Regional Trends and Macroeconomic Predictors of Raw Milk Production in Asia from 2000 to 2032

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Employing multiple linear regression and exponential smoothing techniques, the research investigates the macro-level determinants of milk output, integrating economic, demographic, and environmental variables. The regression model identified livestock stock ( β = 0.193 , p < 0.001 ), agricultural land use ( β = 1.579e⁵, p = 0.00062 ), gross domestic product (GDP) ( β = 499.6, p < 0.001 ), and temperature ( β₁ = 3.375e⁶; β₂ = −7.985e⁴ ) as statistically significant predictors, collectively explaining 33% of the variance in production (R-Squared (R²) = 0.33). Forecasting results using exponential smoothing demonstrated high predictive accuracy (e.g., Mean Absolute Percentage Error (MAPE) = 0.37% for Pakistan, 2.88% for India), enabling reliable projections of species-specific milk output. Projections indicate continued expansion in South Asia - particularly for cattle and buffalo milk - driven by demographic growth and institutional support, while East Asia reveals divergent paths, with China showing modest growth and Japan facing prolonged decline. Emerging markets in Southeast Asia, notably Vietnam and Indonesia, exhibit promising but variable trends. The findings offer region-wide implications for evidence-based policy development, resource allocation, and the design of climate-resilient, economically viable dairy strategies across heterogeneous production systems in Asia. Raw Milk Asia Forecasting Dairy Sector Food Security Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Milk is widely considered as one of the most nutritionally complete natural foods and plays a key role in ensuring food sustainability and security in rural livelihoods, notably in developing nations (Moran & Morey, 2015 ). Within the context of Asia, the dairy industry has evolved as a crucial driver of agricultural transformation, driven by elements such as increasing household incomes, rapid population expansion, shift in dietary habits toward more nutrient-rich food sources and urban development (Food and Agriculture Organization, 2014 ). Although global dairy trade was traditionally led by Western nations, recent decades have undergone a considerable rise in both milk production and consumption across Asia. Notably, countries such as Chine, India, and Pakistan have established themselves as major players to the global dairy supply (Dong, 2006 ; Singh AB, Bhakar V, Gaurav G et al., 2024). The sector seizes a particular importance for rural economies within the region, facilitating a stable income stream and functioning as a form of economic asset for all smallholder farmers, who contribute to most of the milk production (Baldock, D et al.,1996; Siddiky et al., 2017). Dairy production tendencies across Asian countries differ substantially due to variations in technological development, economic conditions, and policy environments. The most of production systems within the region are led by smallholder operations. In Pakistan and India, for example, small - scale farmers are contributing for more than 80% of the total milk supplied to markets, together contributing around 20% of the worldwide milk production (Schembri et al., 2023 ; Staal et al., 2008 ). In contrast, China observed a sharp rise in dairy production following the year 2000, predominantly due to reliable government support and conducive policies. Meanwhile, Japan has underwent stagnation in its dairy sector, mainly linked to market saturation and population decline (Douphrate et al., 2013 ). Türkiye has demonstrated substantial growth, nearly doubling its dairy production in the past two decades to reach a projected 23 million tonnes per annum. On the other hand, Southeast Asian countries including Vietnam and Indonesia have advanced from relatively modest production levels. For instance, Indonesia produced nearly 1 to 2 million tonnes every year and reported a growth rate of 8.1% between 2017 and 2020 (Daryanto et al., 2021 ; Yonar et al., 2022 ). Milk production over Asia is considerably shaped by both the extent of technological adoption and environmental conditions. Unpredictable rainfall patterns and increasing temperatures have posed significant constraints to dairy productivity, particularly in smallholder farmers for climate-sensitive countries such as Pakistan (Abbas et al., 2019 ; Ren et al., 2023 ). On the technological domain, the introduction of artificial insemination techniques, high-yielding cattle breeds, and enhanced feeding approaches has led to remarkable improvements in milk yields (Singh et al., 2024 ). However, small-scale producers often encounter systemic limitations, including increased feed costs and inadequate access to veterinary interventions (Akzar et al., 2022 ). The transition from indigenous to crossbred, higher-yielding cattle has served as a critical role in this shift, adding to over a 200% surge in milk production since the early 1990s (Singh et al., 2024 ). Moreover, institutional support mechanisms— especially cooperative frameworks such as Amul model and the India’s National Dairy Development Board (NDDB) — have been pivotal in facilitating both sectoral expansion and sustained incomes for dairy farmers (Ravichandran et al., 2020 ). Despite notable progress in the dairy sector, there persists a significant lack of country - specific, comprehensive forecasting studies that holistically integrate long - term technological, economic, and environmental variables. To mitigate this research gap, the present study investigates raw milk production trends from 2000 to 2022 across nine key Asian countries: India, Pakistan, Bangladesh, China, Indonesia, Japan, Vietnam, Iran, and Türkiye. Quantitative forecasting approaches continue to dominate agricultural research, with a strong emphasis on regression-based causal models and time series analyses (Sun et al., 2023 ). Within this framework, exponential smoothing which was typically presented by Robert Goodell Brown (Brown, 1959 ) and later expanded by Charles C. Holt (Holt, 1957 ), are often employed to produce predictive estimates, as they assign greater importance to recent data points, thereby offering enhanced sensitivity to evolving production trends (Fomby, 2008 ). Utilizing this methodological approach, the current study seeks to determine and analyze the environmental, economic, and technological determinants of milk production among the targeted countries, while predicting future output trends through 2032. These findings will aim to shape more effective resource allocation, policy development, and strategic planning within the region’s dairy industries. Methods Study Area and Design This study applies a quantitative longitudinal approach to assess trends in raw milk production from 2000 to 2022 and to predict future trends through 2032 across a representative group of Asian nations. Nine countries were rationally selected to represent the structural diversity and geographical breadth of dairy production models within the region. These consist of Pakistan, India, and Bangladesh from South Asia; Japan and China from East Asia; Vietnam and Indonesia from Southeast Asia; and Türkiye and Iran from West Asia. This cross-regional selection includes both emerging dairy markets such as Indonesia and Vietnam, as well as major milk-producing economies such as China and India. The integration of evolving and traditional dairy sectors enables a comprehensive examination of regional production patterns. The study area is shown in Fig. 1 . Data and Variables Data from 2000 through 2022 were obtained from two primary sources: the Food and Agriculture Organization's Statistical (FAOSTAT) database for milk production and livestock-related indicators, and the World Bank Open Data website for economic and demographic variables (FAO, 2025; World Bank, 2025). The analysis includes the following variables: annual raw milk production (in tonnes); gross domestic product (GDP) (in current US dollars); percentage of the population living in rural areas; percentage of total land area devoted to agriculture; total livestock stock (number of animals); and annual mean surface air temperature (°C). These parameters were selected to account for the multidimensional determinants influencing dairy production, encompassing production output, demographic composition, economic development, livestock resources, land use patterns, and climatic factors across the targeted countries. Data Analysis All analyses were conducted using RStudio (Version 2025.5.0.496). The data analysis comprised three main stages: (1) descriptive statistics, (2) multiple linear regression, and (3) exponential smoothing for time series forecasting. Descriptive statistics were used to summarize the main features of the dataset. Key summary measures included frequency (N), mean, standard deviation (SD), median, and interquartile range (IQR). These statistics provided a foundational understanding of the distribution and central tendency of the variables. Pearson's correlation analysis was conducted to examine linear relationships among the continuous variables, including GDP, rural population, agricultural land, livestock stock, and temperature. The correlation analysis used overall mean values for each variable to evaluate their interrelationships. Multiple linear regression was employed using the "lm" function in R to identify the factors associated with raw milk production. The regression model was specified as follows: $$\:Y\:=\:\beta\:₀\:+\:\beta\:₁X₁\:+\:\beta\:₂X₂\:+\:\dots\:\:+\:\beta\:ₚXₚ\:+\:\epsilon\:$$ where Y represents the dependent variable (raw milk production), X₁, X₂, …, Xₚ are the independent variables (predictors), β₀ is the intercept, β₁, …, βₚ are the regression coefficients, and ε is the random error term. To assess multicollinearity among the predictor variables, the Variance Inflation Factor (VIF) was calculated using the "vif" function. VIF values exceeding the commonly accepted thresholds of 5 or 10 were considered indicative of potential multicollinearity issues (O'Brien, 2007 ). Exponential smoothing was applied using annual data of raw milk production, as agricultural production statistics are typically reported on a yearly basis. This approach ensures consistency and comparability across years and countries. Forecast accuracy was evaluated