Relationship Between Six Years of Corn Yields and Terrain Attributes

Crop yield, soil properties, and erosion are strongly related to terrain attributes. The objectives of our study were to examine the relationship between six years of corn (Zea mays L.) yield data and relative elevation, slope, and curvature, and to develop a linear regression model to describe the spatial patterns of corn yield for a 16 ha field in central Iowa, USA. Corn grain yield was measured in six crop years, and relative elevation was measured using a kinematic global positioning system. Slope and curvature were then determined using digital terrain analysis. Our data showed that in the four years with less than normal growing season precipitation, corn yield was negatively correlated with relative elevation, slope, and curvature. In the two years with greater than normal precipitation, yield was positively correlated with relative elevation and slope. A multiple linear regression model based on relative elevation, slope, and curvature was developed that predicted 78% of the spatial variability of the average yield of the transect plots for the four dry years. This model also adequately identified the spatial patterns within the entire field for yield monitor data from 1997, which was one of the dry years. The relationship between terrain attributes and corn yield spatial patterns may provide opportunities for implementing site-specific management.     

基于多年产量数据的精准农业管理分区提取与尺度效应评价

 本研究利用带有差分全球定位系统（DGPS）接收机和产量监测传感器的联合收割机获取的4年产量数据进行精准农业管理分区的提取研究。对经过一系列处理后的4年的产量数据进行栅格平均运算，得到分辨率为4 m的综合产量图。分别采用尺度为12、20、28、36、44、52、60 m 的正方形窗口对分类后的综合产量图进行众数过滤，并从方差减少率、差异显著性、空间破碎化、空间一致性4个角度进行了尺度效应评价。结果表明，分类后众数过滤法有效地去除了由随机变异造成的孤立像元或碎片，保留了实际的产量变异，增加了管理分区的有效面积，提高了管理分区的连续性。分区结果可以直接作为精准农业目标产量分区图，用于作物种植前或产中适时肥料推荐管理决策。     

Investigating temporal and spatial patterns of cranberry yield in New Jersey fields

Cranberries are grown in sensitive wetland ecosystems and precision farming could be beneficial to reduce agro-chemical pollution and increase production without expanding area. Precision farming requires knowledge of the variation of yield within-fields but cranberry harvesting methods produce only one yield value per field unless an expensive pre-harvest berry count is done. Co-operatives and extension services have an important role in precision farming to: (1) determine important factors affecting yield patterns within a growing region and (2) identify fields that would benefit most from future intensive survey. This paper reports a study to investigate temporal and spatial patterns in useable and poor quality cranberry yield for the New Jersey (NJ), USA growing region. Principal components analysis indicated that mean growing season temperature is important for understanding temporal patterns in useable yield and maximum temperatures and precipitation for poor quality yield. Multiple linear regression showed that some cultivars were susceptible to disease and poor quality yield in years with high maximum growing season temperatures. Analysis of spatial patterns using area to area and area to point kriging, local cluster analysis and geographically weighted regression helped identify clusters of fields that were consistently yielding or alternated between high and low yielding. They also showed differences between owners and soil types particularly in hot or wet years showing the different response to soil types to weather and the potential for improvement in irrigation practices by some owners. The methods used should be useful for other growing regions and crops, particularly where there are no yield monitors.     

Spatially correlated forest stand structures: A simulation approach using copulas

Spatial structure of forest stands is one of the main drivers of forest growth and yield, and is an important indicator of wildlife habitat, aesthetics, and other non-timber forest uses. Because spatial structure is costly to measure, a number of approaches for simulating spatial structures have been proposed. In this paper, we propose a simple approach that is capable of generating multispecies stand structures. Based on the method of copulas (Genest and MacKay, 1986, Am. Stat. 40:280-283), we utilize a normal copula to simulate spatially correlated stand structures. Species composition, diameter, height, and crown ratio distributions of each species, and their correlation with underlying spatial patterns are all controlled by user inputs. Example data sets are used to demonstrate how to estimate required parameters and compare simulated spatial structures with observed spatial structures. Except at the smallest scales (<10 m in the longleaf pine dataset and <2 m in the mixed Acadian Forest dataset), the simulated stand structures adequately captured the observed spatial patterns. Based on these comparisons, we conclude that the system is capable of simulating a range of forest stand spatial structures.     

