Soil Salinity Sampling Strategy Using Spatial Modeling Techniques, Remote Sensing, and Field Data

The aim of this study was to develop a methodology for collecting soil salinity samples. The objectives of this paper are to: (1) estimate the number of soil salinity samples needed to capture the variability in the soil salinity data with high accuracy; and (2) compare two types of satellite images with different resolutions: Ikonos with 4?m resolution and Landsat 5 with 30?m resolution. To achieve these objectives, two satellite images were acquired (one for Ikonos and one for Landsat 5) to evaluate the correlation between the measured soil salinity and remote sensing data. From the observed data, three subsets were randomly extracted with each subset containing: 75, 50, and 25% of the data for each field. These three subsets were then used in the modeling process. Ordinary least squares (OLS) (i.e., multiple regression) was used to explore the coarse-scale variability in soil salinity as a function of the Ikonos and Landsat 5 bands. A stepwise procedure was used to identify the best subset of satellite bands to include in the regression models that minimized the Akakie information criteria (AIC). Then, the spatial structure of the residuals from the OLS models were described using sample variogram models. The variogram model with the smallest AIC was selected to describe the spatial dependencies in the soil salinity data. If the residuals were spatially correlated, ordinary kriging was used to model the spatial distribution of soil salinity in the fields. A tenfold cross validation was used to estimate the prediction error for soil salinity. To evaluate the effectiveness of the models, various measures of the prediction error were computed. This study provides an accurate methodology that can be used by researchers in reducing the number of soil samples that need to be collected. This is especially valuable in projects that last several years. The results of this study suggest that the number of soil samples that need to be collected and therefore their cost can be significantly reduced and soil salinity estimation can be significantly improved by using kriging. The results also show that the Ikonos image performed better than the Landsat 5 image.     

Integrated Agro-Economic Approach to Deficit Irrigation on Lettuce Crops in Sicily (Italy)

The effect of four different irrigation levels on the marketable yield and economic return of summer-growth lettuce was evaluated during 2005 and 2006 in Eastern Sicily, Italy. The viability of deficit irrigation was evaluated by estimating optimum applied water levels. Actual evapotranspiration (ETa) was estimated by combining pan evaporation measures and the Penman–Monteith approach (ET0-PM). The highest marketable yield of lettuce was recorded for plots receiving 100% ET0-PM. For deficit irrigated plots, reductions in crop production were ascribed to a decrease in lettuce weight. Crop coefficients equal to 1 determined maximum crop production values. Crop water use efficiency was maximum at a 100% ET0-PM level of water applied, corresponding to yield of 0.3?t?ha?1?mm?1. Irrigation water use efficiency reached its maximum at a 40% ET0-PM level, with values of 0.54 and 0.44?t?ha?1?mm?1 during 2005 and 2006, respectively. Water applied and marketable yield of lettuce showed a significant quadratic relationship. Cost functions had a quadratic form during 2005 and a linear form during 2006. In the land-limiting condition the optimal economic levels fit the agronomic ones well. In the water-limiting condition, ranges of water deficit of 15–44% and 74–94% were as profitable as full irrigation, thus contributing to appreciable water savings.     

The Sustainability of Irrigation with Gypsiferous Mine Water and Implications for the Mining Industry in South Africa

The sustainability of irrigation with gypsiferous mine water and different irrigation management practices was evaluated using a milti-disciplinary approach, where crop response was investigated along with the impact on soil and groundwater resources. Field trials carried out at two mines (Landau and Kleinkopje Colliery, Mpumalanga Province, South Africa) indicated that a wide range of species can be cropped for commercial purposes under irrigation with this water. Chemical analyses of groundwater underlying irrigated areas indicated that contamination of groundwater did not occur after three years. The results of a glasshouse trial indicated that the inclusion of NH₄ ⁺ for N-fertilization in a NO₃ ⁻-NH₄ ⁺ ratio of 2:1 to 1:1 is advantageous to root and top growth of wheat. According to simulations run with the Soil Water Balance (SWB) model and the CLIMGEN weather data generator, soil chemical and physical properties will not be irreparably damaged after thirty years of irrigation. Perennial pastures, irrigated at high frequency, provided the highest net farm income and water utilization.     

徐州西郊污水浇灌农作物的试验研究

该实验就徐州西郊污水对土壤、农作物和蔬菜的污染程度进行了分析、评价,并就污水对农作物与蔬菜的影响进行了比较。结果表明,徐州西郊污水对土壤和农作物均有显著危害,对蔬菜的污染较之农作物的危害尤甚。     

论云南热职院国际教育合作的路径——基于中泰国际教育合作政策的研究

以云南热职院国际教育合作为例,分析了云南热职院“走出去”发展的现状和主客观制约因素,认为中泰两国政府在高等教育合作层面所出台的系列政策具有助推作用,合作的路径是通畅的,由此提出:提高主观理念上的认识,在客观现实中搭建平台是解决目前制约因素的主要路径.     