using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The exponential smoothing model was defined as: $$\:ŷₜ₊₁\:=\:\alpha\:yₜ\:+\:(1\:-\:\alpha\:)ŷₜ$$ where ŷₑ₊₁ is the forecast for the next time period, yₜ is the actual value at time t, ŷₜ is the forecasted value at time t, and α is the smoothing constant (0 < α < 1), representing the weighting applied to the most recent observation. All statistical analyses and visualizations were conducted using R packages including "psych", "ggplot2", "dplyr", "car", "forecast", "corrplot", and "stats" (Fox & Weisberg, 2019 ; Hyndman & Khandakar, 2008; R Core Team, 2024 ; Revelle, 2024 ; Wei & Simko, 2024 ; Wickham, 2016 ; Wickham et al., 2023 ). Results Descriptive Summary Descriptive statistics of the study variables are presented in Table 1 . Milk production and livestock stock demonstrated considerable variability across observations. Economic, demographic, and environmental variables such as GDP, rural population percentage, agricultural land, and surface temperature showed relatively stable patterns across countries. Table 1 Descriptive statistics for study variables (2000–2022) Variables Frequency (N) Mean ± Standard Deviation (SD) Median Inter-quartile range (IQR) (25% – 75%) Raw Milk Production (tonnes) 759 7622883 ± 17627962 566019 128000–6049365 Gross Domestic Product (GDP) 23 1113.19 ± 756.27 837.34 485.25–1730.18 Rural population (percentage of total population) 23 68.64 ± 5.05 68.78 64.53–72.84 Agricultural land (percentage of total land area), 23 71.23 ± 0.84 71.18 70.60–72.02 Stock (total animal count) 759 37263603 ± 50843889 17611392 2897460–42608000 Surface air temperature (Annual average) 23 25.78 ± 0.37 25.76 25.51–25.96 National Growth Patterns in Milk Production (2000–2022) Annual milk production growth rates across the nine selected Asian countries from 2000 to 2022 are presented in Fig. 2 . The results reveal considerable variation in growth trajectories across regions. Vietnam recorded the highest average annual growth rate at 13.2%, followed by Bangladesh (9.6%) and China (6.3%), indicating strong upward trends in these countries over the period. India maintained a steady and positive growth pattern, with an average of 5% annually, reflecting consistent year-on-year increases. Pakistan and Türkiye reported moderate average growth rates of 4.1% and 3.9%, respectively, while Indonesia and Iran showed relatively lower average rates of 2.2% and 2%. In contrast, Japan was the only country to experience an overall negative trend, with an average annual growth rate of − 0.5%, marked by frequent declines across multiple years. Species-Level Milk Production Distribution Milk production distribution by animal type across the selected countries is displayed in Fig. 3 . The density plots illustrate the contribution of five animal species - cattle, buffalo, goats, sheep, and camels - to overall milk output. Cattle emerged as the predominant source of milk in the majority of countries, particularly in Japan, China, and Vietnam, where other species contribute minimally. In contrast, countries like India and Pakistan displayed more diverse production profiles, with significant contributions from buffalo, goats, and camels in addition to cattle. Buffalo milk production was especially notable in South Asian countries such as India, Pakistan, and Bangladesh, while goat and sheep milk showed visible presence in Iran, Türkiye, and Bangladesh. Camel milk production appeared in only a few countries, including India, Pakistan, and China, with relatively low but measurable output. Southeast Asian countries like Indonesia and Vietnam demonstrated more modest but multi-source production, reflecting emerging dairy sectors. Regression Model of Milk Production Determinants As presented in Table 2 , model identified four statistically significant predictors of raw milk production: gross domestic product (GDP), agricultural land use, livestock stock, and surface temperature. GDP showed a positive association (β = 490, p < 0.001 ), as did agricultural land (β = 1.579e⁵, p = 0.00062 ). Livestock stock had the largest standardized effect ( β = 0.193, p < 0.001 ). Surface temperature exhibited a curvilinear relationship, with a positive linear coefficient ( β = 3.375e⁶, p < 0.001 ) and a negative quadratic term ( β = −7.985e⁴, p = 0.00011 ). The coefficient for rural population was not statistically significant ( β = 4.649e⁴, p = 0.430 ). Table 2 Multiple Linear Regression Estimates for Predictors of Raw Milk Production (2000–2022) *Indicates a significant variable ( p value | t |) Raw Milk Production (Intercept) − 4.348e 07 6.857e 06 -6.341 < 0.001 * GDP 490 1.075e 02 4.648 < 0.001 * Rural Pop 4.649e 04 5.889e 04 0.789 0.430 Agri Land 1.579e 05 4.596e 04 3.436 0.00062* Stock 0.193 0.0011 17.070 < 0.001 * Linear temp 3.375e 06 6.917e 05 4.880 < 0.001 * Quadratic temp -7.985e 04 2.054e 04 -3.888 0.00011 * Forecasts of Milk Production by Species (2023–2032) Forecast outputs for species-specific milk production - covering cattle, buffalo, goat, sheep, and camels - across the selected Asian countries are shown in Figs. 4 through 8 . These projections span 2023 to 2032 and were generated using exponential smoothing models applied to production data from 2000 to 2022. The figures include training data, test data, and forecasted values, with 80% and 95% confidence intervals included. Variation is observed in projected trends across species and countries. Cattle and buffalo milk production is projected to increase in India, Pakistan, and Vietnam. Other species-country combinations show more stable forecast patterns. Forecast values for goats, sheep, and camels are generally lower in magnitude and vary between countries. Confidence intervals widen across all forecasts toward the end of the projection period. Discussion Overview of key findings This study provides a robust, data-driven analysis of raw milk production trends across nine key Asian countries from 2000 to 2022, with forecasts extending to 2032. This research contributes to filling the notable gap in long - term, country - specific dairy forecasting models in Asia. These insights - particularly regarding the roles of livestock stock, GDP, agricultural land use, and temperature - carry significant implications for agricultural policy, rural development, and food security. The analysis identified livestock stock, GDP, agricultural land use, and surface temperature as statistically significant predictors of milk production. Forecasts revealed country-specific trajectories, with strong growth expected in India, Vietnam, and Bangladesh, while other countries such as Japan showed stagnation or decline. Growth Trends in Milk Production and Distribution (2000–2022) Between 2000 and 2022, raw milk production expanded significantly across most of the nine Asian countries, with notable growth in India, Vietnam, China, and Bangladesh. India maintained its position as the world's leading milk producer, supported by widespread smallholder involvement, improved cattle genetics, and institutional backing through entities like the National Dairy Development Board and Amul (Ravichandran et al., 2020 ; Birthal et al., 2017 ). Similarly, Bangladesh and Vietnam experienced accelerated growth due to urbanization, evolving consumer preferences, and investments in dairy infrastructure (Delgado et al., 1999 ; Daryanto et al., 2021 ). China's milk production increase was driven largely by industrialized production systems and strong policy support (Fuller et al., 2001 ). In contrast to the upward trends observed in countries like India and Vietnam, Japan experienced a steady decline in milk output. This decline can be attributed to an aging rural workforce, urban migration, and stagnant demand for dairy products (Suzuki and Kaiser, 2005 ). The contrasting growth trajectories - rapid in some countries, stagnant in others - underscore how demographic shifts, economic conditions, and policy support shape dairy sector performance (Gerosa & Skoet, 2012 ). Regarding species distribution, cattle remained the dominant source of milk across most countries, while buffalo, goat, and sheep contributed significantly to South Asian nations such as India and Pakistan. These multi-species systems reflect both ecological adaptability and cultural preferences (Thornton, 2010 ; Devendra, 2001 ). Goat and sheep milk also demonstrated relevance in regions with arid climates or limited grazing resources, emphasizing their resilience and economic importance in marginal farming systems (Haenlein, 2004 ). Predictors associated with Raw Milk Production As presented in Table 2 , multiple regression model identified livestock stock ( β = 0.193, p < 0.001 ) as the most statistically significant predictor of raw milk production. This finding aligns with Singh et al. ( 2024 ), who attributed India's 200% production surge since the 1990s to crossbred cattle adoption. Temperature exhibited a complex relationship with milk production, showing positive linear effects ( β = 3.375e⁶, p < 0.001 ) but negative quadratic impacts ( β = −7.985e⁴, p < 0.001 ). This dual modeling revealed a nuanced relationship: while moderate warmth may enhance productivity, excessive heat likely imposes physiological stress on animals. This pattern is supported by empirical studies in tropical climates (Nardone et al., 2010 ). The findings mirror those of Abbas et al. ( 2019 ) and Ren et al. ( 2023 ), who found that rising temperatures initially boost pasture growth in temperate zones, such as China's northern plains, but severely stress livestock in arid regions, such as Pakistan's Sindh province. GDP was positively associated with milk output, reflecting intensified production in rapidly industrializing economies like China and Vietnam, where capital investment enables large-scale dairy operations (Dong, 2006 ). However, India's smallholder-dominated sector - which contributes 80% of national output - remains buffered against economic volatility through cooperative models such as Amul, as observed by Ravichandran et al. ( 2020 ). This contrast helps explain why GDP - driven intensification is less pronounced in South Asia, where cooperative models provide stability for smallholder operations. Agricultural land also showed a strong positive association with milk production, suggesting that land availability continues to be a critical enabler of dairy expansion. This finding is