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.     

Relationships Between Yield Monitor Data and Airborne Multidate Multispectral Digital Imagery for Grain Sorghum

Remote sensing imagery taken during a growing season not only provides spatial and temporal information about crop growth conditions, but also is indicative of crop yield. The objective of this study was to evaluate the relationships between yield monitor data and airborne multidate multispectral digital imagery and to identify optimal time periods for image acquisition. Color-infrared (CIR) digital images were acquired from three grain sorghum fields on five different dates during the 1998 growing season. Yield data were also collected from these fields using a yield monitor. The images and the yield data were georeferenced to a common coordinate system. Four vegetation indices (two band ratios and two normalized differences) were derived from the green, red, and near-infrared (NIR) band images. The image data for the three bands and the four vegetation indices were aggregated to generate reduced-resolution images with a cell size equivalent to the combine's effective cutting width. Correlation analyses showed that grain yield was significantly related to the digital image data for each of the three bands and the four vegetation indices. Multiple regression analyses were also performed to relate grain yield to the three bands and to the three bands plus the four indices for each of the five dates. Images taken around peak vegetative development produced the best relationships with yield and explained approximately 63, 82, and 85% of yield variability for fields 1, 2, and 3, respectively. Yield maps generated from the image data using the regression equations agreed well with those from the yield monitor data. These results demonstrated that airborne digital imagery can be a very useful tool for determining yield patterns before harvest for precision agriculture.     

Comparison of QuickBird Satellite Imagery and Airborne Imagery for Mapping Grain Sorghum Yield Patterns

Timely and accurate information on crop conditions obtained during the growing season is of vital importance for crop management. High spatial resolution satellite imagery has the potential for mapping crop growth variability and identifying problem areas within fields. The objectives of this study were to use QuickBird satellite imagery for mapping plant growth and yield patterns within grain sorghum fields as compared with airborne multispectral image data. A QuickBird 2.8-m four-band image covering a cropping area in south Texas, USA was acquired in the 2003 growing season. Airborne three-band imagery with submeter resolution was also collected from two grain sorghum fields within the satellite scene. Yield monitor data collected from the two fields were resampled to match the resolutions of the airborne imagery and the satellite imagery. The airborne imagery was related to yield at original submeter, 2.8 and 8.4 m resolutions and the QuickBird imagery was related to yield at 2.8 and 8.4 m resolutions. The extracted QuickBird images for the two fields were then classified into multiple zones using unsupervised classification and mean yields among the zones were compared. Results showed that grain yield was significantly related to both types of image data and that the QuickBird imagery had similar correlations with grain yield as compared with the airborne imagery at the 2.8 and 8.4 m resolutions. Moreover, the unsupervised classification maps effectively differentiated grain production levels among the zones. These results indicate that high spatial resolution satellite imagery can be a useful data source for determining plant growth and yield patterns for within-field crop management.     

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.     

Airborne hyperspectral imagery and linear spectral unmixing for mapping variation in crop yield

Spectral unmixing techniques can be used to quantify crop canopy cover within each pixel of an image and have the potential for mapping the variation in crop yield. This study applied linear spectral unmixing to airborne hyperspectral imagery to estimate the variation in grain sorghum yield. Airborne hyperspectral imagery and yield monitor data recorded from two sorghum fields were used for this study. Both unconstrained and constrained linear spectral unmixing models were applied to the hyperspectral imagery with sorghum plants and bare soil as two endmembers. A pair of plant and soil spectra derived from each image and another pair of ground-measured plant and soil spectra were used as endmember spectra to generate unconstrained and constrained soil and plant cover fractions. Yield was positively related to the plant fraction and negatively related to the soil fraction. The effects of variation in endmember spectra on estimates of cover fractions and their correlations with yield were also examined. The unconstrained plant fraction had essentially the same correlations (r) with yield among all pairs of endmember spectra examined, whereas the unconstrained soil fraction and constrained plant and soil fractions had r-values that were sensitive to the spectra used. For comparison, all 5151 possible narrow-band normalized difference vegetation indices (NDVIs) were calculated from the 102-band images and related to yield. Results showed that the best plant and soil fractions provided better correlations than 96.3 and 99.9% of all the NDVIs for fields 1 and 2, respectively. Since the unconstrained plant fraction could represent yield variation better than most narrow-band NDVIs, it can be used as a relative yield map especially when yield data are not available. These results indicate that spectral unmixing applied to hyperspectral imagery can be a useful tool for mapping the variation in crop yield.     

