The aim of this paper is to develop a methodology for determining the size of the unified land tax in agriculture based on the results of the economic assessment of agricultural land to form the foundation of a new effective system of macroeconomic instruments for state regulation of the innovative development of the agro-industrial complex of the Republic of Kazakhstan. There were used gatherings of facts and summaries, induction and deduction, analysis and synthesis, historical and logical, normative, comparison, index and modeling methods in the research. The article provides an overview of various scholarly perspectives on the challenges and strategies for improving the tax system. The base rates of the unified land tax per hectare of arable land have been calculated to establish equal conditions for all land users. This unified land tax rate is expected to encourage the efficient utilization of land resources and enable the optimization of production structure. The article addresses avenues for improving water management relations in agriculture, aimed at fostering a shared interest and creating incentives for adopting innovative technologies in both agriculture and the water management sector. An essential condition for achieving the effective functioning of Kazakhstan’s agro-industrial complex is its transformation to an innovative development model. This necessitates the development and application of a new system of macroeconomic tools for its implementation, aimed at creating a favorable environment for entrepreneurial development.

The Agriculture Trading Platform (ATP) represents a significant innovation in the realm of agricultural trade in Malaysia. This web-based platform is designed to address the prevalent inefficiencies and lack of transparency in the current agricultural trading environment. By centralizing real-time data on agricultural production, consumption, and pricing, ATP provides a comprehensive dashboard that facilitates data-driven decision-making for all stakeholders in the agricultural supply chain. The platform employs advanced deep learning algorithms, including Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNN), to forecast market trends and consumption patterns. These predictive capabilities enable producers to optimize their market strategies, negotiate better prices, and access broader markets, thereby enhancing the overall efficiency and transparency of agricultural trading in Malaysia. The ATP’s user-friendly interface and robust analytical tools have the potential to revolutionize the agricultural sector by empowering farmers, reducing reliance on intermediaries, and fostering a more equitable trading environment.

This study systemically examines the numerous impacts of climate change on agriculture in Tunisia. In this study, we establish an empirical and comprehensive methodology to assess the effects of climate changes on Tunisian agriculture by investigating current climatic patterns using crop yields and socioeconomic variables. The study also assesses the types of adaptation strategies agriculture uses in Tunisia and explores their effectiveness in coping with climate-related adversities. We also consider some resilience factors, namely the ecological aspect and economic and social camouflage pursued by the (very) men in Tunisian agriculture. We also extensively discuss the complex interconnected relationship between policy interventions and community-based adaptations, a crucial part of the ongoing debate on climate change adaptation and resilience in agriculture. The findings of this study contribute to this important conversation, particularly for areas facing similar challenges.