Google Maps API在林业局级林下作物栽培中的应用

林下经济以其能够充分利用林下土地资源及大面积林荫的优势而得到众多农林专家和林农的青睐,其在实现农林资源共享、优势互补和循环生态方面发挥着很大的作用。结合林下经济发展现状及广大林农的需求特点,综合运用当前先进的Google Maps API技术和A-JAX技术,以JSON作为客户端与服务器的数据传送格式,构建基于B/S的林业局级林下作物栽培咨询平台。该平台提供给用户直观化的信息查询与呈现效果,不仅有效提高农户种植的科学性,也可在一定程度上帮助管理层制定决策。     

New Weighing Method to Measure Shoot Water Interception

The need exists to develop a method that can quantitatively measure water interception from plant shoots. This paper describes a new method for measuring canopy water interception. Corn (Zea Mays L.) was grown in 13?L buckets containing Valentine fine sand (Mixed, mesic Typic Ustipsamment) under a climate-controlled growth chamber. Plants were taken out of the growth chamber for 2–3?h periods for measurements of shoot water interception in a hydraulic laboratory equipped with an Accupulse system suspended from the ceiling that was used to wet the corn shoots at growth stages V7–V13. A lightweight, movable framework was placed around a balance, and buckets containing corn plants were placed on the scale one container at a time. Water was applied until all shoot surfaces were wet and runoff from the leaves and stalk surfaces could be observed. The weighing method for shoot water interception was tested by using the balance to instantaneously measure shoot water interception during application of water and after plant surface runoff ceased. The balance, bucket, and soil surface were covered with plastic to protect them from water, so only the shoots were wet. Interception by the shoot of corn ranged from 31?to?47?mL?shoot?1. These values were much smaller than previous values reported in the literature. The average coefficient of variation was 9.2% for two studies, which was much lower than previously accepted methods. This study suggests that the weighing method for shoot water interception can be used to quantitatively and more accurately measure water intercepted by corn shoots. The weighing intercepted method values presented in this paper are low and suggest that previous interception methods overestimated the interception values.     

Identification of Crop Diseases Based on Improved Genetic Algorithm and Extreme Learning Machine

As an indispensable task in crop protection, the detection of crop diseasesdirectly impacts the income of farmers. To address the problems of low crop-diseaseidentification precision and detection abilities, a new method of detection is proposed based on improved genetic algorithm and extreme learning machine. Taking five different typical diseases with common crops as the objects, this method first preprocesses the images of crops and selects the optimal features for fusion. Then, it builds a model of crop disease identification for extreme learning machine, introduces thehill-climbing algorithm to improve the traditional genetic algorithm, optimizes the initial weights and thresholds of the machine, and acquires the approximately optimal solution. And finally, a data set of crop diseases is used for verification, demonstrating that, compared with several other common machine learning methods, this method can effectively improve the crop-disease identification precision and detection abilities and provide a basis for the identification of other crop diseases.     

Cropland Measurement Using Thematic Mapper Data and Radiometric Model

To halt erosion and desertification, it is necessary to quantify resources that are affected. Necessary information includes inventory of croplands and desert areas as they change over time. Several studies indicate the value of remote sensor data as input to inventories. In this study, the radiometric modeling of spectral characteristics of soil and vegetation provides the theoretical basis for the remote sensing approach. Use of Landsat Thematic Mapper images allows measurement of croplands in Saudi Arabia, demonstrating the capability of the approach. The inventory techniques and remote sensing approach presented are potentially useful in developing countries.     

基于多时相Landsat8 OLI影像的作物种植结构提取

针对基于多时相遥感影像、多种特征量提取多种作物种植结构在我国研究较少的现状,利用多时相Landsat8OLI影像数据,根据温宿县不同作物的农事历,通过分析主要地物的光谱特征和归一化植被指数的时间变化信息,构建不同作物种植结构提取的决策树模型,实现了对温宿县多种作物种植结构信息的提取。结果表明:1水稻的最佳识别依据是5月20日影像的近红外波段和7月23日影像的NDVI值;棉花和春玉米的最佳识别依据是5月20日~9月9日影像的NDVI变化值;冬小麦—夏玉米和林果的最佳识别依据是5月20日~7月23日影像的NDVI变化值;2与单时相监督分类相比,多时相决策树法对多种作物种植结构的提取效果更理想,总体精度提高了7.90%,Kappa系数提高了0.10;3Landsat8OLI影像数据分辨率高、成本低、获取方便,是农作物遥感的良好数据源。