Evaluation of spatial and temporal variability of pasture based on topography and the quality of the rainy season

Yield and botanical composition of a given dry-land pasture are heavily influenced by rainfall distribution, and vary according to topography. Through analyzing these parameters, it is possible to identify patterns of spatial distribution, related to topographic characteristics, which can be used to improve spatial management of pasture. The main objective of this project was to assess the role of rainfall, temperature and topography in the spatial and temporal variability of pasture and establish models for predicting the spatial distribution and yield of certain plant species, based on its topographic characteristics. This study was carried out over three years, 2004 to 2006, in a dry-land pasture located in Southern Portugal. The data obtained were analyzed as a function of distance to flow lines and the results demonstrate that certain topographical characteristics, associated with agronomic information, can be very useful in explaining the spatial and temporal variability of yield and the botanical composition of the pasture. The slope of the linear regression of the variables mentioned can be used to estimate the spatial variation of yield and the botanical composition as a function of distance to flow lines. The regression coefficient can be estimated from the annual rainfall, given the strong correlation between the two.     

Delineation and Scale Effect of Precision Agriculture Management Zones Using Yield Monitor Data Over Four Years

In this study, precision agriculture management zones were delineated using yield data over four years from the combine harvester equipped with yield monitor and DGPS receiver. Relative yields measured during each year were interpolated to 4 m2 grid size using ordinary kriging. The resultant interpolated yield maps were averaged across years to create a map of the mean relative yield, which was then used for cluster analysis. The mean yield map of post-classification was processed by applying majority filtering with window sizes that were equivalent to the grid sizes of 12, 20, 28, 36, 44, 52 and 60 m. The scale effect of management zones was evaluated using relative variance reduction, test of significant differences of the means of yield zones, spatial fragmentation, and spatial agreement. The results showed that the post-classification majority filtering （PCMF） eliminated lots of isolated cells or patches caused by random variation while preserving yield means, high variance reduction, general yield patterns, and high spatial agreement. The zoned result can be used as yield goal map for preplant or in-season fertilizer recommendation in precision agriculture.     

Variograms of Ancillary Data to Aid Sampling for Soil Surveys

To provide reliable estimates for mapping soil properties for precision agriculture requires intensive sampling and costly laboratory analyses. If the spatial structure of ancillary data, such as yield, digital information from aerial photographs, and soil electrical conductivity (EC) measurements, relates to that of soil properties they could be used to guide the sampling intensity for soil surveys. Variograms of permanent soil properties at two study sites on different parent materials were compared with each other and with those for ancillary data. The ranges of spatial dependence identified by the variograms of both sets of properties are of similar orders of magnitude for each study site. Maps of the ancillary data appear to show similar patterns of variation and these seem to relate to those of the permanent properties of the soil. Correlation analysis has confirmed these relations. Maps of kriged estimates from sub-sampled data and the original variograms showed that the main patterns of variation were preserved when a sampling interval of less than half the average variogram range of ancillary data was used. Digital data from aerial photographs for different years and EC appear to show a more consistent relation with the soil properties than does yield. Aerial photographs, in particular those of bare soil, seem to be the most useful ancillary data and they are often cheaper to obtain than yield and EC data.     

基于农业气象模型的农作物单产估算——以湖北省中稻为例

先对气象数据进行空间插值处理,然后通过作物单产区划选择代表性的模拟县,根据模拟县历年作物单产的变化趋势得到作物波动产量,并对波动产量和气象因子进行相关性分析,提取与作物单产相关性最大的气象因子,建立一元线性或多元回归方程,估算作物产量,将估算结果与实际统计结果进行比较。结果表明,作物单产估算结果与统计数据的误差为-7．74％～9．80％。     

A flexible approach to managing variability in grain yield and nitrate leaching at within-field to farm scales