particularly relevant in countries like Iran and Türkiye, where land-use policies have supported pasture development and fodder cultivation. Prior research by Gerber et al. ( 2013 ) similarly identified land access as a bottleneck in intensifying livestock systems in developing regions. Surprisingly, rural population share was not a significant predictor of milk production. This may reflect a decoupling of rurality from production intensity, as urban and peri - urban dairy systems gain prominence and mechanization reduces labor dependence. The finding also suggests that having a large rural population does not guarantee higher output without supportive infrastructure and market access. Milk Production Forecasts (2022–2032) Species-Specific and Regional Dynamics To project future trends, the study employed exponential smoothing methods, which have gained widespread acceptance in agricultural forecasting due to their ability to capture underlying trends while maintaining computational efficiency and interpretability (Hyndman & Athanasopoulos, 2021 ). The exponential smoothing models demonstrated varying predictive accuracy across countries and species, with Mean Absolute Percentage Error (MAPE) values ranging from 0.24–34.89% (Table 3 ). Lower error rates observed in established dairy markets such as Pakistan (0.37% for buffalo, 0.43% for cattle) and India (2.88% for buffalo, 5.92% for cattle) indicate more stable production patterns compared to emerging markets. This variation reflects the heterogeneous nature of Asian dairy systems and aligns with established literature demonstrating higher forecast reliability in mature dairy industries (Thornton, 2010 ). Table 3 Forecast Accuracy Metrics for Species-Level Milk Production by Country (2023–2032) Species Country RMSE MAE MAPE Buffaloe Bangladesh 54128.24 52490.5 18.84 Buffaloe China 210384.7 178727.1 5.75 Buffaloe India 3062759 2828636 2.88 Buffaloe Indonesia 13281.18 11762.27 14.20 Buffaloe Iran 6114.20 3738.981 3.17 Buffaloe Pakistan 164625.5 130277.8 0.37 Buffaloe Türkiye 24245.14 18136.98 34.89 Buffaloe Vietnam 1303.25 1221.13 4.57 Cattle Bangladesh 2017118 1949680 19.40 Cattle China 6369367 5268548 14.22 Cattle India 7820054 6646749 5.92 Cattle Indonesia 25110.99 23457.94 2.46 Cattle Iran 699156.9 534931.7 6.96 Cattle Japan 418394.9 354375 4.70 Cattle Pakistan 134100 96800.97 0.43 Cattle Türkiye 1143513 1056784 5.12 Cattle Vietnam 66228.21 56616.27 5.15 Goats Bangladesh 153521.2 121128.1 14.47 Goats China 5418.66 4927.48 2.11 Goats India 580514.8 419439.4 6.01 Goats Indonesia 6323.6 4781.016 1.31 Goats Iran 27479.45 24767.64 7.88 Goats Pakistan 29860 26571.03 2.70 Goats Türkiye 61529.03 54981.99 9.30 Sheep Bangladesh 10914.17 10585.08 18 Sheep China 81606.88 60785.25 4.77 Sheep Indonesia 6570.84 5336.21 3.46 Sheep Iran 92088.46 86252.62 24.74 Sheep Pakistan 1971.80 1840 4.48 Sheep Türkiye 208937.5 200063.6 16.87 Camels China 3547.69 3045.99 16.18 Camels India 957.86 757.41 9.48 Camels Pakistan 2385.83 2215.99 0.24 Cattle milk production forecasts reveal divergent regional trajectories through 2032. India and China demonstrate sustained upward trends, reflecting expanding domestic demand and continued infrastructure investments. Bangladesh exhibits particularly robust growth projections, consistent with the country's rapidly developing dairy sector driven by urbanization and rising incomes (Hemme & Otte, 2010 ). Conversely, Japan shows stable or slightly declining production, reflecting market maturity and demographic challenges typical of developed dairy economies (Britt et al., 2018 ). Beyond cattle, buffalo milk production continues to grow strongly in South Asia, with India reinforcing its position as the world's largest producer. Pakistan's forecasts indicate continued expansion at moderate rates, reflecting sector commercialization and improved management practices (Borghese & Mazzi, 2005 ). The sustained buffalo milk growth emphasizes the species' continued importance in regional dairy systems. Small ruminant milk production shows distinct regional patterns, with Iran and Turkey demonstrating continued growth in goat and sheep milk sectors. China's goat milk production indicates steady expansion, reflecting growing consumer interest in specialty dairy products and niche market development (Silanikove et al., 2019). Although comparatively minor in volume, camel milk production is showing modest but consistent growth in Pakistan, aligning with increasing commercial interest in camel dairy operations (Faye, 2020 ). These forecasts underscore significant disparities across Asia, reflecting the uneven development of dairy sectors. South Asian countries demonstrate the most robust growth projections across multiple species, reflecting favorable demographic trends, expanding consumer markets, and supportive government policies (Delgado et al., 1999 ). East Asian patterns vary significantly, with China showing continued expansion while Japan exhibits stability, reflecting different development stages and demographic trends. Southeast Asian countries present moderate but consistent growth patterns, with the market expected to grow annually by 7.56% and projected growth at a Compound Annual Growth Rate (CAGR) of 3.14% during 2025–2033. This trend is particularly evident in Vietnam and Indonesia, identified by Bourdinière and Garg (2023) as promising dairy markets in Southeast Asia, with aggressive growth trajectories fueled by demographic expansion, evolving consumer preferences, and increased investment in production and distribution infrastructure. Several limitations should be noted. First, the analysis relies on national-level data, which may obscure important sub-national disparities in production systems, infrastructure access, and climatic vulnerability. Second, the regression model did not incorporate key policy variables or consumer behavior dynamics, both of which can significantly influence dairy production outcomes. Third, the scope of variables included in the model - while statistically significant - remains limited. Important factors such as feed quality and availability, access to veterinary care, and disease management practices were not assessed due to data constraints. Future research should integrate these production-level determinants to provide a more comprehensive understanding of the drivers of milk output across diverse agroecological and socioeconomic contexts. Conclusions This study investigated raw milk production trends across nine Asian countries from 2000 to 2032, using multiple linear regression and exponential smoothing models. The analysis identified livestock stock, GDP, agricultural land use, and temperature as significant predictors of milk output, with livestock stock exerting the strongest influence on production. Forecasting results revealed continued production growth in countries like India, Bangladesh, and Vietnam, while others, such as Japan, are projected to experience stagnation or decline. These divergent growth patterns reflect diverse economic, demographic, and climatic conditions across the region. The study's findings have important implications for agricultural policy, suggesting the need for targeted interventions to support sustainable dairy development, particularly in emerging markets. By providing detailed, species-specific production forecasts, this research contributes valuable insights into food security planning and resource allocation in the context of ongoing climate and economic shifts. Abbreviations GDP: Gross Domestic Product IQR: Interquartile Range MAPE: Mean Absolute Percentage Error MAE: Mean Absolute Error RMSE : Root Mean Squared Error VIF: Variance Inflation Factor Declarations Ethics approval and consent to participate This study did not involve human participants or the use of animals, and therefore did not require ethical approval. Consent for publication All authors of this article have agreed to publish this article in Animal Diseases. Clinical trial number Not applicable Availability of data and materials The datasets analyzed during the current study are publicly available from the FAOSTAT database (https://www.fao.org/faostat) and the World Bank Open Data platform (https://data.worldbank.org). Any additional processed data or supporting material can be obtained from the corresponding author upon reasonable request. Competing Interests The authors declare that there are no competing interests related to this study. Funding Preparation of this manuscript was carried out independently and did not involve any financial assistance or funding support. Author Contributions Sohrab Khan: Conceptualization, study design, investigation, data retrieval, data visualization, formal analysis, writing—original draft preparation, writing—review and editing. Beenish Altaf: Conceptualization, study design, formal analysis, writing—original draft preparation, writing—review and editing. Acknowledgements The authors gratefully acknowledge the Food and Agriculture Organization (FAO) and the World Bank for providing access to publicly available datasets through FAOSTAT and the World Bank Open Data platform, which were essential to the analysis and modeling conducted in this study. Authors' information References Abbas Q, Han J, Adeel A, Ullah R (2019) Dairy production under climatic risks: perception, perceived impacts and adaptations in Punjab, Pakistan. 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1","display":"","copyAsset":false,"role":"figure","size":497631,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area map showing the nine selected Asian countries for raw milk production forecasting (2000–2032)\u003c/p\u003e\n\u003cp\u003eCountries shaded in dark blue include India, Pakistan, Bangladesh, Indonesia, Vietnam, China, Japan, Iran, and Türkiye. The remainder of Asia is shown in light grey. Map created using QGIS 3.40.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7409148/v1/8b394b2421d13f7ad52e0038.jpeg"},{"id":92955527,"identity":"7da1b844-9e77-4155-b204-3aa9a78f7946","added_by":"auto","created_at":"2025-10-07 13:58:40","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":454867,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual growth rates in raw milk production by country (2000–2022).