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[前沿资讯 ] 新疆麦盖提县：立足高标准农田打造“节水丰产”新样板 进入全文
中国农网
今年以来，新疆麦盖提县积极落实“藏粮于技，藏粮于地”战略，立足高标准农田，小面积试验种植小麦浅埋滴灌宽幅匀播技术，充分发掘农田节水潜力，增强农民节水意识，助力农业节水转型升级。当前，小麦生长已进入后期管理阶段，田间去杂工作是保障种源纯度的一项关键措施。在麦盖提县希依提墩乡东风村滴灌小麦试验田里，村民巩建林正在和技术人员一起对小麦进行除杂。放眼望去，田地里的小麦茎秆粗壮、麦穗硕大、籽粒渐丰，微风过处麦浪滚滚，滴灌小麦的种植方式也让巩建林赞不绝口。
[前沿资讯 ] 甘肃成县：唤醒撂荒地 播种新希望 进入全文
中国农网
仲夏时节，在甘肃省成县小川镇撂荒地整治现场，镇村各级干部驻守田间地头，挖掘机忙着挖填泥土，旋耕机跟在后面来回翻耕，这么一来一回，被野草覆盖的撂荒地随即变得平整松软。随后，一群扛着锄头等农具的村民开始忙碌起来，平地、起垄、锄草、拉线……机械的轰鸣声在山间回荡，一幅幅忙碌的农耕画卷徐徐展开。近年来，成县深入贯彻落实习近平总书记关于“粮食安全工作”重要批示指示精神，把开展“撂荒地”整治作为粮食生产安全和耕地保护的重要抓手，严格按照省、市撂荒地专项整治工作部署要求，通过强化组织领导、宣传动员、摸底核实、部门协作等措施，扎实推进全县撂荒地整治工作。
[前沿资讯 ] 奋力书写黑土地高质量发展新答卷——沿着总书记的足迹之黑龙江篇 进入全文
农民日报
这里有中国粮仓，这里有大国重器，这里有莽莽林海……这里是中国“北大门”黑龙江。党的十八大以来，习近平总书记两次深入龙江大地考察调研，为黑龙江全面振兴全方位振兴擘画蓝图。牢记习近平总书记嘱托，黑龙江奋力奔跑，攻坚克难，抓机遇、迎挑战，塑造龙江新优势、展现龙江新形象。
[会议论文 ] Determination of Soil Texture Using Image Processing Technique 进入全文
IEEE
Agriculture is the backbone of world’s economy and it is one of the largest employment sectors. Nowadays, the population is growing fast and simultaneously, the total cultivable land is lessening drastically. Soil texture has a significant impact on the agriculture affecting crop selection and crop growth. This paper presents the development of soil texture detection and pH value determination of the soil using the image processing technique. In this paper, two methods have been applied to identify the soil texture using two colorspace methods in the MATLAB toolbox, which are the Hue Saturation Value (HSV) and Red Green Blue (RGB) color method. Furthermore, to determine the pH value of the soil, an image processing algorithm was applied to obtain the desired output. Moreover, these two proposed methods were applied in the Graphical User Interface (GUI) in MATLAB software. The proposed system is expected to contribute to the community by saving human effort, increases efficiency and generates more accurate results in shorter time.
[学术文献 ] Proximal sensor data fusion for tropical soil property prediction: Soil fertility properties 进入全文
ScienceDirect
Proximal sensors have proven capable of predicting multiple soil properties under different conditions. However, doubts remain about which sensor is preferable for delivering optimal prediction models and which preprocessing methods produce the most accurate results. Portable X-ray fluorescence (pXRF) spectrometry and visible near-infrared (Vis-NIR) diffuse reflectance spectroscopy have been widely used, while the NixProTM color sensor has been explored more recently. This study evaluated the use of pXRF, Vis-NIR, and NixProTM data to predict soil organic matter content (SOM), pH, base saturation (BS), the sum of bases (SB), cation exchange capacity (CEC) at pH = 7 and effective CEC (eCEC), via each sensor in isolation, and via combined sensors data. Moreover, variables interfering in the prediction models' accuracy (data preprocessing methods, soil horizon, soil class, parent material) were used as auxiliary variables. 604 soil samples were collected in Brazil, encompassing ten soil orders and 19 parent materials. Numerical and categorical prediction models (7,980) were created for six soil properties using a random forest algorithm, totaling 7980 models, delivering almost 24,000 results, including coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), residual prediction deviation (RPD) for validation of numerical predictions, and overall accuracy and kappa coefficient for categorical predictions. Although the combination of sensors provided most of the best predictions, pXRF in isolation achieved accuracies close to the three sensors combined. NixProTM offered superior contributions to SOM and CEC predictions, but pXRF and Vis-NIR were responsible for the best results of most studied variables. On average, by adding pXRF to Vis-NIR data, predictive accuracy improved 32%; while adding Vis-NIR to pXRF data increased accuracy by c. a. 6%. Soil-order-specific models improved predictions for Ultisols compared to general models (without soil order distinction), reaching R2 > 0.90. Soil parent material and horizon did not improve models significantly. Categorical predictions improved the accuracy for some properties, reaching an overall accuracy of 100% and kappa index of 1.0 for pH in A horizons of Ultisols via pXRF + Vis-NIR data. Proximal sensor data with no auxiliary variables provided almost all the best results. The fusion of proximal sensors can provide better predictions, but pXRF alone can deliver satisfactory results in most cases for the six soil properties.
[学术文献 ] Strategies for agricultural production management based on land, water and carbon footprints on the Qinghai-Tibet Plateau 进入全文
ScienceDirect
Agricultural production consumes land and water resources, and contributes to greenhouse gas emissions. Optimizing agricultural management to reduce environmental impacts is essential for regional ecological security. An evaluation framework was applied to assess the greenhouse gas emissions, water utilization, and land use of agricultural production in a typical agricultural region of the Huangshui River Basin on the Qinghai-Tibet Plateau, using footprint analysis. The results showed that agricultural production released 1.73 × 109 kg carbon equivalent (CO2-eq), and used 8.39 × 108 m3 of water and 2.96 × 105 ha of land. For the carbon footprint, agricultural material inputs (such as electricity, machinery, diesel, and nitrogen fertilizer) were the largest emission sources. For the water footprint, the blue water footprint was larger than the green water footprint. In addition, suitable management options were explored by establishing six scenarios according to the key factors influencing greenhouse gas emissions and water consumption. For technical strategy management options, using cleaner electricity in irrigation can reduce greenhouse gas emissions by 25.53%. Comprehensive strategies, including fertilizer application optimization and technical strategy management, proved to be more effective and reduced greenhouse gas emissions by 32.41%. The results of this study help to determine optimal agricultural management options for achieving both food security and environmental sustainability in agricultural areas on the Qinghai-Tibet Plateau.
