Variability In Production Research Articles

Bushfire exposure is associated with darker color of beef loin at grading

The effect of bushfires (also known as wildfires) on the Australian livestock industry extends beyond the direct loss of animals and farming assets, with anecdotal reports of increased incidence of dark meat color defects in beef carcasses at grading (AUSMEAT color score >3) from bushfire affected cattle. To understand the association between bushfire exposure and meat color, an investigation using historical MSA (Meat Standards Australia) grading data, pasture data and bushfire data over two bushfire seasons (2018–2019, 2019–2020) was conducted. For bushfire affected beef cattle (n = 451 299), predictive models were developed to estimate the association between bushfire exposure and (1) loin color at grading, and (2) the incidence of ‘dark color’ defects. Both models incorporated bushfire exposure, production, and animal variables, with hierarchical random effects specified as plant/processing date/consignment. For the two predictive models, distance of the animal's originating property from closest bushfire (km), the days the animal was exposed to bushfire, feed type (grass, grain), the use of hormone growth promotants (HGPs; no, yes), the interaction of distance of property from closest bushfire and feed type, and days of bushfire and HGP treatment, were all significant predictors of loin color outcomes (P < 0.05 for all). Model 1 indicated that as distance from closest bushfire decreased, loin from grain-fed animals had darker color (P < 0.001), and that as days of bushfire exposure increased, HGP treatment exacerbated dark color outcomes for grain-fed animals but had little effect on the consistently high color scores of grass-fed HGP treated animals (P = 0.001). For model 2, the highest predicted proportion of dark color defects (11.8 %) was for grass-fed, HGP treated cattle, 10 km from the closest bushfire and exposed to 150 days of bushfire. The lowest predicted proportion of dark colour carcasses (1.3 %) was for grain fed, non-HGP treated cattle, 30 km from the closest bushfire, with no change associated with increasing days of bushfire. Cattle can be categorized in order of increasing susceptibility to dark color defects as a result of being exposed to bushfires; grain-fed cattle with no HGPs were the least susceptible to dark color defects, and grass-fed cattle with HGP treatment were the most susceptible. Remediation strategies are discussed in relation to cattle susceptibility to adverse loin meat color outcomes associated with bushfire exposure.

Does Climate Change and Energy Consumption Affect the Food Security of European Union Countries? Empirical Evidence from a Panel Study

This study aims to provide empirical evidence on whether using renewable and non-renewable energy, CO2 emissions, and the resulting changes in temperature and precipitation can alter cereal yields in European Union countries. To this end, a panel data set covering 1992–2021 has been used to analyse the impact of climate change and economic factors on cereal production in European Union countries. The study employed various analytical techniques to better understand the impact of climate change and economic factors on cereal crop yields. These included using annual average temperature and rainfall to measure climate change, energy consumption, and economic growth to analyse the economic aspects. To achieve the objective, feasible generalised least squares (FGLS) models with robust standard errors and bootstrap and a quantile regression (QR) model with marginal analysis were employed. The Westerlund cointegration test confirmed the presence of cointegration between cereal production and climate variables, economic growth, and energy consumption. The results indicate that increases in temperature and precipitation may contribute to increased cereal production in European Union countries. In contrast, energy consumption, including renewable energy and CO2 emissions, may have a nonlinear effect on cereal production. Quantile regression analysis suggests climate change may impact crop yields most in countries with low and moderate cereal production per hectare. Increased CO2 emissions may increase yields in the initial period, but excessive CO2 may negatively impact cereal production in the long term. It would be advisable for countries with low and moderate crop productivity to consider implementing technological advances and combating global warming by modernising cultivation methods and making greater use of renewable energy sources.

A Theoretical and Practical Approach to Exponential Relation between Innovation and Sustainable Development

A Aside from the political, geographic, economic, legal, social, and cultural problems that every nation, state, and government faces, it is evident that sustainable development is the key to sustainability, progress, and improving the quality of life. Achieving SDG targets has been seen as the primary trend for the past 15 years and the foreseeable future. In this direction, with an eye towards the fifth industrial revolution, innovation has been viewed as one of the primary production variables, along with land, labor, capital, and entrepreneurship, to help address current and future difficulties facing humanity. This study aimed to shed light on the connections between innovation and sustainable development. To test Hypothesis H1, which states a positive solid relationship between the SDG Index and ISO 9001 Index, we conducted a statistical analysis using regressive analysis between the Sustainable Development Goals Index and Innovation Index. This was done to compare the results to Hypothesis H0, which suggests no such relationship exists. The research's findings indicate that scientific management of the factors of production opens up possibilities for long-term sustainable development, ensuring the prosperity of society and everyday life for future generations, encouraging economic growth, and improving the quality of life without endangering the environment. Innovation, embodied in the ISO 56000 family of standards, is applied as an efficient and effective tool, which is urgently needed. The overall conclusion of the study – which also serves as a practical and social contribution to the field – is that, to achieve and sustain sustainable development scenarios, all interested parties – individuals, public and private institutions, decision-makers, and civil society – should look forward to ensuring that the SDG and innovation are built and maintained. Innovation principles can be applied as effective and efficient tools.

