Mapping Potential Crop Management Zones within Fields: Use of Yield-map Series and Patterns of Soil Physical Properties Identified by Electromagnetic Induction Sensing

Investment in precision farming technologies can be expensive and is not expected to be cost-effective for every farm. Previous research and farm experience has shown that the amount of soil variability across a farm and within a field is of key importance for determining potential benefits from the adoption of precision farming. The research reported here evaluates the analysis of yield map sequences and electromagnetic induction (EMI) soil sensing as potentially cost-effective methods for identifying and mapping soil-determined management zones within fields. Both methods are shown to provide useful information for the provisional delineation of soil type boundaries and crop management zones, though soil examination in the field is still necessary to confirm specific soil characteristics.     

Frequency Analysis of Yield for Delineating Yield Response Zones

The yield in any given field or management zone is a product of interaction between many soil properties and production inputs. Therefore, multi-year yield maps may give better insight into determining potential management zones. This research was conducted to develop a methodology to delineate yield response zones by using two-state frequency analysis conducted on yield maps for 3 years on two commercial corn fields near Wiggins, Colorado. A zone was identified by the number of years that yield was equal and greater than the average yield in a given year. Classes producing statistically similar yield were combined resulting in three potential yield zones. Results indicated that the variability of yield over time and space could successfully be assessed at the same time without the drawbacks of averaging data from different years. Frequency analysis of multi-year yield data could be an effective way to establish yield response zones. Seventeen percent of the field #1 consistently produced lower yield than the mean while 43 of the field produced yield over the mean. Corresponding values for field #2 were 6% and 42%.The remainder of the fields produced fluctuating yields between years. These spatially and temporally sound yield response maps could be used to identify the yield-limiting factors in zones where yield is either low or fluctuating. Yield response maps could also be helpful to delineate potential management zones with the help of resource zones such as electrical conductivity and soil maps, along with the directed soil sampling results.     

Management Zones Classified With Respect to Drought and Waterlogging

Within-field variations in potential grain yield may be due to variations in plant available soil water. Different water holding capacities affect yield differently in different years depending on weather. By estimating plant-water availability in different weathers, scenarios could be created of how yield potential and thereby fertilizer demand may vary within fields. To test this, measured cereal grain yields from a dry, a wet and an intermediate year were compared with different soil moisture related variables in a Swedish arable field consisting of clayey and sandy areas. Soil water budget calculations based on weather data and maximum plant available water (PAW), estimated from soil type and rooting data, were used to assess drought. A reasonable correlation between estimated and measured soil moisture was achieved. In the dry year, drought days explained differences in yield between the clayey and the sandy soil, but yield was better explained directly by maximum PAW, elevation, clay content and soil electrical conductivity (SEC). Yield correlated significantly with SEC and elevation within the sandy soil in the dry year and within the clayey soil in the wet year, probably due to water and nitrogen limitation respectively. Dense SEC, elevation and yield data were therefore used to divide the field into management zones representing different risk levels for drought and waterlogging. These could be used as a decision support tool for site-specific N fertilization, since both drought and waterlogging affect N fertilization demand.     

基于模糊聚类分析的田间精确管理分区研究

【目的】以海涂围垦区盐碱土为研究对象，将从SPOT遥感影像提取出的NDVI数据和盐碱地土壤生产力的主要限制因子盐分数据及部分养分数据作为变量进行精确农业管理分区研究。【方法】模糊c均值聚类方法被用来进行分类分区，并引入了模糊聚类指数和归一化分类熵两种分区效果评价指标，对分区结果进行比较和评价。【结果】本研究区，最佳的分区数目为3个。对处于每一子区内土样的化学特性和实测棉花产量数据进行方差分析，发现其均值在所定义的每个管理分区内都存在着统计意义上的显著差异性，其中子区3具有最高的肥力水平和作物产量，而子区1最低。【结论】利用所选取的变量，模糊c均值聚类算法可以较好地进行管理分区划分。分区结果不但可以指导采样，而且可用于实施变量投入和精确施肥推荐，为样区土壤管理提供科学的决策依据。     

棉花遥感应用研究进展

综述了高光谱遥感技术在棉花研究中的进展。探讨了高光谱遥感数据与棉花水分、氮素状况和产量的相关关系以及利用其对水肥、产量估算的潜力，提出了测定的最适宜波段，并提出了今后的研究方向和发展前景。     

