Northern Kazakhstan is a major wheat exporter, contributing to food security in Central Asia and beyond. However, wheat yields fluctuate and low-producing years occur frequently. It is currently unclear to what extent human-induced climate change contributes to this. The most severe low-producing year in this century was in 2010, which had severe consequences for the food security of wheat-importing countries. Here, we present a climate impact attribution study that quantifies the impact of human-induced climate change on the average wheat production and associated economic revenues in northern Kazakhstan in the 21st century and on the likelihood of a low-production year like 2010. The study uses bias-adjusted counterfactual and factual climate model data from two large ensembles of latest-generation climate models as input to a statistical subnational yield model. We consider the climate data and the yield model as fit for purpose as first, the factual climate simulations represent the observations, second, the out-of-sample validation of the yield model performs reasonably well with a mean R ² of 0.54, and third, the results are robust under the performed sensitivity tests. Human-induced climate change has had a critical impact on wheat production, specifically through increases in daily-minimum temperatures and extreme heat. This has resulted in a decrease in yields during 2000–2019 by approximately 6.2%–8.2% (uncertainty range of two climate models) and an increased likelihood of the 2010 low-production event by 1.5–4.7 times (10th to 90th percentile uncertainty range covering both climate models). During 2000–2019, human-induced climate change caused economic losses estimated at between 96 and 180 million USD per year (10th to 90th percentile uncertainty range covering both climate models). These results highlight the necessity for ambitious global mitigation efforts and measures to adapt wheat production to increasing temperatures, ensuring regional and global food security.

In modern times, the risk of food insecurity is a concern for policymakers at the global and national levels, as the issue of hunger and malnutrition still exists. Food security is vulnerable to any crises. The main goal of this paper is to create a neural-network-based predictive model to forecast food consumption trends in Kazakhstan, aiming to reduce the risk of food insecurity. The initial phase of this study involved identifying socioeconomic factors that significantly influence food consumption behaviors in Kazakhstan. Principal component analysis was used to identify key variables, which became the basis for modelling artificial neural networks. It was revealed that the poverty rate, GDP per capita, and food price index are pivotal determinants of food consumption in Kazakhstan. Two models were prepared: to predict food consumption on a national scale per capita per month, and to predict the percentage distribution of various food categories. The prediction of the percentage distribution of various food categories in Kazakhstan demonstrates the positive modelling quality indicators and strengthens the assumption that network modelling can be used. Predictions for total food consumption over the next three years indicate declining metrics, raising concerns about the potential food insecurity risk in Kazakhstan.

This study investigates the application of Artificial Neural Networks (ANN) in forecasting agricultural yields in Kazakhstan, highlighting its implications for economic management and policy-making. Utilizing data from the Bureau of National Statistics of the Republic of Kazakhstan (2000-2023), the research develops two ANN models using the Neural Net Fitting library in MATLAB. The first model predicts the total gross yield of main agricultural crops, while the second forecasts the share of individual crops, including cereals, oilseeds, potatoes, vegetables, melons, and sugar beets. The models demonstrate high accuracy, with the total gross yield model achieving an R-squared value of 0.98 and the individual crop model showing an R value of 0.99375. These results indicate a strong predictive capability, essential for practical agricultural and economic planning. The study extends previous research by incorporating a comprehensive range of climatic and agrochemical data, enhancing the precision of yield predictions. The findings have significant implications for Kazakhstan's economy. Accurate yield predictions can optimize agricultural planning, contribute to food security, and inform policy decisions. The successful application of ANN models showcases the potential of AI and machine learning in agriculture, suggesting a pathway towards more efficient, sustainable farming practices and improved quality management systems.

The article assesses the food security of one region of Kazakhstan - the East Kazakhstan region. In the analyses, criteria such as the level of the region’s food independence, the degree of satisfaction of the population’s physiological needs in basic food, and the level of economic availability of food were applied. The research has shown that the food security in the East Kazakhstan region was at an acceptable level. However, some threats to food security were identified, including: a high level of income differentiation among the households of the East Kazakhstan region, an increase in the price index for food products, and a significant proportion of the population with incomes below the subsistence minimum. The results of the evaluation can be useful for creating agri-food policy measures that will ensure, maintain and improve the level of food security at both a regional and national level.

This research article examines the impact of the COVID-19 pandemic on food security in Kazakhstan. The purpose of the study is to identify the main factors influencing the level of food security during the pandemic and to propose directions for improving it. The research methodology includes a literature review, analysis of statistical data using various logical methods of scientific knowledge such as analysis, synthesis, abstraction, generalization, induction, deduction, and analogy. The study is comprehensive and covers a wide range of aspects influencing food security, providing valuable insights into the underlying causes of food security issues and suggesting practical solutions to enhance food security in the country. The main results of the study show that the pandemic has had a negative impact on the level of food security in Kazakhstan due to disruptions in food supply chains and the economic downturn caused by the pandemic. The analysis of statistical data highlights the need to focus on improving local food production and reducing dependence on imported food products. The study contributes to the field of food security by providing empirical evidence of the impact of the COVID-19 pandemic on food security in Kazakhstan and proposing specific directions for improving it. The practical significance of this research is that it can inform policymakers and stakeholders in the food industry about the measures that need to be taken to ensure food security in Kazakhstan during and after the pandemic.

This research article examines the impact of key external economic factors on food security in Kazakhstan, particularly focusing on the dynamics of domestic food prices in relation to global economic fluctuations. The purpose of the study is to identify and empirically analyze significant external economic determinants influencing Kazakhstan’s food security, providing evidence-based insights for policymakers. The research methodology includes quantitative econometric methods, specifically ordinary least squares regression, correlation analysis, and principal component analysis, utilizing annual statistical data from 2010 to 2024. The study is comprehensive and covers macroeconomic indicators such as the national Food Price Index, exchange rates, global food prices, GDP per capita, and government agricultural expenditures. The main results of the study show that global food prices and national income levels significantly influence domestic food price stability, whereas the direct short-term effects of currency fluctuations and government agricultural expenditures are statistically less pronounced. The analysis of statistical data highlights Kazakhstan’s susceptibility to global economic shocks and logistical disruptions, underscoring the country’s vulnerability due to import dependency and currency volatility. The study contributes to the literature by systematically addressing external economic determinants of food security in the context of Kazakhstan, an aspect that remains relatively underexplored in previous research. The practical significance of this research lies in its capacity to inform policymakers and stakeholders on adaptive strategies for enhancing the resilience and sustainability of Kazakhstan’s food security framework in the face of increasing global uncertainties