Phosphorus Content of Several Plant-Based Yogurts

ObjectiveIn people with chronic kidney disease (CKD), hyperphosphatemia is an independent risk factor for mortality in observational studies. Despite not having proven benefit, dietary control of phosphorus is thought to be essential in CKD. Though dietary and serum phosphorus are correlated, ingested phosphate is less bioavailable from plant-foods rich in phytate than from animal and processed foods. Yogurt is considered a useful food due to its relatively low phosphorus and high protein content but may be detrimental since the protein content is from animal sources. Instead, plant-based yogurts (PBYs) may offer similar benefits without the associated downsides of animal protein in kidney disease, but little is known about the phosphorus contents of PBYs. Design and MethodsProtein contents and phosphorus additives were obtained from nutrition labels of several, widely available PBYs of almond, cashew, oat, coconut, and soy substrates. Phosphorus content was measured via emission spectrometry by Eurofins (Madison, WI). Based on these data, phosphorus-to-protein ratio (PPR) for each PBY was calculated. ResultsPhosphorus was highest in Silk Soy Strawberry, then Silk Almond Strawberry and Siggi’s Coconut Mixed Berries. Phosphorus was lowest in So Delicious Coconut Strawberry, then Oatly Oat Strawberry, Forager Cashew Strawberry, and Kite Hill Almond Strawberry. According to the ingredient labels, Silk Soy Strawberry, Silk Almond Strawberry, and Oatly Oat Strawberry contained phosphorus additives while Siggi’s Coconut Mixed Berries contained pea protein additives. Of PBYs from the same substrate class, So Delicious Coconut Strawberry and Siggi’s Coconut Mixed Berries had the largest differences in their phosphorus and protein contents. These seven samples’ PPR ratios were higher than that of dairy yogurt values reported from the literature, including Stonyfield Organic Oikos Strawberry, Chobani Nonfat Strawberry, and Yoplait Greek Strawberry. ConclusionLow phosphorus-to-protein ratio (PPR) foods are emphasized for people with CKD. Siggi’s Coconut Mixed Berries had the lowest PPR. However, So Delicious Coconut Strawberry had the highest ratio which underscores product variability despite both using the same PBY substrate. Of the samples analyzed, Siggi’s Coconut Mixed Berries may be the most desirable for patients with CKD because its PPR was the lowest.

Stochastic modeling of eastern oyster (Crassostrea virginica) production in a Mexican tropical coastal lagoon using alternative stocking density management schemes

A stochastic model was developed to analyze Crassostrea virginica production when cultivated at three stocking densities, defined as the area occupation (50, 75, and 100%) in Nestier-type trays, using juvenile oysters with an initial size (height) of 29.40 ± 0.18 mm. A 25-week grow-out was first conducted in the Rio Lagartos lagoon, showing that final oyster size was inversely related to density (P &lt; 0.05), ranging from 61.87 mm (100% density) to 68.00 mm (50% density). At the same time, mortality was negligible (lower than 1.0%). The environmental conditions in the lagoon were stable, and the oyster growth rate positively correlated with water temperature and pH. Low pH was associated with slow oyster growth, possibly as an adverse effect of acidic conditions on the oyster shell formation. The model indicated that, for an initial 100,000 oyster population, the maximum production (oyster dozens) is obtained after 20 weeks (8310 at 50% density) and 25 weeks (8250 at 75%, and 6670 at 100% density). There was a 100% probability of oysters reaching a minimum commercial size (MCS) of 60.00 mm when using the 50 and 75% densities and 80.6% when using 100%. The model estimated that, for the maximum density, extending the grow-out up to 32 weeks would allow reaching MCS with 100% probability, although this should be experimentally confirmed. The random variability of oyster production in 50 and 75% densities was minimal, while at 100% density, it was extremely high. A sensitivity analysis indicated that for 50 and 75% densities, the main stochastic elements influencing production, are related to oyster mortality, while at 100% density, they are related to oyster growth.