We up-scaled the APSIM simulation model of crop growth, water and nitrogen dynamics to interpret and respond to spatial and temporal variations in soil, season and crop performance and improve yield and decrease nitrate leaching. Grain yields, drainage below the maximum root depth and nitrate leaching are strongly governed by interaction of plant available soil water storage capacity (PAWC), seasonal rainfall and nitrogen supply in the water-limited Mediterranean-type environment of Western Australia (WA). APSIM simulates the interaction of these key system parameters and the robustness of its simulations has been rigorously tested with the results of several field experiments covering a range of soil types and seasonal conditions in WA. We used yield maps, soil and weather data for farms at two locations in WA to determine spatial and temporal patterns of grain yield, drainage below the maximum root depth and nitrate leaching under a range of weather, soil and nitrogen management scenarios. On one farm, we up-scaled APSIM simulations across the whole farm using local weather and fertiliser use data and the average PAWC values of soil type polygons. On a 70 ha field on another farm, we used a linear regression of apparent soil electrical conductivity (EC_a) measured by EM38 against PAWC to transform an EC_a map of the field into a high resolution (5 m grid) PAWC map. We then used regressions of simulated yields, drainage below the maximum root depth and nitrate leaching on PAWC to upscale the APSIM simulations for a range of weather and fertiliser management scenarios. This continuous mapping approach overcame the weakness of the soil polygons approach, which assumed uniformity in soil properties and processes within soil type polygons. It identified areas at greatest financial and environmental risks across the field, which required focused management and simulated their response to management interventions. Splitting nitrogen applications increased simulated wheat yields at all sites across the field and decreased nitrate leaching particularly where the water storage capacity of the soil was small. Low water storage capacity resulted in both low wheat yields and large leaching loss. Another management option to decrease leaching may be to grow perennial vegetation that uses more water and loses less by drainage.Paper from the 5th European Conference on Precision Agriculture (5ECPA), Uppsala, Sweden, 2005     

Airborne Videography to Identify Spatial Plant Growth Variability for Grain Sorghum

Much research has focused on the use of intensive grid soil sampling and yield monitors to identify within-field spatial variability in precision farming. This paper reports on the use of airborne videography to identify spatial plant growth patterns for grain sorghum. Color-infrared (CIR) digital video images were acquired from two grain sorghum fields in south Texas several times during the 1995 and 1996 growing seasons. The video images were registered, and classified into several zones of homogeneous spectral response using an unsupervised classification procedure. Ground truthing was performed upon a limited number of sites within each zone to determine plant density, plant height, leaf area index, biomass, and grain yield. Results from both years showed that the digital video imagery identified within-field plant growth variability and that classification maps effectively differentiated grain production levels and growth conditions within the two fields. A temporal comparison of the images and classification maps indicated that plant growth patterns differed somewhat between the two successive growing seasons, though areas exhibiting consistently high or low yield were identified within each field.     

长江下游平原小麦生长季气候变化特点及小麦发展方向

利用长江下游平原 10个样点 196 0～ 1997年的逐日气象资料 ,分析了近 40年来小麦生长季的气候变化特点 ;根据GISSTransientB模型的有关网格点值 ,展望了研究区域未来 5 0年气候演变的趋势 ;采用上述各样点近40年的小麦单产资料和同期气候资料 ,评价了当前小麦气候产量的变化规律 ;将经修正的CERES Wheat模型在各样点未来不同年份的气候变化情景下运行 ,并考虑大气CO2 增长对小麦产量的增益作用 ,模拟了今后 5 0年研究区域小麦气候产量的走势。结果表明 :近 40年来 ,长江下游平原小麦生长季已出现了明显的增温现象 ,并伴随日照时数和降雨日数的显著降低 ,小麦气候产量因暖冬、阴湿寡照和后期高温等呈下走趋势 ) ;今后 5 0年 ,这种下走趋势将因大气CO2 浓度增长而一定程度地得到缓解或补偿。因此 ,在研究区域目前种植结构调整中 ,合理压缩小麦面积是顺应气候变化的举措之一 ,但不能因此“封杀”小麦在研究区域的应有地位。培育耐阴的弱冬性或春性的优质小麦品种和发展多元化种植 ,将是研究区域今后利用冬季气候资源的发展方向。     

