Climate change severely impacts agricultural production, which jeopardizes food security.  China is the second largest maize producer in the world and also the largest consumer of maize.  Analyzing the impact of climate change on maize yields can provide effective guidance to national and international economics and politics.  Panel models are unable to determine the group-wise heteroscedasticity, cross-sectional correlation and autocorrelation of datasets, therefore we adopted the feasible generalized least square (FGLS) model to evaluate the impact of climate change on maize yields in China from 1979–2016 and got the following results: (1) During the 1979–2016 period, increases in temperature negatively impacted the maize yield of China.  For every 1°C increase in temperature, the maize yield was reduced by 5.19 kg 667 m^–2 (1.7%).  Precipitation increased only marginally during this time, and therefore its impact on the maize yield was negligible.  For every 1 mm increase in precipitation, the maize yield increased by an insignificant amount of 0.043 kg 667 m^–2 (0.014%).  (2) The impacts of climate change on maize yield differ spatially, with more significant impacts experienced in southern China.  In this region, a 1°C increase in temperature resulted in a 7.49 kg 667 m^–2 decrease in the maize yield, while the impact of temperature on the maize yield in northern China was insignificant.  For every 1 mm increase in precipitation, the maize yield increased by 0.013 kg 667 m^–2 in southern China and 0.066 kg 667 m^–2 in northern China.  (3) The resilience of the maize crop to climate change is strong.  The marginal effect of temperature in both southern and northern China during the 1990–2016 period was

In order to improve the image segmentation performance of cotton leaves in natural environment, an automatic segmentation model of diseased leaf with active gradient and local information is proposed.  Firstly, a segmented monotone decreasing edge composite function is proposed to accelerate the evolution of the level set curve in the gradient smooth region.  Secondly, canny edge detection operator gradient is introduced into the model as the global information.  In the process of the evolution of the level set function, the guidance information of the energy function is used to guide the curve evolution according to the local information of the image, and the smooth contour curve is obtained.  And the main direction of the evolution of the level set curve is controlled according to the global gradient information, which effectively overcomes the local minima in the process of the evolution of the level set function.  Finally, the Heaviside function is introduced into the energy function to smooth the contours of the motion and to increase the penalty function ?(x) to calibrate the deviation of the level set function so that the level set is smooth and closed.  The results showed that the model of cotton leaf edge profile curve could be obtained in the model of cotton leaf covered by bare soil, straw mulching and plastic film mulching, and the ideal edge of the ROI could be realized when the light was not uniform.  In the complex background, the model can segment the leaves of the cotton with uneven illumination, shadow and weed background, and it is better to realize the ideal extraction of the edge of the blade.  Compared with the Geodesic Active Contour (GAC) algorithm, Chan-Vese (C-V) algorithm and Local Binary Fitting (LBF) algorithm, it is found that the model has the advantages of segmentation accuracy and running time when processing seven kinds of cotton disease leaves images, including uneven lighting, leaf disease spot blur, adhesive diseased leaf, shadow, complex background, unclear diseased leaf edges, and staggered condition.  This model can not only conduct image segmentation of cotton leaves under natural conditions, but also provide technical support for the accurate identification and diagnosis of cotton diseases.