玉米穗粒重与果穗三维几何特征关系的定量研究

【目的】从穗粒重与果穗三维几何关系的角度探索籽粒产量的制约因素,寻找进一步提高玉米产量的途径。【方法】利用图像处理技术采集了10个品种的果穗几何特征,分析了穗粒重对果穗几何特征组合的回归,以及穗粒重与穗大小特征的相关性。矩形度定义为果穗面积占其外接矩形面积的比例,分别与穗长+穗粗、穗面积、穗体积组合建立回归方程。【结果】上述3种组合方程,分别解释了品种间籽粒产量总变异的77.7%、70%和78.7%,矩形度的贡献大于或者约等于穗大小几何特征。同样结构的回归方程在矫正品种产量后,解释了环境间籽粒产量总变异的81.3%—82.0%,矩形度的贡献小于穗大小几何特征。穗大小对籽粒产量的简单决定系数为:在品种间,三种维数的大小特征都不显著;在环境间,穗长、穗粗、穗面积、穗体积分别为0.387、0.167、0.590、0.571。【结论】穗大小单一特征的重要性次序为:穗体积穗面积穗长、穗粗,穗矩形度是反映穗形态的一个重要性状,与穗大小特征相组合,能够高精度预测穗粒重。     

Evaluating high resolution SPOT 5 satellite imagery to estimate crop yield

High resolution satellite imagery has the potential to map within-field variation in crop growth and yield. This study examined SPOT 5 satellite multispectral imagery for estimating grain sorghum yield. A 60 km × 60 km SPOT 5 scene and yield monitor data from three grain sorghum fields were recorded in south Texas. The satellite scene contained four spectral bands (green, red, near-infrared and mid-infrared) with a 10-m spatial resolution. Subsets were extracted from the scene that covered the three fields. Images with pixel sizes of 20 and 30 m were also generated from the individual field images to simulate coarser resolution satellite imagery. Vegetation indices and principal components were derived from the images at the three spatial resolutions. Grain yield was related to the vegetation indices, the four bands and the principal components for each field, and for all the fields combined. The effect of the mid-infrared band on estimates of yield was examined by comparing the regression results from all four bands with those from the other three bands. Statistical analysis showed that the 10-m, four-band image and the aggregated 20-m and 30-m images explained 68, 76 and 83%, respectively, of the variation in yield for all the fields combined. The coefficient of determination between yield and the imagery increased with pixel size because of the smoothing effect. The inclusion of the mid-infrared band slightly improved the R ² values. These results indicate that high resolution SPOT 5 multispectral imagery can be a useful data source for determining within-field yield variation for crop management.     

Field methods for making productivity classes for site-specific management of wheat

Reducing the decision-making unit to classes within fields can improve yields, efficiency in the use of nutrients and profitability of crops. The objectives were to compare methods for class delimitation in wheat (Triticum aestivum L.) crops based on apparent productivity levels and establish similarities among them in terms of spatial overlapping, productive attributes and the use of nitrogen. In three wheat fields, high and low apparent productivity classes (APC) were defined based on eight methodologies: yield maps, soil maps, gramineae vegetation index, rotation crop index, interpretation of satellite images, management records, elevation and integrated soil and yield maps. In each APC, soil and crop yield components were determined under five nitrogen fertilization levels. Among delimitation methodologies, the degree of coincidence varied from 1.4 to 81.7%. The differences in soil properties, nitrogen use efficiency and grain yields were greater among fields than among APC within each field. In each field, the delimitation methodologies identified different single factors that discriminated among the potential management classes and were partially associated with the crop grain yields. The wheat crops at the low APC yielded 39% less and 12% less than at the high APC, respectively. The nitrogen fertilization, at the rate for maximum productivity for each ACP, reduced the yield differences between contrasting APC. Nitrogen fertilization also modified clustering of classes based on expected yields. Making management classes for wheat based on expected productivity is more accurate when based on previous crop production information under similar nitrogen fertilization conditions than the targeted crop.