\u003c/p\u003e\n\u003cp\u003eEach bar represents the year-on-year percentage change in milk production. Blue bars indicate years of positive growth, while red bars mark years of decline. The dashed red line in each panel represents the country’s average annual growth rate over the period. The solid blue line shows the smoothed trend in annual growth rates, with the surrounding grey band representing the 95% confidence interval.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7409148/v1/7e5adcad0c8fe70363893a27.jpeg"},{"id":92955776,"identity":"eed0eeeb-341d-4a2d-bbcc-13c11de8c15b","added_by":"auto","created_at":"2025-10-07 14:06:40","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":445384,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of milk production by animal species across selected Asian countries (2000–2022)\u003c/p\u003e\n\u003cp\u003eDensity ridgeline plots show the distribution of annual milk output by species (cattle, buffalo, goats, sheep, camels), with colors distinguishing species. Each panel represents a country included in the study. X-axis values reflect production in million tons; species are ordered by median production per country. Data source: FAOSTAT\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7409148/v1/2bd408ae7ec3e5052ae7707f.jpeg"},{"id":92955772,"identity":"488dfed7-26a7-4261-a156-a3770a2c1f09","added_by":"auto","created_at":"2025-10-07 14:06:40","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":433442,"visible":true,"origin":"","legend":"\u003cp\u003eForecasts of buffalo milk production by country (2023–2032)\u003c/p\u003e\n\u003cp\u003eEach panel shows buffalo milk production trends by country. Blue lines indicate training data (2000–2017), purple lines show test data (2018–2022), and orange lines represent forecasts (2023–2032). Shaded areas, with darker and lighter bands showing the 80% and 95% confidence intervals, respectively.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7409148/v1/45bbad1f9d261213b91327cb.jpeg"},{"id":92955531,"identity":"a469a0cf-1a02-498f-90d4-7d6104ec451a","added_by":"auto","created_at":"2025-10-07 13:58:40","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":473064,"visible":true,"origin":"","legend":"\u003cp\u003eForecasts of cattle milk production by country (2023–2032)\u003c/p\u003e\n\u003cp\u003eBlue indicates training data, purple shows test data, and orange represents forecasts. Shaded areas reflect 80% and 95% confidence intervals around the projected values.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7409148/v1/d9448205d3b4ab65c3dc7dee.jpeg"},{"id":92955537,"identity":"b6cf229e-5d6e-477b-8f92-d914e02ef892","added_by":"auto","created_at":"2025-10-07 13:58:40","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":269804,"visible":true,"origin":"","legend":"\u003cp\u003eForecasts of camel milk production in China, India, and Pakistan (2023–2032)\u003c/p\u003e\n\u003cp\u003eBlue = training data, purple = test data, orange = forecast. Shaded areas represent 80% and 95% confidence intervals\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7409148/v1/1adfb451899f10bdd6864d7a.jpeg"},{"id":92955535,"identity":"711c51e0-4127-48ca-8946-c39c4db952ce","added_by":"auto","created_at":"2025-10-07 13:58:40","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":394886,"visible":true,"origin":"","legend":"\u003cp\u003eForecasts of goat milk production by country (2023–2032)\u003c/p\u003e\n\u003cp\u003eBlue shows the training period, purple represents test data, and orange marks the forecasted values. Confidence around forecasts is illustrated by shaded regions at 80% (darker) and 95% (lighter) levels.\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7409148/v1/b87cdf7e723938e2d4720b96.jpeg"},{"id":92955778,"identity":"77ad06f8-dd3c-4dcd-84ca-0fff7bca0956","added_by":"auto","created_at":"2025-10-07 14:06:40","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":368783,"visible":true,"origin":"","legend":"\u003cp\u003eForecasts of sheep milk production by country (2023–2032)\u003c/p\u003e\n\u003cp\u003eBlue = training data, purple = test data, orange = forecast. Shaded bands indicate 80% and 95% confidence intervals\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7409148/v1/1485449b0af1cebc311fc85c.jpeg"},{"id":93489943,"identity":"29e8b800-78a2-4ce6-9224-b149c4cd47af","added_by":"auto","created_at":"2025-10-14 12:03:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4278189,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7409148/v1/ee07ee19-8f8a-43e3-8a41-cc2e59267412.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Regional Trends and Macroeconomic Predictors of Raw Milk Production in Asia from 2000 to 2032","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMilk is widely considered as one of the most nutritionally complete natural foods and plays a key role in ensuring food sustainability and security in rural livelihoods, notably in developing nations (Moran \u0026amp; Morey, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Within the context of Asia, the dairy industry has evolved as a crucial driver of agricultural transformation, driven by elements such as increasing household incomes, rapid population expansion, shift in dietary habits toward more nutrient-rich food sources and urban development (Food and Agriculture Organization, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Although global dairy trade was traditionally led by Western nations, recent decades have undergone a considerable rise in both milk production and consumption across Asia. Notably, countries such as Chine, India, and Pakistan have established themselves as major players to the global dairy supply (Dong, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Singh AB, Bhakar V, Gaurav G et al., 2024). The sector seizes a particular importance for rural economies within the region, facilitating a stable income stream and functioning as a form of economic asset for all smallholder farmers, who contribute to most of the milk production (Baldock, D et al.,1996; Siddiky et al., 2017).\u003c/p\u003e\u003cp\u003eDairy production tendencies across Asian countries differ substantially due to variations in technological development, economic conditions, and policy environments. The most of production systems within the region are led by smallholder operations. In Pakistan and India, for example, small - scale farmers are contributing for more than 80% of the total milk supplied to markets, together contributing around 20% of the worldwide milk production (Schembri et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Staal et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In contrast, China observed a sharp rise in dairy production following the year 2000, predominantly due to reliable government support and conducive policies. Meanwhile, Japan has underwent stagnation in its dairy sector, mainly linked to market saturation and population decline (Douphrate et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). T\u0026uuml;rkiye has demonstrated substantial growth, nearly doubling its dairy production in the past two decades to reach a projected 23\u0026nbsp;million tonnes per annum. On the other hand, Southeast Asian countries including Vietnam and Indonesia have advanced from relatively modest production levels. For instance, Indonesia produced nearly 1 to 2\u0026nbsp;million tonnes every year and reported a growth rate of 8.1% between 2017 and 2020 (Daryanto et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yonar et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMilk production over Asia is considerably shaped by both the extent of technological adoption and environmental conditions. Unpredictable rainfall patterns and increasing temperatures have posed significant constraints to dairy productivity, particularly in smallholder farmers for climate-sensitive countries such as Pakistan (Abbas et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ren et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). On the technological domain, the introduction of artificial insemination techniques, high-yielding cattle breeds, and enhanced feeding approaches has led to remarkable improvements in milk yields (Singh et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, small-scale producers often encounter systemic limitations, including increased feed costs and inadequate access to veterinary interventions (Akzar et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The transition from indigenous to crossbred, higher-yielding cattle has served as a critical role in this shift, adding to over a 200% surge in milk production since the early 1990s (Singh et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Moreover, institutional support mechanisms\u0026mdash; especially cooperative frameworks such as Amul model and the India\u0026rsquo;s National Dairy Development Board (NDDB) \u0026mdash; have been pivotal in facilitating both sectoral expansion and sustained incomes for dairy farmers (Ravichandran et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite notable progress in the dairy sector, there persists a significant lack of country - specific, comprehensive forecasting studies that holistically integrate long - term technological, economic, and environmental variables. To mitigate this research gap, the present study investigates raw milk production trends from 2000 to 2022 across nine key Asian countries: India, Pakistan, Bangladesh, China, Indonesia, Japan, Vietnam, Iran, and T\u0026uuml;rkiye. Quantitative forecasting approaches continue to dominate agricultural research, with a strong emphasis on regression-based causal models and time series analyses (Sun et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Within this framework, exponential smoothing which was typically presented by Robert Goodell Brown (Brown, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1959\u003c/span\u003e) and later expanded by Charles C. Holt (Holt, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1957\u003c/span\u003e), are often employed to produce predictive estimates, as they assign greater importance to recent data points, thereby offering enhanced sensitivity to evolving production trends (Fomby, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Utilizing this methodological approach, the current study seeks to determine and analyze the environmental, economic, and technological determinants of milk production among the targeted countries, while predicting future output trends through 2032. These findings will aim to shape more effective resource allocation, policy development, and strategic planning within the region\u0026rsquo;s dairy industries.