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

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

Tea is a valuable economic plant grown extensively in several Asian countries. The accurate mapping of tea plantations is critical for the growth and development of the tea industry. In eastern India, tea plantations have a significant role in its economy. Sonitpur, Jorhat, Sibsagar, Dibrugarh, and Tinsukia are major tea-producing districts in Assam. Due to the rapid increase in tea plantations and the burgeoning population, a detailed mapping and regular monitoring of tea plantations are imperative for understanding land use alteration.The present study aims to analyse the dynamics of tea plantations from 1990 to 2022 at a decadal scale, using satellite data, such as Landsat-5 and Sentinel-2.A supervised classifier called Random Forest (RF) was deployed in the Google Earth Engine (GEE) platform to classify tea plantations.The results showed significant growth in tea plantations in the district of Dibrugarh (112%), whereas the remaining districts had a growth rate of 45-89%. During 32 years (1990-2022), about 1280.47 km2 (78.71%) of areas of tea plantations expanded across five districts of Assam. Precision and recall were used to measure the accuracy of tea plantations classification, which exhibited considerably high F1 scores (0.80 to 0.96).This study helps to demonstrate the application of remote sensing techniques to evaluate the dynamics of tea plantations which can help policymakers to manage the tea estates and underlying changes in land cover. 

/pdf/monitoring-tea-plantations-during-1990-2022-using-multi-2eezzh3g.pdf

Monitoring tea plantations during 1990–2022 using multi-temporal satellite data in Assam (India)

Tea trees (Camellia sinensis), a quintessential homestead agroforestry crop cultivated in over 60 countries, hold significant economic and social importance as a vital specialty cash crop. Accurate nationwide crop data is imperative for effective agricultural management and resource regulation. However, many regions grapple with a lack of agroforestry cash crop data, impeding sustainable development and poverty eradication, especially in economically underdeveloped countries. The large-scale mapping of tea plantations faces substantial limitations and challenges due to their sparse distribution compared to field crops, unfamiliar characteristics, and spectral confusion among various land cover types (e.g., forests, orchards, and farmlands). To address these challenges, we developed the Manual management And Phenolics substance-based Tea mapping (MAP-Tea) framework by harnessing Sentinel-1/2 time series images for automated tea plantation mapping. Tea trees, exhibiting higher phenolic content, evergreen characteristics, and multiple shoot sprouting, result in extensive canopy coverage, stable soil exposure, and radar backscatter signal interference from frequent picking activities. We developed three phenology-based indicators focusing on phenolic content, vegetation coverage, and canopy texture leveraging the temporal features of vegetation, pigments, soil, and radar backscattering. Characteristics of biochemical substance content and manual management measures were applied to tea mapping for the first time. The MAP-Tea framework successfully generated China's first updated 10 m resolution tea plantation map in 2022. It achieved an overall accuracy of 94.87% based on 16,712 reference samples, with a kappa coefficient of 0.83 and an F1 score of 85.63%. The tea trees are typically cultivated in mountainous and hilly areas with a relatively low planting density (averaging about 10%). Alpine tea trees exhibited a notably dense concentration and dominance, mainly found in regions with elevations ranging from 700 m to 2000 m and slopes between 2° to 18°. The areas with low altitudes and slopes hold the largest tea plantation area and output. As the slope increased, there was a gradual decline in the dominance of tea areas. The results suggest a good potential for the knowledge-based approaches, combining biochemical substance content and human activities, for national-scale tea plantation mapping in complex environment conditions and challenging landscapes, providing important reference significance for mapping other agroforestry crops. This study contributes significantly to advancing the achievement of the Sustainable Development Goals (SDGs) considering the crucial role that agroforestry crops play in fostering economic growth and alleviating poverty. The first 10m national Tea tree data products in China with good accuracy (ChinaTea10m) are publicly accessed at https://doi.org/10.6084/m9.figshare.25047308. 