基于FCM的绿洲农田养分管理分区研究

 【目的】以天山北麓绿洲农耕区——新疆农八师148团农田为研究对象,以193个耕层土壤（0～30 cm）有机质、碱解氮、速效磷和速效钾含量的分析数据为变量进行农田土壤养分精确管理分区研究。【方法】模糊c-均值聚类法被用来进行分区,以棉田产量为外部变量,采用FPI、c-φ多次组合法及基于外部变量的多元回归法来确定适宜的模糊控制参数。【结果】研究区最佳分区数为4,模糊指数为1.6。各管理分区土壤养分的变异系数都较分区前全研究区有所减小,而分区间土壤养分差异显著。研究区的平均混乱度指数为0.19,不同模糊类别交叠程度小,地理空间上土壤的隶属关系相对明确。【结论】通过选取适宜的外部变量,模糊c-均值聚类法可以较好地进行管理分区划分,分区结果可以作为变量施肥的单独作业单元进行耕作管理。     

Airborne Hyperspectral Imagery and Yield Monitor Data for Mapping Cotton Yield Variability

Increased availability of hyperspectral imagery necessitates the evaluation of its potential for precision agriculture applications. This study examined airborne hyperspectral imagery for mapping cotton (Gossypium hirsutum L.) yield variability as compared with yield monitor data. Hyperspectral images were acquired using an airborne imaging system from two cotton fields during the 2001 growing season, and yield data were collected from the fields using a cotton yield monitor. The raw hyperspectral images contained 128 bands between 457 and 922 nm. The raw images were geometrically corrected, georeferenced and resampled to 1 m resolution, and then converted to reflectance. Aggregation functions were then applied to each of the 128 bands to reduce the cell resolution to 4 m (close to the cotton picker's cutting width) and 8 m. The yield data were also aggregated to the two grids. Correlation analysis showed that cotton yield was significantly related to the image data for all the bands except for a few bands in the transitional range from the red to the near-infrared region. Stepwise regression performed on the yield and hyperspectral data identified significant bands and band combinations for estimating yield variability for the two fields. Narrow band normalized difference vegetation indices derived from the significant bands provided better yield estimation than most of the individual bands. The stepwise regression models based on the significant narrow bands explained 61% and 69% of the variability in yield for the two fields, respectively. To demonstrate if narrow bands may be better for yield estimation than broad bands, the hyperspectral bands were aggregated into Landsat-7 ETM+ sensor's bandwidths. The stepwise regression models based on the four broad bands explained only 42% and 58% of the yield variability for the two fields, respectively. These results indicate that hyperspectral imagery may be a useful data source for mapping crop yield variability.     

Predicting Cotton Lint Yield Maps from Aerial Photographs

It is generally accepted that aerial images of growing crops provide spatial and temporal information about crop growth conditions and may even be indicative of crop yield. The focus of this study was to develop a straightforward technique for creating predictive cotton yield maps from aerial images. A total of ten fields in southern Georgia, USA, were studied during three growing seasons. Conventional (true color) aerial photographs of the fields were acquired during the growing season in two to four week intervals. The aerial photos were then digitized and analyzed using an unsupervised classification function of image analysis software. During harvest, conventional yield maps were created for each of the fields using a cotton picker mounted yield monitor. Classified images and yield maps were compared quantitatively and qualitatively. A pixel by pixel comparison of the classified images and yield maps showed that spatial agreement between the two gradually increased in the weeks after planting, maintained spatial agreement of between 40% and 60% during weeks eight to fourteen, and then gradually declined again. The highest spatial agreement between a classified image and a yield map was 78%. The highest average agreement was 52% and occurred 9.9 weeks after planting. The visual similarity between the classified images and the yield maps were striking. In all cases, the dates with the best visual agreement occurred between eight and ten weeks after planting, and generally, during July for southern Georgia. This method offers great potential for offering cotton farmers early-season maps that predict the spatial distribution of yield. Although these maps can not provide magnitudes, they clearly show the resulting yield patterns. With inherent knowledge of past performance, farmers can use this information to allocate resources, address crop growth problems, and, perhaps, improve the profitability of their farm operation. These maps are well suited to be offered to farmers as a service by a crop consultant or a cooperative.     