The 46{degrees} Halo and Its Arcs

Ice crystals in the form of right hexagonal prisms have faces that form 90 degrees prisms. Light rays were traced through these prism faces by computer calculation, and the light patterns that would be produced in the sky for a particular distribution of crystal orientations were simulated. Crystals with random orientations produce a 46 degrees halo. Hexagonal plate crystals with nearly horizontal end faces produce circumzenithal and circumhorizontal arcs. Hexagonal column crystals with horizontal axes produce supralateral and infralateral arcs. Plate crystals spinning about a horizontal axis that is a face diagonal of the crystal produce a series of arcs touching the 46 degrees halo. Each of these effects was simulated for several elevations of the sun.     

基于改进的SWAT模型的山地流域吸附态总磷负荷模拟

SWAT模型模拟的产沙量为流域总产沙量,而无法获得产沙量的空间分布以及与产沙量关联的吸附态负荷的空间分布。为此,针对山地流域,提出"径流连通性因子",基于该因子,形成改进的产沙量以及吸附态总磷负荷的时空分布模型,并对模型进行验证。应用所构建的模型,采用小流域推广法,将非完整流域的行政区域三峡库区中所包含的小流域模型及其模型参数分别扩大模拟范围,总体覆盖整个库区,实现对三峡库区产沙量和吸附态总磷负荷时空分布的模拟,确定出产沙和产生吸附态总磷负荷的关键源区。所构建的模型可用于其他山地流域。     

Making GIS a versatile analytical tool for research in precision farming

A field-level geographic information system (FIS) is being developed. The system provides special analytical functions useful for research in precision farming. Spatial data from various sources with different accuracies and resolutions are integrated. Remotely sensed data are used to assist field-data analysis. Region blocking and partitioning are obtained using logic and arithmetic queries and overlays. Spatial patterns are analyzed in spatial and frequency domains in both Cartesian and polar coordinate systems. A discrete model was developed to simulate the time lag inherent to a yield sensor on a combine harvester. Z-transformation was used to numerically remove the lagging effect.     

Exploring the Spatial Relationships Between Some Soil Properties and Wheat Yields in Two Soil Types

A field study was conducted to quantify spatial soil variability and to analyze correlations among soil properties at different spatial scales. Soil samples from 0 to 30 cm depth were collected from two adjacent fields in the southwestern Beauce Plain (France) which consisted of Haplic Calcisols and Rendzic Leptosols. Factorial kriging analysis (FKA) was used to describe the co-regionalization of nine soil properties. A linear model of co-regionalization including a nugget effect, and two spherical models were fitted to the experimental data direct and cross-variograms of the topsoil layer properties which were previously estimated. Co-kriged regionalized factors, related to short and long-range variation, were then mapped to characterize soil variation across the two fields. The potential value of ancillary sampled variables, such as yield data, to provide information on soil properties was tested. The relation between yield and measured soil properties appeared to be weak in general. However, the structures of the variation in yield appeared to be relatively stable for two years and showed similar patterns as the co-kriged soil factors. This suggests that information on the scale of variation of soil properties can be derived from yield maps, which can also be used as a guide to suitable sampling interval for soil properties and as a basis for managing fields in a precise way.     

Spatial variation in yield and quality in a small apple orchard

We describe the yield and quality of apples from a 0.8 ha apple orchard located in northern Greece over two growing seasons and consider the potential for site-specific management. The orchard has two apple cultivars: Red Chief (main cultivar) and Fuji (pollinator). Yield was measured by weighing all fruit harvested from groups of five adjacent trees and the position of the central tree was recorded by GPS. Apple quality at harvest was evaluated from samples of the two cultivars in both years for which fruit mass, flesh firmness, soluble solids content, juice pH and acidity of the juice were determined. The variation in tree flowering was also measured in the spring of the second season using a stereological sampling procedure. The results showed considerable variability in the number of tree flowers, yield and quality across the orchard for both cultivars. The number of flowers was strongly correlated with the final yield. These data could potentially be used to plan precise thinning and for early prediction of yield; the latter is important for marketing the fruit. Several quality characteristics, including fruit juice soluble solids content and acid content were negatively correlated with yield. The general patterns of spatial variation in several variables suggested that changes in topography and aspect had important effects on apple yield and quality.     