IMPACT OF CLIMATE CHANGE ON RICE PRODUCTIVITY IN KHYBAR PAKHTUNKHWA, PAKISTAN

Climate change is currently the most significant concern on the planet. This study examined the impacts of climate change on rice yield across agro-ecological zones of Khybar Pakhtunkhwa, Pakistan. This study focused only on four districts across four agroecological zones in Khybar Pakhtunkhwa based on the availability of data on areas under rice, rice production, and meteorological variables. Districts Swat from Zone A, Mansehra from Zone B, Mardan from Zone C, and D.I. Khan from Zone D were selected. This study covered 33 years (1986-2018) of data across these four districts. Panel unit root tests of LLC and IPS suggested that all variables included in the model were stationary. Durbin Wu Hausman's test favored the fixed effect model compared to the random effect model. Diagnosis of fixed effect model results showed that it was plagued with problems of cross-sectional dependency, heteroscedasticity, and autocorrelation. Final findings were estimated using the Feasible Generalized Least Squares (FGLS) model to overcome the problems in the fixed effect model. Results revealed that area under rice crop has a positive and significant effect on rice yield. The estimated critical temperature for the maximum yield of rice was 34.48 ºC in the sowing stages, 35.85 ºC in the vegetative stages, and 29.43 ºC in the maturity stages, and Rainfall in the vegetative stages was 152 mm. It is concluded that rice yield showed a decline when temperature and rainfall exceeded the critical levels. The government needs to use afforestation and other appropriate measures to keep the temperature from rising in these three zones (zones A, C and D). The extension department needs to inform rice growers about adaptation strategies to climate change.

Sustainability of Adoption of New Improved Rice Variety Innovation in West Kalimantan Coastal Areas: Review of Social and Cultural Aspects

As a staple food in Indonesia, rice has significant socio-economic value, especially in regions such as West Kalimantan. However, the variability of rice production poses a challenge to food security and economic stability. This study assesses the sustainability of the adoption of new improved rice varieties (VUBs) in West Kalimantan, focusing on social and cultural aspects using Multi-Aspect Sustainability Analysis (MSA). Focus group discussions and expert interviews were used to identify important factors affecting VUB adoption. The results indicated a sustainable adoption status (54.17%), with potential for improvement (58.33%) in the future. Farmer education emerged as the most sensitive factor, highlighting the need for educational interventions to improve technology access and adoption. In addition, farmers' knowledge of VUB, culture of mutual cooperation, the absence of social conflict, and positive consumer preference for VUB rice are also critical for adoption. Recommendations include targeted educational interventions, improved knowledge dissemination strategies, consideration of the cultural context, conflict management approaches, and market-oriented strategies to sustainably increase VUB adoption. Addressing these factors can contribute to sustainable agriculture and rural development in West Kalimantan and similar contexts, ensuring long-term benefits for farmers and food security.

Workplace accidents, economic determinants and underreporting: an empirical analysis in Italy

PurposeThis paper proposes an analysis of occupational accidents in Italy at the regional level. For this purpose, our panel is composed of 20 regions over the 2010–2019 time span.Design/methodology/approachWe apply different econometric estimation techniques (pooled OLS model, panel fixed and random effects models and semiparametric fixed model) using INAIL and ISTAT data. Our models investigate workplace accidents at the regional level by accounting for socioeconomic, labour market and productive system variables and controlling for possible underreporting bias.FindingsOverall results reveal the existence of a relevant under-notification phenomenon of accidents at work with respect to moderate accidents, that is higher especially for the southern regions of Italy. However, when considering as outcome variable an alternative set of more severe workplace accidents our model specification remains highly jointly statistically significant. Among our main findings, the analysis shows that worker skills (blue collar) strongly affect the regional pattern of workplace accidents, i.e. an increase of 1% of low paid employees generates about an increase of 1.8 severe workplace accidents per thousand workers. Moreover, we provide evidence that the size of the firm is inversely related to the occupational accident rates. Finally, our results highlight a nonlinear relationship between GDP and occupational accidents for the Italian regional context, confirmed by the high statistical significance of the quadratic term in all the estimated linear models and by the semi-parametric analysis.Originality/valueA first element of originality of our study consists of investigating the macro determinants of occupation accidents at a regional Italian level. Second, the empirical literature (Boone and Van Ours, 2006) highlights the possible bias of underreporting behaviours on nonfatal accidents in contrast to fatal accidents that are always reported. From this perspective, we have identified a few analyses (namely, Boone et al., 2011) considering different accident sets characterised by different severity degrees. Thus, this paper contributes to the literature considering five alternative subsets of accidents stratified by degree of severity (i.e. moderate, severe, moderate plus severe, severe plus fatal and total accident rates) to test for possible underreporting bias affecting our econometric model.