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Area and Design\u003c/h2\u003e\u003cp\u003eThis study applies a quantitative longitudinal approach to assess trends in raw milk production from 2000 to 2022 and to predict future trends through 2032 across a representative group of Asian nations. Nine countries were rationally selected to represent the structural diversity and geographical breadth of dairy production models within the region. These consist of Pakistan, India, and Bangladesh from South Asia; Japan and China from East Asia; Vietnam and Indonesia from Southeast Asia; and T\u0026uuml;rkiye and Iran from West Asia. This cross-regional selection includes both emerging dairy markets such as Indonesia and Vietnam, as well as major milk-producing economies such as China and India. The integration of evolving and traditional dairy sectors enables a comprehensive examination of regional production patterns. The study area is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData and Variables\u003c/h3\u003e\n\u003cp\u003eData from 2000 through 2022 were obtained from two primary sources: the Food and Agriculture Organization's Statistical (FAOSTAT) database for milk production and livestock-related indicators, and the World Bank Open Data website for economic and demographic variables (FAO, 2025; World Bank, 2025). The analysis includes the following variables: annual raw milk production (in tonnes); gross domestic product (GDP) (in current US dollars); percentage of the population living in rural areas; percentage of total land area devoted to agriculture; total livestock stock (number of animals); and annual mean surface air temperature (\u0026deg;C). These parameters were selected to account for the multidimensional determinants influencing dairy production, encompassing production output, demographic composition, economic development, livestock resources, land use patterns, and climatic factors across the targeted countries.\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eData Analysis\u003c/h2\u003e\u003cp\u003eAll analyses were conducted using RStudio (Version 2025.5.0.496). The data analysis comprised three main stages: (1) descriptive statistics, (2) multiple linear regression, and (3) exponential smoothing for time series forecasting.\u003c/p\u003e\u003cp\u003eDescriptive statistics were used to summarize the main features of the dataset. Key summary measures included frequency (N), mean, standard deviation (SD), median, and interquartile range (IQR). These statistics provided a foundational understanding of the distribution and central tendency of the variables. Pearson's correlation analysis was conducted to examine linear relationships among the continuous variables, including GDP, rural population, agricultural land, livestock stock, and temperature. The correlation analysis used overall mean values for each variable to evaluate their interrelationships.\u003c/p\u003e\u003cp\u003eMultiple linear regression was employed using the \"lm\" function in R to identify the factors associated with raw milk production. The regression model was specified as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:Y\\:=\\:\\beta\\:₀\\:+\\:\\beta\\:₁X₁\\:+\\:\\beta\\:₂X₂\\:+\\:\\dots\\:\\:+\\:\\beta\\:ₚXₚ\\:+\\:\\epsilon\\:$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere Y represents the dependent variable (raw milk production), X₁, X₂, \u0026hellip;, Xₚ are the independent variables (predictors), β₀ is the intercept, β₁, \u0026hellip;, βₚ are the regression coefficients, and ε is the random error term.\u003c/p\u003e\u003cp\u003eTo assess multicollinearity among the predictor variables, the Variance Inflation Factor (VIF) was calculated using the \"vif\" function. VIF values exceeding the commonly accepted thresholds of 5 or 10 were considered indicative of potential multicollinearity issues (O'Brien, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eExponential smoothing was applied using annual data of raw milk production, as agricultural production statistics are typically reported on a yearly basis. This approach ensures consistency and comparability across years and countries. Forecast accuracy was evaluated using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE).\u003c/p\u003e\u003cp\u003eThe exponential smoothing model was defined as:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:ŷₜ₊₁\\:=\\:\\alpha\\:yₜ\\:+\\:(1\\:-\\:\\alpha\\:)ŷₜ$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere ŷₑ₊₁ is the forecast for the next time period, yₜ is the actual value at time t, ŷₜ is the forecasted value at time t, and α is the smoothing constant (0\u0026thinsp;\u0026lt;\u0026thinsp;α\u0026thinsp;\u0026lt;\u0026thinsp;1), representing the weighting applied to the most recent observation.\u003c/p\u003e\u003cp\u003eAll statistical analyses and visualizations were conducted using R packages including \"psych\", \"ggplot2\", \"dplyr\", \"car\", \"forecast\", \"corrplot\", and \"stats\" (Fox \u0026amp; Weisberg, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Hyndman \u0026amp; Khandakar, 2008; R Core Team, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Revelle, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wei \u0026amp; Simko, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wickham, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wickham et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eDescriptive Summary\u003c/h2\u003e\u003cp\u003eDescriptive statistics of the study variables are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Milk production and livestock stock demonstrated considerable variability across observations. Economic, demographic, and environmental variables such as GDP, rural population percentage, agricultural land, and surface temperature showed relatively stable patterns across countries.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive statistics for study variables (2000\u0026ndash;2022)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrequency (N)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;Standard Deviation (SD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMedian\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eInter-quartile range (IQR) (25% \u0026ndash; 75%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRaw Milk Production (tonnes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e759\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7622883\u0026thinsp;\u0026plusmn;\u0026thinsp;17627962\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e566019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e128000\u0026ndash;6049365\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGross Domestic Product (GDP)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1113.19\u0026thinsp;\u0026plusmn;\u0026thinsp;756.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e837.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e485.25\u0026ndash;1730.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural population (percentage of total population)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68.64\u0026thinsp;\u0026plusmn;\u0026thinsp;5.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e68.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e64.53\u0026ndash;72.84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAgricultural land (percentage of total land area),\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e71.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e70.60\u0026ndash;72.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStock (total animal count)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e759\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37263603\u0026thinsp;\u0026plusmn;\u0026thinsp;50843889\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17611392\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2897460\u0026ndash;42608000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurface air temperature (Annual average)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25.51\u0026ndash;25.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eNational Growth Patterns in Milk Production (2000\u0026ndash;2022)\u003c/h2\u003e\u003cp\u003eAnnual milk production growth rates across the nine selected Asian countries from 2000 to 2022 are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The results reveal considerable variation in growth trajectories across regions. Vietnam recorded the highest average annual growth rate at 13.2%, followed by Bangladesh (9.6%) and China (6.3%), indicating strong upward trends in these countries over the period. India maintained a steady and positive growth pattern, with an average of 5% annually, reflecting consistent year-on-year increases. Pakistan and T\u0026uuml;rkiye reported moderate average growth rates of 4.1% and 3.9%, respectively, while Indonesia and Iran showed relatively lower average rates of 2.2% and 2%. In contrast, Japan was the only country to experience an overall negative trend, with an average annual growth rate of \u0026minus;\u0026thinsp;0.5%, marked by frequent declines across multiple years.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSpecies-Level Milk Production Distribution\u003c/h3\u003e\n\u003cp\u003eMilk production distribution by animal type across the selected countries is displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The density plots illustrate the contribution of five animal species - cattle, buffalo, goats, sheep, and camels - to overall milk output. Cattle emerged as the predominant source of milk in the majority of countries, particularly in Japan, China, and Vietnam, where other species contribute minimally. In contrast, countries like India and Pakistan displayed more diverse production profiles, with significant contributions from buffalo, goats, and camels in addition to cattle. Buffalo milk production was especially notable in South Asian countries such as India, Pakistan, and Bangladesh, while goat and sheep milk showed visible presence in Iran, T\u0026uuml;rkiye, and Bangladesh. Camel milk production appeared in only a few countries, including India, Pakistan, and China, with relatively low but measurable output. Southeast Asian countries like Indonesia and Vietnam demonstrated more modest but multi-source production, reflecting emerging dairy sectors.