Estimation of Apple Flowering Frost Loss for Fruit Yield Based on Gridded Meteorological and Remote Sensing Data in Luochuan, Shaanxi Province, China

Adaptation of agriculture to extreme weather events: evidence from apple farmers' organic fertilizer use in China

Overcoming the challenge of more frequent and extreme weather events holds importance in agricultural production. We take spring frost disasters as a representative extreme weather event to identify how perennial economic crop farmers adjust the quantity of organic fertilizer used in response to extreme weather events and their adjustment mechanism. In this study, we establish a conceptual framework for the adaptation mechanism of apple growers under extreme weather events. This article draws and verifies five hypotheses through on-site investigations of apple growers in Shaanxi Province, China. Empirical evidence shows that farmers increase the quantity of commercial organic fertilizer materials in the year and in the following year when spring frost occurs, indicating that their adaptative behavior can be subdivided into repair and prevention. Mechanism analysis shows that liquidity constraints impact farmers' adaptive behavior. Liquidity constraints limit the ability of farmers to increase the quantity of commercial organic fertilizer materials to adapt to a spring frost disaster. Furthermore, for farmers not constrained by liquidity constraints, household resource endowment conditions still affect their adaptive behavior. Significantly, the household labor force size mainly influences farmers to increase commercial organic fertilizer to adapt to a spring frost disaster. Our findings highlight the differences between the adaptive behavior mechanism of perennial crop farmers and food crop farmers. Moreover, we reconfirm the stimulating effect of organic fertilizer on crop production.

Climatic suitability projection for deciduous fruit tree cultivation in main producing regions of northern China under climate warming

China is the largest fruit producer and consumer market in the world. Understanding the growing conditions responses to climate change is the key to predict future site suitability of main cultivation areas for certain deciduous fruit trees. In this study, we used dynamic and growing degree day models driven by downscaled daily temperatures from 22 global climate models to project the effects of climate change on growing conditions for deciduous fruit trees under two representative concentration pathway (RCP) 4.5 and RCP8.5 scenarios over 2 future time periods (represented by central years 2050s and 2085s) in northern China. The results showed a general increase of available winter chill for all sites under RCP4.5 scenario, and the most dramatic increase in chill accumulation could reach up to 36.8% in northeast regions for RCP8.5. However, the forecasted chill will decrease by 6.4% in southeast stations under RCP8.5 by 2085s. Additionally, the increase rate of growing season heat showed spatially consistency, and the most pronounced increase was found in the RCP8.5 by 2085s. For the southwest station, median heat accumulation increased by 20.8% in the 2050s and 37.1% in the 2085s under RCP8.5. Similar increasing range could be found in the northeast station; the median growing season heat increased by 19.8% and 38.8% in the 2050s and 2085s under RCP8.5, respectively. Moreover, the date of last spring frost was expected to advance and the frequency of frost occurrences was projected to decline in the study area compared to the past. Overall, the present study improves understanding regarding site-specific characteristics of climatic suitability for deciduous fruit tree cultivation in main producing regions of northern China. The results could provide growers and decision-makers with theoretical evidence to take adaptive measure to ensure fruit production in future.

Remote Sensing Image Denoising Based on Deep and Shallow Feature Fusion and Attention Mechanism

Optical remote sensing images are widely used in the fields of feature recognition, scene semantic segmentation, and others. However, the quality of remote sensing images is degraded due to the influence of various noises, which seriously affects the practical use of remote sensing images. As remote sensing images have more complex texture features than ordinary images, this will lead to the previous denoising algorithm failing to achieve the desired result. Therefore, we propose a novel remote sensing image denoising network (RSIDNet) based on a deep learning approach, which mainly consists of a multi-scale feature extraction module (MFE), multiple local skip-connected enhanced attention blocks (ECA), a global feature fusion block (GFF), and a noisy image reconstruction block (NR). The combination of these modules greatly improves the model’s use of the extracted features and increases the model’s denoising capability. Extensive experiments on synthetic Gaussian noise datasets and real noise datasets have shown that RSIDNet achieves satisfactory results. RSIDNet can improve the loss of detail information in denoised images in traditional denoising methods, retaining more of the higher-frequency components, which can have performance improvements for subsequent image processing.

Estimation of Apple Tree Leaf Chlorophyll Content Based on Machine Learning Methods

Leaf chlorophyll content (LCC) is one of the most important factors affecting photosynthetic capacity and nitrogen status, both of which influence crop harvest. However, the development of rapid and nondestructive methods for leaf chlorophyll estimation is a topic of much interest. Hence, this study explored the use of the machine learning approach to enhance the estimation of leaf chlorophyll from spectral reflectance data. The objective of this study was to evaluate four different approaches for estimating the LCC of apple tree leaves at five growth stages (the 1st, 2nd, 3rd, 4th and 5th growth stages): (1) univariate linear regression (ULR); (2) multivariate linear regression (MLR); (3) support vector regression (SVR); and (4) random forest (RF) regression. Samples were collected from the leaves on the eastern, western, southern and northern sides of apple trees five times (1st, 2nd, 3rd, 4th and 5th growth stages) over three consecutive years (2016–2018), and experiments were conducted in 10–20-year-old apple tree orchards. Correlation analysis results showed that LCC and ST, LCC and vegetation indices (VIs), and LCC and three edge parameters (TEP) had high correlations with the first-order differential spectrum (FODS) (0.86), leaf chlorophyll index (LCI) (0.87), and (SDr − SDb)/ (SDr + SDb) (0.88) at the 3rd, 3rd, and 4th growth stages, respectively. The prediction models of different growth stages were relatively good. The MLR and SVR models in the LCC assessment of different growth stages only reached the highest R2 values of 0.79 and 0.82, and the lowest RMSEs were 2.27 and 2.02, respectively. However, the RF model evaluation was significantly better than above models. The R2 value was greater than 0.94 and RMSE was less than 1.37 at different growth stages. The prediction accuracy of the 1st growth stage (R2 = 0.96, RMSE = 0.95) was best with the RF model. This result could provide a theoretical basis for orchard management. In the future, more models based on machine learning techniques should be developed using the growth information and physiological parameters of orchards that provide technical support for intelligent orchard management.