Effects of Soil Moisture and Atmospheric Vapor Pressure Deficit on the Temporal Variability of Productivity in Eurasian Grasslands

Effects of Soil Moisture and Atmospheric Vapor Pressure Deficit on the Temporal Variability of Productivity in Eurasian Grasslands

Technological and Nutritional Aspects of Bread Production: An Overview of Current Status and Future Challenges.

Bakery products, especially bread, exist in many homes worldwide. One of the main reasons for its high consumption is that the main raw material is wheat, a cereal that can adapt to a wide variety of soils and climates. However, the nutritional quality of this raw material decreases during its industrial processing, decreasing the value of fibers, proteins, and minerals. Therefore, bread has become a product of high interest to increase its nutritional value. Due to the high consumption of bread, this paper provides a general description of the physicochemical and rheological changes of the dough, as well as the sensory properties of bread by incorporating alternative flours such as beans, lentils, and soy (among others). The reviewed data show that alternative flours can improve fiber, macro, and micronutrient content. The high fiber content reduces the quality of the texture of the products. However, new processing steps or cooking protocols, namely flour proportions, temperature, cooking, and fermentation time, can allow adjusting production variables and optimization to potentially overcome the decrease in sensory quality and preserve consumer acceptance.

Variation of ecosystem resilience across the anthropogenic biomes of India: A comprehensive analysis

Quantifying ecosystem resilience under drought is crucial for sustainable development strategies. This study aims to investigate the spatial and temporal variability of Net Primary Productivity (NPP) across anthropogenic biomes in India (2000 to 2020) and to understand the post-drought long-term ecosystem resilience. A time series study of monthly precipitation, standardized precipitation index (SPI), and NPP were applied to understand ecosystem resilience across twenty anthropogenic biomes. Mann-Kendall test was used to quantify the magnitude and direction of the trend. In addition, bivariate raster maps of mean precipitation and soil moisture were presented in relation to ecosystem resilience in India. The forested areas in the Himalayan region and the Western Ghats of India were identified with resilient ecosystem that can withstand climate change. However, the croplands and rangelands were non-resilient to drought, making them vulnerable to climate change. Northern and western part of India falls under catastrophic to critical non-resilient ecosystem. Soil moisture availability in the biome, forest cover, type of land use, agricultural practices, and climate shocks are mainly influencing the resilience of the anthropogenic biomes in India. The resilience assessment can be used by policymakers to plan anthropogenic interventions in harmony with nature.

INSTAILITY ANALAYSIS OF GROUNDNUT CROP IN GUJARAT: BEFORE AND AFTER Bt-COTTON

The time series data on area, production and productivity of groundnut crop for 31 years were collected for major groundnut growing districts and Gujarat state for the period 1985-86 to 2015-16 from Directorate of Agriculture, Gandhinagar, Gujarat State. The entire study period was divided into two sub-periods on the basis of introduction of Bt- cotton as pre Bt-cottoni.e.period I (1985-1986 to 2001-2002) and post Bt- cottoni.e.period II (2002-2003 to 2015-2016).Instability in the area, production and productivity of groundnut was analyzed using the Cuddy Della Valle Index (CDVI).The results revealed that the majority of the districts showed medium level of variability for area except Ahmedabad, Banaskantha, Kheda and Surat (High variability), Rajkot (Low variability), all the districts showed high level of variability for production and high level of variability for productivity except Kheda, Surat and Surendranagar which showed medium level of variability.