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.     

Integrating remote sensing information with crop model to monitor wheat growth and yield based on simulation zone partitioning

Research into crop growth models at the spatial scale is of great significance for evaluating crop growth, predicting grain yield and studying global climate change. Coupling spatial remote sensing (RS) data can effectively promote the simulation of growth models at spatial scales. However, the integration of RS data and crop models to produce a coupled model based on pixel by pixel requires a large amount of calculations. Simulation zone partitioning is used to separate and cluster the large area into a few relatively uniform zones. Then, the growth model can run on the basis of these units. This method both reflects spatial heterogeneity and avoids repeated simulations of regions with similar attributes, improving the simulation efficiency. In this study, simulation partitioning was performed using soil nutrient indices (organic matter content, total nitrogen content and available potassium content) and corresponding spatial characteristics of wheat growth, as indicated by RS data. A coupled model, integrating RS information and the WheatGrow model, using vegetation indices as the coupling parameters (based on the Particle Swarm Optimization algorithm and PROSAIL model), was developed. The aim was to realize accurate prediction of wheat growth parameters and grain yield at the spatial scale. Good zone partitions were obtained by partitioning with the spatial combination of soil nutrient indices and the wheat canopy vegetation index, calculated during the main growth (jointing, heading and filling) stages. The variation coefficients of each index within individual simulation sub-zones were much smaller than those of the indices across the whole area. An analysis of variance showed that the indices were significantly different between the simulation sub-zones, which indicated that appropriate simulated sub-zones had been defined. The minimum root mean square error of the leaf area index, leaf nitrogen accumulation and yield between the predicted values and the values simulated by the coupled model were 0.92, 1.12 g m^?2, and 409.70 kg ha^?1, respectively, which were obtained when the soil-adjusted vegetation index was used as a partitioning zone and assimilating parameter. These results demonstrated that the coupled model of the crop model and RS data, based on the simulation sub-zones had a good prediction accuracy. The results provide important technical support for increasing model efficiency, when crop models need to be applied at the spatial scale.     

Soil organic carbon dynamics and crop yield for different crop rotations in a degraded ferruginous tropical soil in a semi-arid region: a simulation approach

In recent years, simulation models have been used as a complementary tool for research and for quantifying soil carbon sequestration under widely varying conditions. This has improved the understanding and prediction of soil organic carbon (SOC) dynamics and crop yield responses to soil and climate conditions and crop management scenarios. The goal of the present study was to estimate the changes in SOC for different cropping systems in West Africa using a simulation model. A crop rotation experiment conducted in Farak?-Ba, Burkina Faso was used to evaluate the performance of the cropping system model (CSM) of the Decision Support System for Agrotechnology Transfer (DSSAT) for simulating yield of different crops. Eight crop rotations that included cotton, sorghum, peanut, maize and fallow, and three different management scenarios, one without N (control), one with chemical fertilizer (N) and one with manure applications, were studied. The CSM was able to simulate the yield trends of various crops, with inconsistencies for a few years. The simulated SOC increased slightly across the years for the sorghum-fallow rotation with manure application. However, SOC decreased for all other rotations except for the continuous fallow (native grassland), in which the SOC remained stable. The model simulated SOC for the continuous fallow system with a high degree of accuracy normalized root mean square error (RMSE)=0·001, while for the other crop rotations the simulated SOC values were generally within the standard deviation (s.d.) range of the observed data. The crop rotations that included a supplemental N-fertilizer or manure application showed an increase in the average simulated aboveground biomass for all crops. The incorporation of this biomass into the soil after harvest reduced the loss of SOC. In the present study, the observed SOC data were used for characterization of production systems with different SOC dynamics. Following careful evaluation of the CSM with observed soil organic matter (SOM) data similar to the study presented here, there are many opportunities for the application of the CSM for carbon sequestration and resource management in Sub-Saharan Africa.