to a concerning state of affairs regarding global eating patterns, particularly in countries where agriculture plays a significant role in their economies and productivity levels. The focus of this review is on deteriorating consequences of climate alteration with the prime emphasis on agriculture sector and how the altering climatic patterns affect food security either directly or indirectly. Climate shifts and the resultant alteration in the temperature ranges have put the survival and validity of many species at risk, which has exaggerated biodiversity loss by progressively fluctuating the ecological structures. The indirect influence of climate variation results in poor quality and higher food costs as well as insufficient systems of food distribution. The concluding segment of the review underscores the emphasis on policy implementation aimed at mitigating the effects of climate change, both on a regional and global scale. The data of this study has been gathered from various research organizations, newspapers, policy papers, and other sources to aid readers in understanding the issue. The policy execution has also been analyzed which depicted that government engrossment is indispensable for the long-term progress of nation, because it will guarantee stringent accountability for the tools and regulations previously implemented to create state-of-the-art climate policy. Therefore, it is crucial to reduce or adapt to the effects of climate change because, in order to ensure global survival, addressing this worldwide peril necessitates a collective global commitment to mitigate its dire consequences. 

Securing a sustainable future: the climate change threat to agriculture, food security, and sustainable development goals

Trend and spatial pattern of stable cropland productivity in China based on satellite observations (2001−2020)

The pivotal role of computer technology in augmenting crop productivity, optimizing supply chain management in food production, and reducing wastage through precision agriculture techniques is undeniable. The utilization of computers has significantly transformed the field of food production, enhancing effectiveness, environmental sustainability, and economic viability. This study examines the asymmetric and long-term effects of computer usage on grain output in the selected Chinese provinces. Additionally, it investigates the impact of other variables, such as energy use and the application of pesticides and fertilizers on grain yields. To analyze the dataset spanning from 2002 to 2020, the researchers employed the FGLS, D-K, and Dumitrescu-Hurlin causality methods. The study's findings underscore the substantial positive impact of computer utilization on grain production. Besides, fertilizer application and adoption of irrigation practices positively influence grain yields. Conversely, the utilization of pesticides, power consumption, and rural labor engagement exhibit a negative impact on grain output. Therefore, evaluating the potential of investing in sophisticated technologies, considering multiple elements including local conditions, the availability of financial resources, and the role of agricultural industrialization in regional development is imperative. We discuss the policy implications of these findings in detail in the Conclusion section. 

Digital agriculture for sustainable development in China: The promise of computerization

Agriculture plays a crucial role in Nigeria's economy, serving as a vital source of sustenance and livelihood for numerous Nigerians. With the escalating impact of climate change on crop yields, it becomes imperative to develop models that can effectively study and predict rice output under varying climatic conditions. This study collected rice yield data from Katsina state, spanning the years 1970 to 2017, sourced from the Nigeria Bureau of Statistics. Additionally, climatic data for the same period were obtained from the World Bank Climate Knowledge portal. Logistic Regression (LR), Artificial Neural Network (ANN), Random Forest (RF), Random Trees (RT), and Naïve Bayes (NB) were employed to develop rice yield prediction models utilizing this dataset. The findings reveal that random forest and random trees exhibited superior classification performance for yield prediction. The developed models offer a promising tool for predicting future rice yields, facilitating proactive measures to ensure food security for the people of the state.

/pdf/rice-yield-forecasting-a-comparative-analysis-of-multiple-3iiu3tsi.pdf

Rice Yield Forecasting: A Comparative Analysis of Multiple Machine Learning Algorithms

Artificial intelligence (AI) has been clearly established as a technology with the potential to revolutionize fields from healthcare to finance - if developed and deployed responsibly. This is the topic of responsible AI, which emphasizes the need to develop trustworthy AI systems that minimize bias, protect privacy, support security, and enhance transparency and accountability. Explainable AI (XAI) has been broadly considered as a building block for responsible AI (RAI), with most of the literature considering it as a solution for improved transparency. This work proposes that XAI and responsible AI are significantly more deeply entwined. In this work, we explore state-of-the-art literature on RAI and XAI technologies. Based on our findings, we demonstrate that XAI can be utilized to ensure fairness, robustness, privacy, security, and transparency in a wide range of contexts. Our findings lead us to conclude that XAI is an essential foundation for every pillar of RAI.

Explainable AI is Responsible AI: How Explainability Creates Trustworthy and Socially Responsible Artificial Intelligence

Identifying the Determinants of Crop Yields in China Since 1952 and its Policy Implications

Cultural service assessment of cultivated land ecosystem in the Yangtze River Delta region from a supply–demand-flow perspective

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Identifying the Determinants of Crop Yields in China Since 1952 and its Policy Implications

Cultural service assessment of cultivated land ecosystem in the Yangtze River Delta region from a supply–demand-flow perspective