Where is tea grown in the world: A robust mapping framework for agroforestry crop with knowledge graph and sentinels images

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

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

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

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

. A timely and accurate understanding of the spatial distribution of tea plantations is beneficial for agricultural management and regional sustainable development. However, obtaining detailed distribution data on large-area tea plantations remains challenging owing to limitations in computational capabilities, training data, and workflow design. Utilizing the Google Earth Engine, which provides a catalog of multisource data in a cloud-based environ-ment, we developed a methodology to generate a highly accurate tea plantation map, with a 10-m resolution, for Anhui Province, China, by integrating a random forest model with a progressive model. Our major contribution lies in this hybrid approach, which comprises two major com-ponents: (1) an optimal classification band combination derived from Sentinel-2 products and the digital elevation model filtered by the J-M distance model and (2) a progressive random forest method introduced for tea plantation classification. The experimental results show that our proposed workflow achieved an average classification accuracy of 89.27% for the entire Anhui Province. In addition, this approach is semiautomatic and can effectively reduce the labor required during the generation of training data compared with traditional classification approaches. These findings demonstrate the potential of integrating machine learning and progressive models to produce high-precision remote sensing classification maps. © The Authors. Published SPIE Creative Commons License.

https://www.spiedigitallibrary.org/journals/journal-of-applied-remote-sensing/volume-16/issue-2/024509/Mapping-large-area-tea-plantations-using-progressive-random-forest-and/10.1117/1.JRS.16.024509.pdf

Mapping large area tea plantations using progressive random forest and Google Earth Engine

Exploring the value of cultivated land resources around big cities is an important prerequisite for when realizing the “trinity” of cultivated land protection in terms of quantity, quality, and ecology. At present, the value of cultivated land resources faces the problem of having a low comparative efficiency of economic output, inadequate visualisation of asset value, and serious undervaluation. The manifestation of social, ecological, and cultural values in cultivated land plays an important role in accurately calculating the value of cultivated land. Therefore, this study attempted to clarify the composition and account for the logic of the comprehensive value of cultivated land around big cities. Taking the Hefei metropolitan area as an example, we used mathematical modeling and geostatistical analysis to measure the integrated value of cultivated land around big cities from 2010 to 2020 and analyse the spatial and temporal evolution characteristics and influencing factors. The results revealed a 2:7:1 ratio of economic, social, ecological, and cultural values for cultivated land in the metropolitan area. The comprehensive value of cultivated land was higher in 2020 than in 2010, was approximately 7.7 times the current compensation standard, and was significantly affected by the natural geography, economic development, cultivated land conditions, and protection policies. Furthermore, the comprehensive value showed spatial differentiation characteristics of ‘high in the east and south and low in the west and north’, and a spatial agglomeration effect was evident in some areas. A significant increase was observed in the social value of cultivated land, followed by an increase in the ecological and cultural values, whereas a slow downward trend was observed for the economic value. The economic value of cultivated land around big cities showed a downward trend, whereas the social, ecological, and cultural values showed upward trends. In the future, differentiated policy tools should be adopted based on the spatial heterogeneity of the comprehensive value of cultivated land in the Hefei metropolitan area to enhance their comprehensive value. In addition, scientific and reasonable compensation standards should be established, and high-quality agricultural development with the high-level protection of cultivated land should be promoted in metropolitan areas.

Accounting for the Logic and Spatiotemporal Evolution of the Comprehensive Value of Cultivated Land around Big Cities: Empirical Evidence Based on 35 Counties in the Hefei Metropolitan Area

Aquatic ecological health is a global challenge due to its significant impacts, and a real-time method to accurately assess the ecological health is therefore particularly useful. Based on the hydrological, water quality, meteorological, and topographic data collected from field observations and organizations, this study developed a process-based hydrodynamic and water quality model using an environmental fluid dynamics code (EFDC) model of Lake Poyang to simulate the spatiotemporal variations in water levels and the total nitrogen (TN), total phosphorus (TP), and chlorophyll a (Chl a) contents. Compared with the measured values, the models were confirmed to be reliable and acceptable. Then, to achieve a real-time assessment, the simulated results were calculated and transformed into five ordinal rating categories by using a lake ecosystem health assessment framework. Our research revealed the following results: (1) the highest and lowest 10-day water level statuses indicated that the hydrological conditions of this lake were at “good” or “excellent” levels. (2) The health levels of TN were mostly at the “fair” level or below, and most of the real-time assessment health curves of lake zones were close to the single peak type. (3) The TP contents were often at “good” levels, and the health assessment curves of the lake zones exhibited downward trends during the periods of rapid rise or retreat. (4) A fluctuating characteristic was found in the health assessment of Chl a with at least two declining peaks in all lake zones. This study demonstrated the high value of physics-based models for assessing the ecological health of aquatic ecosystems in a robust and accurate manner.

https://www.frontiersin.org/articles/10.3389/fenvs.2022.881335/pdf

A Real-Time Assessment of Aquatic Ecological Health Using a Process-Based Model: An Example From Lake Poyang, China

Rapid Large-Scale Monitoring of Pine Wilt Disease Using Sentinel-1/2 Images in GEE

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