Machine learning algorithms have been applied in the agriculture field to forecast crop productivity. Previous studies mainly focused on the whole crop growth period while different time windows on yield prediction were still unknown. The entire growth period was separated into each month to assess their corresponding predictive ability by taking maize production (silage and grain) in Czechia. We present a thorough assessment of county-level maize yield prediction in Czechia using a machine learning algorithm (extreme learning machine (ELM)) and an extensive set of weather data and maize yields from 2002 to 2018. Results show that sunshine in June and water deficit in July were vastly influential factors for silage maize yield. The two primary climate parameters for grain maize yield are minimum temperature in September and water deficit in May. The average absolute relative deviation (AARD), root mean square error (RMSE), and coefficient (R2) of the proposed models are 6.565–32.148%, 1.006–1.071%, 0.641–0.716, respectively. Based on the results, silage yield will decrease by 1.367 t/ha (3.826% loss), and grain yield will increase by 0.337 t/ha (5.394% increase) when the max temperature in May increases by 2 °C. In conclusion, ELM models show a great potential application for predicting maize yield.
China has experienced dramatic economic growth and social development, especially in the period between 1978 and 2008. The biodiversity and the socioeconomic sustainability in China were under threat, and the loss of wetlands was a significant aspect of ecological deterioration in the country at that time. However, the driving factors for the loss of wetlands are not well understood, probably due to a lack of accurate country-scale data. This study analyzes the changes in China’s wetland area between 1978 and 2008 (1978, 1990, 2000, and 2008) and the interchange between different wetland types from 1990 to 2000. We select 29 socioeconomic parameters (per capita GDP, primary industry added value, secondary industry ratio, total population, arable land, pesticide use, aquatic products, railway mileage, domestic wastewater, urban sewage treatment capacity, etc.) and three meteorological parameters (annual temperature, annual precipitation, and annual sunshine) to analyze the driving forces of changes in wetlands. The factor analysis based on these parameters shows that two factors can explain 65.8% of the total variation from the data, while eight parameters can explain 59.7%. Furthermore, multiple linear regression analysis reveals that five factors are of great significance in explaining wetland change in China, which are annual temperature (p < 0.001), inland waterway mileage (p < 0.001), urban land acquisition (p = 0.01), secondary industry ratio (p = 0.014), and railway mileage (p = 0.02). In conclusion, climate change (especially temperature) and inland waterway mileage are the primary factors for changes in the wetlands in China, and other socioeconomic indicators, especially from industrial and construction factors, also play an important role in changes in wetlands during China’s rapid economic development. In order to enhance wetland conservation efforts in China, we recommend prioritizing efforts to mitigate climate change on wetlands, promoting sustainable development policies, restoring and creating wetlands in urban areas, and utilizing advanced technologies to obtain accurate data.
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