\u003c/p\u003e\n\u003ch3\u003eRegression Model of Milk Production Determinants\u003c/h3\u003e\n\u003cp\u003eAs presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, model identified four statistically significant predictors of raw milk production: gross domestic product (GDP), agricultural land use, livestock stock, and surface temperature. GDP showed a positive association \u003cem\u003e(β\u0026thinsp;=\u0026thinsp;490, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e), as did agricultural land \u003cem\u003e(β\u0026thinsp;=\u0026thinsp;1.579e⁵, p\u0026thinsp;=\u0026thinsp;0.00062\u003c/em\u003e). Livestock stock had the largest standardized effect (\u003cem\u003eβ\u0026thinsp;=\u0026thinsp;0.193, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e). Surface temperature exhibited a curvilinear relationship, with a positive linear coefficient (\u003cem\u003eβ\u0026thinsp;=\u0026thinsp;3.375e⁶, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e) and a negative quadratic term (\u003cem\u003eβ = \u0026minus;7.985e⁴, p\u0026thinsp;=\u0026thinsp;0.00011\u003c/em\u003e). The coefficient for rural population was not statistically significant (\u003cem\u003eβ\u0026thinsp;=\u0026thinsp;4.649e⁴, p\u0026thinsp;=\u0026thinsp;0.430\u003c/em\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultiple Linear Regression Estimates for Predictors of Raw Milk Production (2000\u0026ndash;2022) *Indicates a significant variable (\u003cem\u003ep value\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStandard Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003et value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePr (\u0026gt;| t |)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003eRaw Milk Production\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Intercept)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;4.348e\u003csup\u003e07\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.857e\u003csup\u003e06\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-6.341\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e490\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.075e\u003csup\u003e02\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.648\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRural Pop\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.649e\u003csup\u003e04\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.889e\u003csup\u003e04\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.789\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.430\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAgri Land\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.579e\u003csup\u003e05\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.596e\u003csup\u003e04\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.436\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.00062*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStock\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.193\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e17.070\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLinear temp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.375e\u003csup\u003e06\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.917e\u003csup\u003e05\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.880\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuadratic temp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-7.985e\u003csup\u003e04\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.054e\u003csup\u003e04\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-3.888\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.00011\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eForecasts of Milk Production by Species (2023\u0026ndash;2032)\u003c/h2\u003e\u003cp\u003eForecast outputs for species-specific milk production - covering cattle, buffalo, goat, sheep, and camels - across the selected Asian countries are shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e through \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. These projections span 2023 to 2032 and were generated using exponential smoothing models applied to production data from 2000 to 2022. The figures include training data, test data, and forecasted values, with 80% and 95% confidence intervals included. Variation is observed in projected trends across species and countries. Cattle and buffalo milk production is projected to increase in India, Pakistan, and Vietnam. Other species-country combinations show more stable forecast patterns. Forecast values for goats, sheep, and camels are generally lower in magnitude and vary between countries. Confidence intervals widen across all forecasts toward the end of the projection period.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eOverview of key findings\u003c/h2\u003e\u003cp\u003eThis study provides a robust, data-driven analysis of raw milk production trends across nine key Asian countries from 2000 to 2022, with forecasts extending to 2032. This research contributes to filling the notable gap in long - term, country - specific dairy forecasting models in Asia. These insights - particularly regarding the roles of livestock stock, GDP, agricultural land use, and temperature - carry significant implications for agricultural policy, rural development, and food security. The analysis identified livestock stock, GDP, agricultural land use, and surface temperature as statistically significant predictors of milk production. Forecasts revealed country-specific trajectories, with strong growth expected in India, Vietnam, and Bangladesh, while other countries such as Japan showed stagnation or decline.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eGrowth Trends in Milk Production and Distribution (2000\u0026ndash;2022)\u003c/h2\u003e\u003cp\u003eBetween 2000 and 2022, raw milk production expanded significantly across most of the nine Asian countries, with notable growth in India, Vietnam, China, and Bangladesh. India maintained its position as the world's leading milk producer, supported by widespread smallholder involvement, improved cattle genetics, and institutional backing through entities like the National Dairy Development Board and Amul (Ravichandran et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Birthal et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Similarly, Bangladesh and Vietnam experienced accelerated growth due to urbanization, evolving consumer preferences, and investments in dairy infrastructure (Delgado et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Daryanto et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). China's milk production increase was driven largely by industrialized production systems and strong policy support (Fuller et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn contrast to the upward trends observed in countries like India and Vietnam, Japan experienced a steady decline in milk output. This decline can be attributed to an aging rural workforce, urban migration, and stagnant demand for dairy products (Suzuki and Kaiser, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The contrasting growth trajectories - rapid in some countries, stagnant in others - underscore how demographic shifts, economic conditions, and policy support shape dairy sector performance (Gerosa \u0026amp; Skoet, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRegarding species distribution, cattle remained the dominant source of milk across most countries, while buffalo, goat, and sheep contributed significantly to South Asian nations such as India and Pakistan. These multi-species systems reflect both ecological adaptability and cultural preferences (Thornton, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Devendra, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Goat and sheep milk also demonstrated relevance in regions with arid climates or limited grazing resources, emphasizing their resilience and economic importance in marginal farming systems (Haenlein, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003ePredictors associated with Raw Milk Production\u003c/h2\u003e\u003cp\u003eAs presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, multiple regression model identified livestock stock (\u003cem\u003eβ\u0026thinsp;=\u0026thinsp;0.193, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e) as the most statistically significant predictor of raw milk production. This finding aligns with Singh et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), who attributed India's 200% production surge since the 1990s to crossbred cattle adoption.\u003c/p\u003e\u003cp\u003eTemperature exhibited a complex relationship with milk production, showing positive linear effects (\u003cem\u003eβ\u0026thinsp;=\u0026thinsp;3.375e⁶, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e) but negative quadratic impacts (\u003cem\u003eβ = \u0026minus;7.985e⁴, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e). This dual modeling revealed a nuanced relationship: while moderate warmth may enhance productivity, excessive heat likely imposes physiological stress on animals. This pattern is supported by empirical studies in tropical climates (Nardone et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The findings mirror those of Abbas et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and Ren et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), who found that rising temperatures initially boost pasture growth in temperate zones, such as China's northern plains, but severely stress livestock in arid regions, such as Pakistan's Sindh province.\u003c/p\u003e\u003cp\u003eGDP was positively associated with milk output, reflecting intensified production in rapidly industrializing economies like China and Vietnam, where capital investment enables large-scale dairy operations (Dong, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). However, India's smallholder-dominated sector - which contributes 80% of national output - remains buffered against economic volatility through cooperative models such as Amul, as observed by Ravichandran et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This contrast helps explain why GDP - driven intensification is less pronounced in South Asia, where cooperative models provide stability for smallholder operations.