Sources of Technical Inefficiency for Smallholder Field Pea Producers in Ethiopia

This study intended to measure the technical efficiency and sources of inefficiency for field pea producers in Ethiopia. The study used primary data collected from 207 smallholder farmers. A multi-stage sampling technique was followed to select sample households. A stochastic frontier was the model used, and a single-step estimation approach was followed. The result indicated that the mean technical efficiency is 49.23 %, and the average productivity of the crop is 0.96 tons/hectare. This indicates that the technical efficiency could be increased by 51.77 % through proper utilization of production variables, i.e. agrochemicals, oxen power, plot size, and seed rate. The inefficiency model result also confirmed that the technical inefficiency of field pea production was negatively affected by access to off-farm income, access to credit services, extension contacts, adoption of new technologies, access to training, and participation in contract farming of the crop. Therefore, this study recommends agricultural policies favoring vocational training to farmers on the new agricultural technology packages, availing access to off-farm incomes, facilitating credit services to farmers, and establishing legal frameworks that encourage contract farming.

Quantum simulation of coopetition

We propose the rule engine of expert system consists of quantum algorithm in two folds: quantum mechanics and quantum computation. The simulation of quantum computation-based coopetition leads to explain the external economics scale (EES) and disruptive innovations of coopetition by the use of Brynjolfsson–Hitt computing productivity function as a real context of the proposed expert system. We show the quantum coopetition computation of Brynjolfsson–Hitt system on a global polyadic coopetition of globotics upheaval. The Schrödinger and Heisenberg pictures are discussed: the Schrödinger picture results the energy of coopetition environment, while the Heisenberg picture provides production function transformation due to quantum effect of production variables. The Composite Production Function is constructed through quantum algorithm, and the energy of member coopetition depends on quadratic of elasticity and production variables. It is shown that the composite production function of a coopetition depth is ≈O((n+δ)∑iβi), where it depends on the number of production function variables (n), the number of perturbation variables (δ), and the output elasticities of production function variables (∑iβi>1). Our findings suggest that the model has potential implementation for the expert system of coopetition on globotics upheaval era.

Analyzing Impact of Money Supply and Foreign Reserves on Economic Growth in Iraq for 2004-2021

Monetary policy is one of the most important economic policies in various countries. Its importance is evident through its basic variables and the effectiveness of its impact on economic activity. The focus in this study has been on the money supply and foreign reserves as independent variables and their impact on economic growth rates in Iraq for the period 2004-2021. Based on the ARDL standard model, through which the path of the relationship between these variables and economic growth and its reflection on growth rates was determined. And effective monetary policies are those policies that are characterized by transferring their effects to the economy with the least risks. The analytical approach was followed to clarify the impact of these variables on economic growth, represented by the variable of gross domestic product. And then extract the most important main results and important recommendations.

An evaluation of statistical models of microcystin detection in lakes applied forward under varying climate conditions

Algal blooms can threaten human health if cyanotoxins such as microcystin are produced by cyanobacteria. Regularly monitoring microcystin concentrations in recreational waters to inform management action is a tool for protecting public health; however, monitoring cyanotoxins is resource- and time-intensive. Statistical models that identify waterbodies likely to produce microcystin can help guide monitoring efforts, but variability in bloom severity and cyanotoxin production among lakes and years makes prediction challenging. We evaluated the skill of a statistical classification model developed from water quality surveys in one season with low temporal replication but broad spatial coverage to predict if microcystin is likely to be detected in a lake in subsequent years. We used summertime monitoring data from 128 lakes in Iowa (USA) sampled between 2017 and 2021 to build and evaluate a predictive model of microcystin detection as a function of lake physical and chemical attributes, watershed characteristics, zooplankton abundance, and weather. The model built from 2017 data identified pH, total nutrient concentrations, and ecogeographic variables as the best predictors of microcystin detection in this population of lakes. We then applied the 2017 classification model to data collected in subsequent years and found that model skill declined but remained effective at predicting microcystin detection (area under the curve, AUC ≥ 0.7). We assessed if classification skill could be improved by assimilating the previous years’ monitoring data into the model, but model skill was only minimally enhanced. Overall, the classification model remained reliable under varying climatic conditions. Finally, we tested if early season observations could be combined with a trained model to provide early warning for late summer microcystin detection, but model skill was low in all years and below the AUC threshold for two years. The results of these modeling exercises support the application of correlative analyses built on single-season sampling data to monitoring decision-making, but similar investigations are needed in other regions to build further evidence for this approach in management application.