基于无人机多光谱影像的春玉米田管理分区研究

为了提高大尺度农田管理的针对性,探寻低成本高效的分区方法,以黑龙江省典型黑土区30.8 hm²春玉米田为对象进行分区研究。基于吐丝期无人机多光谱影像,使用多尺度分割与模糊聚类相结合的方法进行分区,同时基于播种前的土壤养分（土壤有机质、速效氮磷钾）、土壤体积含水率、电导率、pH进行模糊聚类分区并作为对照,对分区间春玉米产量和土壤养分进行方差分析,并对分区内变异系数进行比较以评价分区效果。结果表明,基于无人机影像得到4个较优管理分区为M1、M2、M3、M4,各分区产量分别为7 597.53、8 236.35、8 686.98、9 119.93 kg·hm^-2,各分区间产量差异显著,其中M1、M2、M3间土壤养分差异显著（P<0.05）,分区内作物产量和土壤养分的变异系数降低;基于土壤数据确定4个分区,即S1、S2、S3、S4,春玉米产量分别为7 754.81、8 173.44、8 860.05、9 153.23 kg·hm^-2,分区间土壤养分、土壤水分差异显著（P<0.05）,分区内部土壤均一性提高。综合来看,2种方法的划分结果在空间分布上具有一定的相似性,同级分区的空间重合度分别为40.00%、46.51%、57.45%、59.38%,整体重合度为51.32%。当缺乏土壤数据时,无人机多光谱影像可为农田管理分区提供参考依据。     

新疆北疆棉花多元复合遥感估产模型研究

[目的]揭示棉花产量与棉花叶面积指数( LAI)、归一化植被指数(NDVI)相关关系,辨识新疆北疆棉花遥感估产最佳时相,建立棉花产量与LAI及NDVI间的多元复合遥感估产模型,为大面积棉花生产管理和估产提供理论参考.[方法]以TM影像为数据源,结合实地调查的棉花LAI、NDVI和产量等数据,对影像数据进行校正,最后用统计学方法分析棉花指数与产量数据间关系和建模.[结果]棉花LAI在各生育期呈先升后降的趋势,花铃期最高,均值为3.69;棉花NDVI在各生育期基本处于稳定的较高水平,棉花生长旺盛,长势较好;棉花蕾期和花铃期LAI与产量呈极显著正相关,花铃期相关系数最高,达到0.75;新疆北疆棉花最佳估产时相为花铃期,最优估产模型为Y=17.76 LAI - 123.05 NDVI +232.15.[结论]利用LAI和NDVI建立多元复合估产模型能有效提高棉花的估产精度.     

基于县域耕地地力评价的耕地管理分区研究——以山东省青州市为例

基于GIS技术选取合适评价因子,运用层次分析、模糊评价等方法对青州市耕地进行定量化评价。利用模糊C均值聚类法对评价结果进行分区研究,揭示该市耕地质量整体较好;依据FPI和NCE指数确定最佳分区数目为3个,由北向南耕地地力依次降低,受地形因素影响较大;利用模糊聚类方法进一步对分区1进行划分,分为6个管理子区,得到了内部更为均衡的分区管理单元,为统一采取管理措施提供了有益的参考。     

Site-specific Approaches to Cotton Insect Control. Sampling and Remote Sensing Analysis Techniques

When insect population density varies within the same cotton field, estimation of abundance is difficult. Multiple population densities of the same species occur because cotton fields (due to edaphic and environmental effects) are apportioned into various habitats that are colonized at different rates. These various habitats differ temporally in their spatial distributions, exhibiting varying patterns of interspersion, shape and size. Therefore, when sampling multiple population densities without considering the influence of habitat structure, the estimated population mean represents a summary of diverse population distributions having different means and variances. This single estimate of mean abundance can lead to pest management decisions that are incorrect because it may over- or under-estimate pest density in different areas of the field. Delineation of habitat classes is essential in order to make local control decisions. Within large commercial cotton fields, it is too laborious for observers on the ground to map habitat boundaries, but remote sensing can efficiently create geo-referenced, stratified maps of cotton field habitats. By employing these maps, a simple random sampling design and larger sample unit sizes, it is possible to estimate pest abundance in each habitat without large numbers of samples. Estimates of pest abundance by habitat, when supplemented with ecological precepts and consultant/producer experience, provide the basis for spatial approaches to pest control. Using small sample sizes, the integrated sampling methodology maps the spatial abundance of a cotton insect pest across several large cotton fields.     

