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In order to clarify the temporal and spatial changes in the internal photothermal environment in an open-field agrivoltaic system (OAVS), this paper took the OAVS in eastern China as the research object and divided the internal area into the southern area, middle area and northern area, according to the spatial structure. Further, a photothermal environment test was conducted in the above three areas in the summer and winter. The results showed that the summer average daylight rate (Rm-avg) in the middle area was 66.6%, while the Rm-avg in the other two areas was about 20%, with no significant difference. In the winter, the light environment in the southern area was slightly better, and the Rm-avg in the above three areas was 26.4%, 24.7% and 19.7%, respectively. On the whole, the relationship between the thermal environmental factors and the solar radiation intensity was consistent. Further, a 3D model of an OAVS was established using Autodesk Ecotect Analysis 2011, and the internal light environment was simulated. Compared with the measured values, the relative error was less than 10%, which verified the reliability of the OAVS model. Then, the model was used to reveal the temporal and spatial changes in the light environment of the OAVS. The simulation results showed that the daylighting rate in the summer from the ground to the height of the fig canopy inside the system was 20.7% to 61.5%. In the winter, the daylighting rate from the ground to the height of the fig canopy inside the system was 17.7% to 36.4%. The effectiveness of the OAVS in reducing the level of solar radiation intensity depended on the time of day and the angle of the sun. At the spatial scale, due to the strong consumption of light by photovoltaic panels, there was a strong horizontal and vertical light environment gradient inside the system. In conclusion, the photothermal environment research of an OAVS based on Autodesk Ecotect Analysis 2011 can not only provide a basis for agricultural production and structural design such as span, height and the laying density of PV panels, but also expand its application to regions with different latitudes and longitudes and specific climates. [ABSTRACT FROM AUTHOR]

Drought has a significant impact on agricultural production, but the evolution of drought in the North China Plain (NCP) and its impact on winter wheat yield remain unclear. In this paper, we used the Standardized Precipitation Evapotranspiration Index (SPEI) and combined with the Standardized Yield Residual Series (SYRS) and the Vegetation Condition Index (VCI) to study the spatiotemporal distribution of drought at different time scales and its impact on winter wheat yield. We found that: (1) The southern NCP was dominated by mild drought at the 1-month scale. In the northern NCP, mild drought was observed at the short-time scale and moderate drought at the medium- and long-time scales; (2) the frequency of mild drought was high and the frequency of moderate drought was a step lower. Moreover, drought risks were increasing in the north-central, eastern, and northeastern NCP, while the southern, west-central, southeastern, and northwestern NCP were becoming wetter (at the 6- and 9-month scales); (3) the maximum correlation coefficients were concentrated in the seedling to the greening stages. (4) Drought affected winter wheat with a lag of approximately 3 months, and the VCI was the most sensitive to the short-term SPEI. [ABSTRACT FROM AUTHOR]

Agricultural advancements have significantly impacted people's lives and their surroundings in recent years. The insufficient knowledge of the whole agricultural production system and conventional ways of irrigation have limited agricultural yields in the past. The remote sensing innovations recently implemented in agriculture have dramatically revolutionized production efficiency by offering unparalleled opportunities for convenient, versatile, and quick collection of land images to collect critical details on the crop's conditions. These innovations have enabled automated data collection, simulation, and interpretation based on crop analytics facilitated by deep learning techniques. This paper aims to reveal the transformative patterns of old Chinese agrarian development and fruit production by focusing on the major crop production (from 1980 to 2050) taking into account various forms of data from fruit production (e.g., apples, bananas, citrus fruits, pears, and grapes). In this study, we used production data for different fruits grown in China to predict the future production of these fruits. The study employs deep neural networks to project future fruit production based on the statistics issued by China's National Bureau of Statistics on the total fruit growth output for this period. The proposed method exhibits encouraging results with an accuracy of 95.56% calculating by accuracy formula based on fruit production variation. Authors further provide recommendations on the AGR-DL (agricultural deep learning) method being helpful for developing countries. The results suggest that the agricultural development in China is acceptable but demands more improvement and government needs to prioritize expanding the fruit production by establishing new strategies for cultivators to boost their performance. [ABSTRACT FROM AUTHOR]