Production performance of major vegetables in Maharashtra

The present study was restricted to only six crops namely brinjal, tomato, potato, onion, cabbage, and cauliflower which had the highest production in the year 2020-2021 amongst the vegetable crops in Maharashtra. The study was based on secondary data pertaining to the period 2001-2002 to 2020-21.In onion, there was a significant increasing trend of growth in area and production, while in potato significant increasing trend of growth was observed in production and yield, likewise tomato showed a significant increasing trend of growth in area and brinjal showed a significant increasing trend of growth in yield. The area, production, and yield of cabbage and cauliflower were not significant at all.Highest variability in area and production was observed in onion and cabbage, respectively while highest inconsistency of yield was seen in potato. Lest variability in area, production and yield was observed in brinjal. The instability in area was high in cabbage and cauliflower, medium in tomato, potato and onion, and low in brinjal. While in production, it was high in cabbage and cauliflower, medium in potato, tomato, onion and brinjal. Also, in yield, it was medium in potato and onion and low in brinjal, tomato, cabbage and cauliflower.

Optimization of Kampung Chicken Seedling Production Using the Monte Carlo Method (Case Study on Hacci Farm)

This research aims to optimize free-range chicken hatchery production at Hacci Farm using the Monte Carlo method, which is known for its ability to overcome uncertainty and variability in production. The data in this research uses chicken seed production data in 2022 and 2023 which is then used as input in the Monte Carlo simulation. The simulation results reveal that the Monte Carlo method provides more accurate predictions regarding free-range chicken seed production. Through this simulation, Hacci Farm can predict the amount of free-range chicken hatchery production in the future and can prepare more effective strategic steps in hatchery management to increase operational efficiency, reduce risks, and maximize free-range chicken production results. The results of implementing the Monte Carlo method show quite high accuracy, namely 98.7% in 2022 and 97.95% in 2023, this can help in planning and managing resources optimally. It is hoped that the use of this method can make a significant contribution in increasing the quality and quantity of free-range chicken production at Hacci Farm, as well as providing a model that can be adopted by other farms for similar purposes.

Caracterización del sistema caprino de producción extensiva en el Altiplano de Tamaulipas, México

In Tamaulipas extensive goat production systems in the semi-arid region of the Altiplano contribute annually with the production of 41% of live animals and 87% of milk in the state. The objective of the study was to characterize the goat production units in the municipalities of Jaumave, Bustamante and Tula, in which a significant number of production units are located. Through the application of structured surveys conducted in December 2021 and January 2022, socioeconomic, nutritional, productive, reproductive, and sanitary information was obtained from 40 goat production units. Data was analyzed using principal component factor analysis and a cluster analysis with the Statistical Program for Social Sciences (SPSS version 24). Four factors (management, production, grazing and reproduction) explained most of the total variation of the data and four groups of production units were determined. The characterization of the extensive goat production system in the Altiplano of Tamaulipas is described in general terms with management and production variables. However, the analysis of the typologies of production units showed heterogeneity in the system, in this sense, the differences between the means of the groupings that stood out the most were zootechnical purpose, reproduction, production and annual income. Underfeeding, scarce water sources and poor reproductive management are associated with low productivity and high animal mortality.

Impacts of vegetation greening and climate change on trend and interannual variability in vegetation productivity in the Wuyi Mountain region

ABSTRACT The Gross Primary Productivity (GPP) is an important component in regional and global carbon budgets. Southeastern China has experienced vegetation greening and climate change. Yet, it remains unclear how these changes have impacted GPP in this region. As one of six special national parks in China with complex topography, high biodiversity, and little destructive human activities to the ecosystems, the Wuyi Mountain region is selected to study these impacts. In this study, we use a hydroecological model (BEPS-TerrainLab V2.0) to simulate the spatial and temporal variations of GPP in the Wuyi Mountain region over 2001–2018. We quantitatively separate the effects of vegetation greening and climate change on the trend and interannual variation in GPP through sensitivity experiments. The results show a significant increasing trend in Leaf Area Index (LAI) in the region over 2001–2018 (0.06 m2 m−2 yr−1, p < 0.01). For climate, a significant warming trend (0.03°C yr−1, p = 0.06) and an insignificant wetting trend are found, companied with large interannual variations. The sensitivity experiments suggest that the combined effect of vegetation greening and climate change makes the annual GPP increase significantly over 2001–2018 (14.41 g C m−2 yr−2, p < 0.01). Vegetation greening plays a dominant role in the GPP increasing trend with a positive contribution of 15.76 g C m−2 yr−2. Climate change only makes an insignificant negative contribution (−0.43 g C m−2 yr−2), mainly due to warming. However, the climate modulates the interannual variation of GPP dominantly, with temperature being the most influential climate factor. Our results underscore the critical impact of vegetation greening on the GPP trend and the impact of climate on the GPP interannual variation in this subtropical forest region over east coast of China.