棉田墒情远程监测信息管理系统的构建

[目的]提高新疆膜下滴灌棉花生产效率和水分利用效率,指导农民进行合理灌溉.[方法]采用Borland Delphi 7.0高级编程语言+Microsoft SQL Server 2000数据库、模块化程序设计思想、面向对象的集成开发模式.[结果]开发了棉田墒情远程监测信息管理系统.该系统运用农田墒情远程监测设备系统实时获取水分监测数据、通过Internet远程获取中国气象科学数据共享平台中新疆全境自动气象站的实时气象数据、棉田苗情数据,判断棉花是否缺水并向农户手机发布棉田墒情状态和灌溉决策.系统在新疆生产建设兵团农六师105团2连、农八师149团11连、农八师150团12连自动化灌溉地进行了安装应用.[结论]采用该系统可制定出科学合理的灌溉决策,指导农民进行合理灌溉,使有限的水资源发挥最大的灌溉效益.     

全球气候变暖对中国种植制度的可能影响XIV.东北大豆高产稳产区及农业气象灾害分析

【目的】大豆是主要的粮油兼用作物,东北三省是我国大豆主产区,研究气候变化背景下东北三省大豆气候生产潜力高产稳产性区域分布及其变化特征,明确不同区域限制大豆高产稳产性的主要农业气象灾害,可为东北三省大豆合理布局、防灾避灾以及高产稳产提供科学参考。【方法】以1981年为时间节点,将研究时段划分为1961—1980年（时段Ⅰ）和1981—2019年（时段Ⅱ）两个时段,利用调参验证后的DSSAT-CROPGRO-Soybean模型模拟研究区域大豆潜在种植区各站点气候生产潜力,明确气候变化背景下大豆气候生产潜力高产稳产性区域分布及其变化特征;结合大豆冷害和干旱指标,明确不同高产稳产性区域冷害和干旱的时空分布特征;结合统计方法,明确限制大豆高产性和稳产性的主要农业气象灾害因子。【结果】（1）与1961—1980年（时段Ⅰ）相比,1981—2019年（时段Ⅱ）大豆潜在种植区增加2.81×10⁶ hm²,占研究区域总土地面积的3.57%;（2）与时段Ⅰ相比,时段Ⅱ稳产区面积占比减少,其中高产稳产区面积占潜在种植区内总土地面积的比例由17.67%减少到17.11%,高产不稳产区占比由13.54%增加到15.13%,低产稳产区占比由34.98%增加到38.17%,低产不稳产区占比由18.58%减小到18.49%;（3）研究时段内,大豆生长季冷害发生频次总体呈现先上升后下降趋势,高产稳产和高产不稳产区冷害特别是严重冷害发生频次高于低产稳产区以及低产不稳产区;大豆生长季轻旱和中旱发生频次增加,重旱发生频次减小;（4）大豆产量变化与冷害发生频次呈负相关关系,产量变异性的变化与冷害和干旱发生频次均呈正相关关系。【结论】气候变暖背景下,东北三省大豆潜在种植区呈北移西扩趋势,可种植面积增加;大豆高产不稳产和低产稳产面积增加,高产稳产区和低产不稳产区面积减少;不同高产稳产性区域内主要农业气象灾害不同,低产区较高产区总体低温冷害发生频次高,不稳产区较稳产区干旱发生频次高。但在高产稳产性变化区域,冷害发生频次下降,干旱发生频次上升。总体而言冷害是大豆高产性的主要限制因子,冷害和干旱是大豆产量不稳定的主要限制因子。     

小流域综合治理的遥感系列制图研究

本文提出遥感系列制图的原则,详细阐述了判读制图的方法步骤,建立了彩红外航空像片土地利用现状主要类型的判读标志。还对面积量算和成图精度进行了全面分析。研究证明遥感系列制图是对小流域综合治理动态监测的有效手段。     

地块窗口支持下作物的遥感估产

为克服样方代表性狭窄、误差大、需重复采样等缺点,提出了基于地块窗口的估产模式,以行政村作为研究区域,利用QuickBird影像提取的地块区域作为窗口,TM影像为主要数据源,结合实地调查,分别以玉米和花生为研究对象,在地块、区域2个层面初步探讨了在地块窗口支持下遥感估产的关键技术.结果表明:采用以地块为窗口的分类方法在大...     