\u003c/p\u003e\u003cp\u003eAgricultural land also showed a strong positive association with milk production, suggesting that land availability continues to be a critical enabler of dairy expansion. This finding is particularly relevant in countries like Iran and T\u0026uuml;rkiye, where land-use policies have supported pasture development and fodder cultivation. Prior research by Gerber et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) similarly identified land access as a bottleneck in intensifying livestock systems in developing regions.\u003c/p\u003e\u003cp\u003eSurprisingly, rural population share was not a significant predictor of milk production. This may reflect a decoupling of rurality from production intensity, as urban and peri - urban dairy systems gain prominence and mechanization reduces labor dependence. The finding also suggests that having a large rural population does not guarantee higher output without supportive infrastructure and market access.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eMilk Production Forecasts (2022\u0026ndash;2032) Species-Specific and Regional Dynamics\u003c/h2\u003e\u003cp\u003eTo project future trends, the study employed exponential smoothing methods, which have gained widespread acceptance in agricultural forecasting due to their ability to capture underlying trends while maintaining computational efficiency and interpretability (Hyndman \u0026amp; Athanasopoulos, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The exponential smoothing models demonstrated varying predictive accuracy across countries and species, with Mean Absolute Percentage Error (MAPE) values ranging from 0.24\u0026ndash;34.89% (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Lower error rates observed in established dairy markets such as Pakistan (0.37% for buffalo, 0.43% for cattle) and India (2.88% for buffalo, 5.92% for cattle) indicate more stable production patterns compared to emerging markets. This variation reflects the heterogeneous nature of Asian dairy systems and aligns with established literature demonstrating higher forecast reliability in mature dairy industries (Thornton, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eForecast Accuracy Metrics for Species-Level Milk Production by Country (2023\u0026ndash;2032)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpecies\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCountry\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRMSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMAE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMAPE\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBuffaloe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBangladesh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54128.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e52490.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18.84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBuffaloe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChina\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e210384.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e178727.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBuffaloe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3062759\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2828636\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBuffaloe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndonesia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13281.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11762.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBuffaloe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIran\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6114.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3738.981\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBuffaloe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePakistan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e164625.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e130277.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBuffaloe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u0026uuml;rkiye\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24245.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18136.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e34.89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBuffaloe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVietnam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1303.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1221.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCattle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBangladesh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2017118\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1949680\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19.40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCattle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChina\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6369367\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5268548\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCattle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7820054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6646749\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.92\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCattle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndonesia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25110.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23457.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.46\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCattle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIran\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e699156.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e534931.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCattle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJapan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e418394.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e354375\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCattle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePakistan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e134100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e96800.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCattle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u0026uuml;rkiye\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1143513\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1056784\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCattle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVietnam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66228.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56616.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGoats\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBangladesh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e153521.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e121128.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGoats\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChina\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5418.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4927.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGoats\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e580514.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e419439.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGoats\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndonesia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6323.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4781.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGoats\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIran\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27479.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24767.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGoats\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePakistan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29860\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26571.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGoats\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u0026uuml;rkiye\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61529.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e54981.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSheep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBangladesh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10914.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10585.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSheep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChina\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e81606.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60785.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.77\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSheep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndonesia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6570.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5336.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.46\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSheep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIran\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e92088.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e86252.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24.74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSheep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePakistan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1971.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1840\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSheep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u0026uuml;rkiye\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e208937.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e200063.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16.87\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCamels\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChina\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3547.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3045.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCamels\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e957.