     

攀西地区水稻高产氮肥运筹模式的研究

以攀西地区安宁河流域主推的杂交籼型优质稻金优527和常规粳型优质稻合系39为材料,采用田间试验方法,研究了氮肥运筹方式对水稻产量的影响,结果表明,氮肥用量对2供试品种产量的效应较穗粒肥比例的效应大,只有在适宜的氮肥水平和穗粒肥比例下才能获得水稻的高产;供试籼型品种对氮肥的敏感性较粳型品种高,其高产的适宜氮肥用量较粳型品种低。本文还分别建立了水稻产量与氮肥用量和穗粒肥比例的二次回归模型,并经统计分析得出了金优527产量≥10.5t/hm2、合系39产量≥10.5t/hm2的优化氮肥用量和穗粒肥比例。     

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.     

高产田鲁麦22号产量构成优化分析

１９９５～１９９６年对鲁麦２２号小麦亩产５５０ｋｇ以上的２０块高产田进行了分析。该品种亩穗数、穗粒数和千粒重与产量的偏相关系数分别为０．９８８７＊＊、０．９７６７＊＊和０．８６５１＊＊；三因素间偏相关关系分析，亩穗数与穗粒数间有微弱的正相关，但与千粒重间呈极显著的负相关，穗粒数与千粒重间有显著的正相关。通径分析，亩穗数、穗粒数、千粒重对产量的净作用分别为０．７３４３、－０．０５２９和－０．１６４１。表明亩穗数对产量贡献最大，是该品种的主攻方向。通常亩产６００ｋｇ的适宜产量构成为３０．８６～３１．９８万穗／亩、４４．８～４６．８粒／穗、千粒重４８．９～５１．５ｇ     

Empirical Modeling of Relationships Between Sorghum Yield and Soil Properties

A crucial part of any site-specific management is the identification of causes of yield variability and assessment of crop requirements. Therefore, relationships between yield and soil properties must be identified. In this study, relationships between sorghum yield and soil properties on a verbosols within a field located in Moree, in northern NSW, Australia, were examined. Measured soil properties included pH; available phosphorus; percent clay, silt and sand; gravimetric moisture content of air-dry soil and at matric potentials corresponding to –1 500 kPa and –33 kPa; percent organic carbon; CEC and exchangeable calcium, magnesium, sodium and potassium and copper, zinc, manganese and iron contents. The exchangeable sodium percentage (ESP) and the Ca/Mg ratio were calculated. We used a number of empirical methods and found that neural networks, projection pursuit regression, generalized additive models and regression trees are good techniques for modeling yield response. However, further comparison of these techniques is needed. By modeling yield response to individual soil properties and using kriging to map yields predicted from these models, it was possible to identify which soil properties limited production in different areas of the field.     