不同管理方式荔枝遥感监测与空间变化研究

【目的】荔枝作为常绿果树,存在缺乏管理而沦为绿化树现象。准确了解和掌握荔枝种植区域的空间分布及空间变迁趋势,对荔枝产业空间布局优化、标准化管理等具有重要意义。【方法】以广州市增城区荔枝种植区域为例,利用遥感技术,对2015、2019年两期遥感影像进行目视解译,判断有人管理与无人管理荔枝种植区域的空间变化特征,借助重心迁移模型和破碎度指数定量分析荔枝时空变化程度。【结果】遥感解译总体精度较高,达到86.54%。增城区荔枝种植面积减少67.69 hm²,其中,有人管理荔枝种植面积减少127.28 hm²,无人管理荔枝种植面积增加59.59 hm²。荔枝种植区域重心逐渐远离城镇,整体向东迁移45.14 m、向北迁移107.01 m,其中有人管理荔枝种植区域向西北迁移,无人管理荔枝种植区域向西南迁移。荔枝种植区域的破碎度指数从2015年的0.0407增加至2019年的0.0413,空间分布呈现逐渐分散格局;但有人管理荔枝种植区域的破碎度指数从0.0244降低至0.0238,空间上呈现聚集状态。【结论】利用遥感技术可以精准快速识别...     

基于总体抽样下子总体追加抽样设计的棉花种植面积遥感估算方法研究

针对遥感与抽样相结合的农作物种植面积估算方法中,子总体的作物种植面积估算需要新的抽样体系设计和样本野外调查等繁重工作,缺少基于总体下对子总体的作物种植面积估算的方法,以沙湾县、玛纳斯县和呼图壁县为总研究区,提出了基于遥感和PPS抽样相结合的总体抽样下子总体追加抽样设计的子总体研究区呼图壁县棉花种植面积估算方法,并以新疆建设兵团统计局公布数据为真值对其估算结果进行对比分析。结果显示,基于总体抽样下子总体追加抽样设计后,子总体研究区估计量的变异系数为0.023 3,远低于0.05,而追加抽样设计前变异系数为0.122 3,说明样本在该方法下的代表性得到极大提高。以新疆建设兵团统计局公布数据为真值进行对比发现,子总体研究区棉花种植面积提取精度达到94.2%,能够有效提取子总体中的棉花种植面积,同时避免了重新建立子总体研究区抽样体系所需要的人力、物力、财力等资源的消耗。     

Soil Test,Aerial Image and Yield Data as Inputs for Site-specific Fertility and Hybrid Management Under Maize

Several potential sources of information exist to support precision management of crop inputs. This study evaluated soil test data, bare-soil remote sensing imagery and yield monitor information for their potential contributions to precision management of maize (Zea mays L.). Data were collected from five farmer-managed fields in Central New York in 1999, 2000, and 2001. Geostatistical techniques were used to analyze the spatial structure of soil fertility (pH, P, K, NO₃ and organic matter content) and yield variables (yield, hybrid response and N fertilization response), while remote sensing imagery was processed using principal component analysis. Geographic information system (GIS) spatial data processing and correlation analyses were used to evaluate relationships in the data. Organic matter content, pH, P, and K were highly consistent over time and showed high to moderate levels of spatial autocorrelation, suggesting that grid soil sampling at 2.5–5.5ha scale may be used as a basis for defining fertility management zones. Soil nitrate levels were strongly influenced by seasonal weather conditions and showed low potential for site-specific N management. Aerial image data were correlated to soil organic matter content and in some cases to yield, mainly through the effect of drainage patterns. Aerial image data were not well correlated with soil fertility indicators, and therefore were not useful for defining fertility management zones. Yield response to hybrid selection and nitrogen fertilization rates were highly variable among years, and showed little justification for site-specific management. In conclusion, we recommend grid-based management of lime, P, and K, but no justification existed within our limited study area for site-specific N or hybrid management.     

冬小麦遥感估产多种模型研究

综合冬小麦地面光谱资料及相应的农学参数资料,ＮＯＡＡ／ＡＶＨＲＲ 资料,历年各县冬小麦单产、播种面积、总产资料,历年新疆各站气象资料,监测点历年冬小麦发育期、密度、产量分析等资料,证明地面光谱植被指数与冬小麦密度、生物量、叶面积指数关系密切,从而建立了密度与生物量的光谱监测模型,进而建立了北疆试验区各层冬小麦种植面积估算和产量预报卫星遥感模型,辅以冬小麦产量农业气象预报模型、农学模型及模拟模型,自１９９４ 年投入应用以来的结果表明,这套模型预报精度高、效果很好