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e757.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCamels\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePakistan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2385.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2215.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eCattle milk production forecasts reveal divergent regional trajectories through 2032. India and China demonstrate sustained upward trends, reflecting expanding domestic demand and continued infrastructure investments. Bangladesh exhibits particularly robust growth projections, consistent with the country's rapidly developing dairy sector driven by urbanization and rising incomes (Hemme \u0026amp; Otte, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Conversely, Japan shows stable or slightly declining production, reflecting market maturity and demographic challenges typical of developed dairy economies (Britt et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBeyond cattle, buffalo milk production continues to grow strongly in South Asia, with India reinforcing its position as the world's largest producer. Pakistan's forecasts indicate continued expansion at moderate rates, reflecting sector commercialization and improved management practices (Borghese \u0026amp; Mazzi, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The sustained buffalo milk growth emphasizes the species' continued importance in regional dairy systems.\u003c/p\u003e\u003cp\u003eSmall ruminant milk production shows distinct regional patterns, with Iran and Turkey demonstrating continued growth in goat and sheep milk sectors. China's goat milk production indicates steady expansion, reflecting growing consumer interest in specialty dairy products and niche market development (Silanikove et al., 2019). Although comparatively minor in volume, camel milk production is showing modest but consistent growth in Pakistan, aligning with increasing commercial interest in camel dairy operations (Faye, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThese forecasts underscore significant disparities across Asia, reflecting the uneven development of dairy sectors. South Asian countries demonstrate the most robust growth projections across multiple species, reflecting favorable demographic trends, expanding consumer markets, and supportive government policies (Delgado et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). East Asian patterns vary significantly, with China showing continued expansion while Japan exhibits stability, reflecting different development stages and demographic trends. Southeast Asian countries present moderate but consistent growth patterns, with the market expected to grow annually by 7.56% and projected growth at a Compound Annual Growth Rate (CAGR) of 3.14% during 2025\u0026ndash;2033. This trend is particularly evident in Vietnam and Indonesia, identified by Bourdini\u0026egrave;re and Garg (2023) as promising dairy markets in Southeast Asia, with aggressive growth trajectories fueled by demographic expansion, evolving consumer preferences, and increased investment in production and distribution infrastructure.\u003c/p\u003e\u003cp\u003eSeveral limitations should be noted. First, the analysis relies on national-level data, which may obscure important sub-national disparities in production systems, infrastructure access, and climatic vulnerability. Second, the regression model did not incorporate key policy variables or consumer behavior dynamics, both of which can significantly influence dairy production outcomes. Third, the scope of variables included in the model - while statistically significant - remains limited. Important factors such as feed quality and availability, access to veterinary care, and disease management practices were not assessed due to data constraints. Future research should integrate these production-level determinants to provide a more comprehensive understanding of the drivers of milk output across diverse agroecological and socioeconomic contexts.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study investigated raw milk production trends across nine Asian countries from 2000 to 2032, using multiple linear regression and exponential smoothing models. The analysis identified livestock stock, GDP, agricultural land use, and temperature as significant predictors of milk output, with livestock stock exerting the strongest influence on production. Forecasting results revealed continued production growth in countries like India, Bangladesh, and Vietnam, while others, such as Japan, are projected to experience stagnation or decline. These divergent growth patterns reflect diverse economic, demographic, and climatic conditions across the region. The study's findings have important implications for agricultural policy, suggesting the need for targeted interventions to support sustainable dairy development, particularly in emerging markets. By providing detailed, species-specific production forecasts, this research contributes valuable insights into food security planning and resource allocation in the context of ongoing climate and economic shifts.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eGDP: Gross Domestic Product\u003c/p\u003e\n\u003cp\u003eIQR: Interquartile Range\u003c/p\u003e\n\u003cp\u003eMAPE: Mean Absolute Percentage Error\u003c/p\u003e\n\u003cp\u003eMAE: Mean Absolute Error\u003c/p\u003e\n\u003cp\u003eRMSE : Root Mean Squared Error\u003c/p\u003e\n\u003cp\u003eVIF: Variance Inflation Factor\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not involve human participants or the use of animals, and therefore did not require ethical approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors of this article have agreed to publish this article in Animal Diseases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study are publicly available from the FAOSTAT database (https://www.fao.org/faostat) and the World Bank Open Data platform (https://data.worldbank.org). Any additional processed data or supporting material can be obtained from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no competing interests related to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePreparation of this manuscript was carried out independently and did not involve any financial assistance or funding support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSohrab Khan: Conceptualization, study design, investigation, data retrieval, data visualization, formal analysis, writing\u0026mdash;original draft preparation, writing\u0026mdash;review and editing. Beenish Altaf: Conceptualization, study design, formal analysis, writing\u0026mdash;original draft preparation, writing\u0026mdash;review and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge the Food and Agriculture Organization (FAO) and the World Bank for providing access to publicly available datasets through FAOSTAT and the World Bank Open Data platform, which were essential to the analysis and modeling conducted in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; information\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbbas Q, Han J, Adeel A, Ullah R (2019) Dairy production under climatic risks: perception, perceived impacts and adaptations in Punjab, Pakistan. 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Indian J Anim Sci 92:105-111. https://doi.org/10.56093/ijans.v92i1.120934\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Raw Milk, Asia, Forecasting, Dairy Sector, Food Security","lastPublishedDoi":"10.21203/rs.3.rs-7409148/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7409148/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study presents a comprehensive longitudinal analysis of raw milk production trends and forecasts across nine representative Asian countries - India, Pakistan, Bangladesh, China, Indonesia, Japan, Vietnam, Iran, and T\u0026uuml;rkiye - spanning the period 2000 to 2032. Employing multiple linear regression and exponential smoothing techniques, the research investigates the macro-level determinants of milk output, integrating economic, demographic, and environmental variables. The regression model identified livestock stock (\u003cem\u003eβ\u0026thinsp;=\u0026thinsp;0.193\u003c/em\u003e, \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e), agricultural land use (\u003cem\u003eβ\u0026thinsp;=\u0026thinsp;1.579e⁵, p\u0026thinsp;=\u0026thinsp;0.00062\u003c/em\u003e), gross domestic product (GDP) (\u003cem\u003eβ\u0026thinsp;=\u0026thinsp;499.6, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e), and temperature (\u003cem\u003eβ₁ = 3.375e⁶; β₂ = \u0026minus;7.985e⁴\u003c/em\u003e) as statistically significant predictors, collectively explaining 33% of the variance in production (R-Squared (R\u0026sup2;)\u0026thinsp;=\u0026thinsp;0.33). Forecasting results using exponential smoothing demonstrated high predictive accuracy (e.g., Mean Absolute Percentage Error (MAPE)\u0026thinsp;=\u0026thinsp;0.37% for Pakistan, 2.88% for India), enabling reliable projections of species-specific milk output. Projections indicate continued expansion in South Asia - particularly for cattle and buffalo milk - driven by demographic growth and institutional support, while East Asia reveals divergent paths, with China showing modest growth and Japan facing prolonged decline. Emerging markets in Southeast Asia, notably Vietnam and Indonesia, exhibit promising but variable trends. The findings offer region-wide implications for evidence-based policy development, resource allocation, and the design of climate-resilient, economically viable dairy strategies across heterogeneous production systems in Asia.\u003c/p\u003e","manuscriptTitle":"Regional Trends and Macroeconomic Predictors of Raw Milk Production in Asia from 2000 to 2032","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-07 13:58:35","doi":"10.21203/rs.3.rs-7409148/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6441f59a-3fdf-4fb0-9b87-07fa1a0e0947","owner":[],"postedDate":"October 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-14T11:54:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-07 13:58:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7409148","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7409148","identity":"rs-7409148","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}