不同类型高粱主要农艺性状与品质性状差异分析

对来自国内外60份不同类型高粱材料的农艺性状、产量性状和品质性状在新疆干旱区的表现进行分析比较,目的是了解不同高粱类型在新疆干旱区的差异表现,为新疆干旱区能源用高粱品种选育提供参考依据.研究发现,供试高粱材料间生育期差异显著(P＜0.05),其中,甜高粱的生育期最长,平均为116.8 d,变幅在100.5～131.5 d之间.农艺性状分析发现,帚高粱的株高最高,平均为275.2 cm,籽粒高粱最矮,平均只有235.6 cm.甜高粱的伸长节间数最多,平均为12.6个,其中,甜高粱X054伸长节间数最多,为15.6个.各高粱类型的产量构成差异较大,籽粒高粱的籽粒产量最高,平均为7.9 t/hm2,而甜高粱和帚高粱的茎秆产量较高,分别为10.3和10.2t/hm2,全株总干物质积累量以甜高粱为最高,平均为25.3 t/hm2,变幅在18.4～31.5 t/hm2之间,其次为帚高粱的23.6 t/hm2,籽粒高粱最低,平均只有20.3 t/hm2.品质性状分析表明,籽粒各组分含量最为稳定,各高粱类型间差异较小,其次为叶片和叶鞘,而不同高粱类型间茎秆的碳水化合物含量和木质素含量差异较大,其中,甜高粱表现出更高的可溶性总糖含量,平均为236.6 g/kg,是籽粒高粱的3倍、帚高粱的2倍之多,而甜高粱的木质纤维素含量明显低于籽粒高粱和帚高粱,特别是纤维素含量,平均要低100 g/kg以上.碳水化合物产量分析发现甜高粱和帚高粱的平均积累量较高,分别为14.2和13.8 t/hm2,各高粱类型中均以纤维素和半纤维的贡献最大,特别是帚高粱材料,平均贡献率达65.9％.综上,相比于籽粒高粱和帚高粱,甜高粱整体表现出更强的能源利用潜力.同时应指出,各类型高粱材料均为宝贵的种质资源,应充分的挖掘利用,以筛选并进一步培育出适合新疆干旱区种植的能源用高粱新品种.     

Interpreting Within-Field Relationships Between Crop Yields and Soil and Plant Variables Using Factor Analysis

Precision farming technologies allow for collection of large amounts of data from producers' fields. This study used grid-sampling techniques and factor analysis to investigate relationships between several site variables and corn (Zea mays L.) yields on five producer's fields. Sampling positions (112 to 258) were at the intersecting points of grid lines spaced 15 m. Variables measured were soil organic matter, pH, P, K, and NO3-N; residue cover; broadleaf and grass weed control; corn height at two dates, plant population, and grain yield. Correlation and multiple regression analyses showed that some variables were related to corn yields but the variables involved in significant relationships varied among fields. Moreover, the site variables often were highly correlated and the correlations varied among fields. In these conditions multiple regression would be an unreliable analysis tool. Study of covariance relationships among the variables using factor analysis showed that some of the variables measured could be grouped to indicate a number of underlying common factors influencing corn yields. These common factors were soil fertility, weed control, and conditions for early plant growth. Their importance in explaining the yield variability differed greatly among fields. Application of factor analysis to data generated by precision-farming technologies has potential for describing and understanding relationships between measured variables.     

Block Estimating of Spatial Yield Data and its Uncertainty

On-the-go yield monitors have been available for both grain and bulk crops. Most of the yield monitors today provide yield measurement at a fixed time interval. Conversion of these point yield data into raster yield maps for further analysis is necessary. In this study, a data-blocking procedure is proposed to create raster yield maps from point yield data. The blocking procedure includes: (1) converting the fixed-time-interval data into fixed-distance-interval data; (2) using a moving average algorithm to estimate a cell value when there are sufficient data points within the cell; (3) using a geostatistical algorithm to estimate a cell value when there are not enough data points within the cell but values of its neighboring cells are known; and (4) calculating an uncertainty index for each cell value estimation. An example application of the yield-blocking procedure with potato harvest data in 1996 was given.     

Simulation of corn yields in the Upper Great Lakes Region of the US using a modeling framework

We examined the utility of a site-based simulation model for corn (MAIZE) applied to the Upper Great Lakes Region (UGLR) of the United States for 1992–1996. To evaluate model outputs, we used a modeling applications system integrative framework (MASIF) to simulate corn yields in the UGLR. We divided the region into two ecological zones (upper UGLR-1 and lower UGLR-2) and compared MAIZE and USDA National Agriculture Statistics Service (NASS) yield estimates in each zone. There was greater agreement between MAIZE and NASS yields at lower yield values. As NASS yields increased, MAIZE yields also increased, but to a greater extent than did NASS yields. We conducted a mean spatial analysis of maize yield from 1992 to 1996. We also conducted a spatial analysis of 1994, 1995 and 1996. There are both spatial and temporal differences when yields are mapped.     

