[学术文献] Fallowing temporal patterns assessment in rainfed agricultural areas based on NDVI time series autocorrelation values 进入全文
International Journal of Applied Earth Observation and Geoinformation
Fallowing is a common practice in Mediterranean areas where water scarcity becomes a limiting factor, affecting soil productivity, crop yield and biodiversity. In mainland Spain, fallow lands expand across three million hectares every year, constituting around 30% of rainfed arable lands and 6% of the national surface. There is a need of monitoring fallow lands to better map land use intensity and therefore achieve a sustainable expansion and intensification of agriculture. However, most of current land use classification systems do not include lands under fallowing practices as a specific class. In this research, a new and highly operative methodology based on NDVI time series autocorrelation values to assess fallowing temporal patterns across rainfed agricultural areas is proposed. This approach was tested in mainland Spain, using the autocorrelation function of MODIS NDVI time series from 2001 to 2012 at 250 m spatial resolution. The field observational database from the Spanish Ministry of Agriculture, Fisheries and Food was used for validation purposes. The dataset used includes 338 pixels with annual information about the cultivated and fallowed surface within the entire study period. It was demonstrated that specific autocorrelation values at lags corresponding to one, two, and three years contained relevant information to identify lands under fallowing practices and assess their temporal pattern. Integrating autocorrelation variables in a random forest model made it possible to improve the assessment. The classification results were in agreement with the field dataset with an overall accuracy higher than 80%. Results revealed that approximately half of rainfed agricultural areas were regularly cultivated and distributed mainly in the northwestern Spain. The other half mainly located across northeast, center and south of Spain, showed crop-fallow rotation patterns. This methodology is a promising technique to map land management intensity using the entire time series in a highly operative manner. It is expected that in the near future the availability of remote sensing time series with better spatial resolution will make it possible to improve the assessment of agricultural intensification.
[学术文献] Optimization of pollutant reduction system for controlling agricultural non-point-source pollution based on grey relational analysis combined with analytic hierarchy process 进入全文
Journal of Environmental Management
Many technologies have been developed to control agricultural non-point-source pollution (ANPSP). However, most reduce pollution from only a single source instead of considering an entire region with multiple pollution sources as a control unit. A pollutant reduction system for controlling ANPSP at a regional scale could be built by integrating technologies and the reuse of treated wastewater (TWR) and nutrients (NR) to protect the environment and achieve agricultural sustainability. The present study proposes four systematic schemes involving TWR for irrigation and NR in a region with three sources of ANPSP (crop farming, livestock and aquaculture). Subsequently, a multi-objective evaluation model is established based on the analytical hierarchy process (AHP) combined with grey relational analysis (GRA) to identify the optimal scheme considering six indices, namely, pollutant reductions (total nitrogen, TN; total phosphorous, TP; ammonium-nitrogen, NH4+-N; and chemical oxygen demand, COD) and costs (construction and operational costs). The Taihu Lake Basin suffers from some of the worst ANPSP in China, and a case study was conducted in a town with three ANPSP sources. Four systems were developed on the basis of suggested technologies and the scenarios of TWR and NR (Scenario I: no reuse, Scenario II: reuse of all livestock wastewater and manure, Scenario III: reuse of some aquaculture wastewater, and Scenario IV: reuse of all livestock wastewater and manure and some aquaculture wastewater). Pollutant reductions were calculated based on removal efficiency and pollutant loads, which were estimated from the local pollutant export coefficients and agricultural information (crop farming, livestock, and aquaculture). The costs were determined on the basis of the total pollutant reductions and unit cost. The results showed that the optimal system was the Scenario IV because it had the highest grey correlation degree among the four proposed systems. The optimal system met the irrigation water demand in Xinjian. In the optimal system, the removal efficiencies of the pollutants TN, TP, NH4+-N, and COD were 84.3%, 94.2%, 89.6% and 94.0%, respectively. In addition, the implementation of NR in the optimal system reduced the use of chemical fertilizers by nearly 81.7 kg N ha−1 and 39.9 kg P ha−1. The proposed methods provide a reference for the construction of a pollutant reduction system for controlling ANPSP in a multi-source region.
什么是颠覆性农业科技？颠覆性农业科技是如何诞生的？颠覆性农业科技未来发展趋势如何？7月23日，在由果酒产业技术创新战略联盟和中国科学技术出版社等联合举办的颠覆性农业科技论坛上，来自农业科技界的专家学者对此进行了深入研讨。 本次论坛以 “普及农业知识 启发科学思维” 为主题，以2019年3月正式出版的《颠覆性农业科技》一书中的62项颠覆性农业科技成果为主线，采取走进颠覆性农业科技原创地的方式，来到中国农业科学院农业环境与可持续发展研究所，邀请专家对植物数字工厂、马铃薯主食化等几项颠覆性农业科技进行了介绍和科普。
中国科技网·科技日报讯（刘宇峰 记者 谢开飞）7月20日，福建省农业农村厅、福建省农科院、福建农林大学三家共同发起成立福建省农业科技创新联盟。该联盟是由农业行政管理部门、农业科研机构、农业院校、农业技术推广机构、农业企业以及农民合作社、家庭农场等新型农业经营主体共同组成的非法人联盟组织，系全省首个农业科技创新联盟，由福建省农业科学院院长翁启勇担任联盟理事长。目前，首批联盟成员单位共126家，计划成立全省性产业技术创新专业联盟15个。
[学术文献] Water quality monitoring method based on feedback self correcting dense connected convolution network 进入全文
This paper presents a method of water quality monitoring using a Feedback self correcting system combined with a Densely Connected Convolution Network. We find an effective method to correct the model output and innovate the method of biological water quality monitoring. Fish movement trajectory is a comprehensive expression of various water quality classification characteristics used in all the literature, and it is an important basis for classification of biological water quality. In this paper, we use the image segmentation method of Mask-RCNN to obtain the centroid coordinates of the fish and draw the trajectory image of the fish in a certain period of time. The trajectory image data sets are divided into normal and abnormal water quality. Densely connected convolution network(DenseNet) is used to classify the quality of water. The experiment is based on normal and abnormal water quality image data, and the model correction system with deviation feedback can be designed by the output of softmax. The learning ability of the classification modelin practical application is greatly improved and enhance the stability of the detection system. The experimental results show that the water quality identification rate of the model reaches 99.38%, which is far higher than that of all previous water quality classification models.