辽宁省粮食作物的生产潜力和高产对策分析

从作物产量限制因子和产量形成规律分析了辽宁省粮食作物的生产潜力．辽宁省Ｃ４作物如玉米、高梁的最高理论亩产量可达１４３４ｋｇ，Ｃ３作物如水稻可达１０７０ｋｇ．依靠政策、科学技术和物资投入是实现作物高产的保证；加强高产栽培技术配套措施的研究和推广，搞好作物品种改良是实现高产的技术途径．     

不同肥料配施处理对甜高粱产量及锤度的影响

[目的]研究不同肥料配施处理对新疆灰漠土种植甜高粱的影响,以期找到最佳施肥方式.[方法]通过对不同品种甜高粱在不同施肥方式下的产量和锤度的研究.[结果]不同品种对甜高粱产量的影响达极显著水平,对锤度影响不明显;不同施肥方式对甜高粱产量影响不显著,对甜高粱锤度的影响达极显著水平;品种-施肥不同方式互作对甜高粱产量和锤度的影响均达到极显著水平.[结论]无论从产量还是锤度结果来看,等量化施肥处理都取得更好的效果.     

不同种植密度对高粱成穗数、产量及其构成因素的影响

[目的]为了探讨不同种植密度对高粱成穗数、产量及其构成因素的影响,确定酿造高粱品种在旱地的合理种植密度,[方法]本试验以酿造高粱‘晋杂23号’为试验材料,在山西省农科院经济作物研究所试验田研究了不同种植密度（6.00万株/hm2、8..25 万株/hm2、10.50 万株/hm2、12.75 万株/hm2、15.00 万株/hm2）对高粱生长、产量及其构成因素、成穗数的影响。[结果]结果表明：随着种植密度的增加,高粱株高、茎粗、穗长、最大叶面积、穗粒重、千粒重呈下降趋势,但差异不显著。在6.00万株/hm2～10.50万株/hm2的密度范围内,籽粒产量随密度增加呈大幅度增加;当密度超过10.50万株/hm2时,籽粒产量趋于平稳。回归分析表明,高粱种植密度与成穗数间呈极显著的正相关,回归方程为y=26866.222+0.555x。[结论]综合分析,酿造高粱‘晋杂23号’的种植密度以10.50万株/hm2为宜。该研究为酿造高粱在该地区种植获得高产提供了一定理论依据。     

Can Landform Stratification Improve Our Understanding of Crop Yield Variability?

Farmers account for yield and soil variability to optimize their production under mainly economic considerations using the technology of precision farming. Therefore, understanding of the spatial variation of crop yield and crop yield development within arable fields is important for spatially variable management. Our aim was to classify landform units based on a digital elevation model, and to identify their impact on biomass development. Yield components were measured by harvesting spring barley (Hordeum vulgare, L.) in 1999, and winter rye (Secale cereale, L.) in 2000 and 2001, respectively, at 192 sampling points in a field in Saxony, Germany. The field was stratified into four landform units, i.e., shoulder, backslope, footslope and level. At each landform unit, a characteristic yield development could be observed. Spring barley grain yields were highest at the level positions with 6.7 t ha⁻¹ and approximately 0.15 t ha⁻¹ below that at shoulder and footslope positions in 1999. In 2000, winter rye harvest exhibited a reduction at backslope positions of around 0.2 t ha⁻¹ as compared to the highest yield obtained again at level positions with 11.1 t ha⁻¹. The distribution of winter rye grain yield across the different landforms was completely different in 2001 from that observed in 2000. Winter rye showed the highest yields at shoulder positions with 11.1 t ha⁻¹, followed by the level position with 0.5 t ha⁻¹ less grain yield. Different developments throughout the years were assumed to be due to soil water and meteorological conditions, as well as management history. Generally, crop yield differences of up to 0.7 t ha⁻¹ were found between landform elements with appropriate consideration of the respective seasonal weather conditions. Landform analysis proved to be helpful in explaining variation in grain yield within the field between different years.