Exploitation of spatial information in high resolution digital imagery to map leaf area index

Crop responses to management practices and the environment, as quantified by leaf area index (LAI), provide decision-making criteria for the delineation of crop management zones. The objective of this work was to investigate whether spatial correlations inferred from remotely sensed imagery can be used to interpolate and map LAI using a relatively small number of ground-based LAI measurements. Airborne imagery was recorded with the Airborne Imaging Spectrometer for Applications (AISA) radiometer over a 3.2 ha corn field. Spectral vegetation indexes (SVI) were derived from the image and aggregated to cells of 2 × 2 m², 4 × 4 m², and 8 × 8 m² resolution. The residual maximum likelihood method was used to estimate the LAI variogram parameters. A generalized least squares regression was used to relate ground truth LAI data and collocated image pixels. This regression result was then used to convert variograms from the imagery to LAI units as well as to interpolate and map LAI. The decrease in resolution by merging pixels led to an increase in the value of the r ² and to a decrease in root mean-squared error (RMSE) values. The accuracy of kriged estimates from the variogram of the measured LAI and that from the image derived variograms was estimated by cross-validation. There was no difference in the accuracy of the estimates using either variograms from measured LAI values or from those of converted SVIs. Maps of LAI from ground-based measurements made by kriging the data with image-derived variogram parameters were similar to those obtained by with kriging with the variogram of measured LAI. Similar coarse spatial trends of high, medium and low LAI were evident for both maps. Variogram parameters from ground-based measurements of LAI compared favorably with those derived from remotely sensed imagery and could be used to provide reasonable results for the interpolation of LAI measurements.     

Feasibility of Site-Specific Management of Corn Hybrids and Plant Densities in the Great Plains

The goal of this research was to determine the potential for use of site-specific management of corn hybrids and plant densities in dryland landscapes of the Great Plains by determining (1) within-field yield variation, (2) yield response of different hybrids and plant densities to variability, and (3) landscape attributes associated with yield variation. This work was conducted on three adjacent fields in eastern Colorado during the 1997, -98, and -99 seasons. Treatments consisted of a combination of two hybrids (early and late maturity) and four plant densities (24,692, 37,037, 49,382 and 61,727 plants ha^-1) seeded in replicated long strips. At maturity, yield was measured with a yield-mapping combine. Nine landscape attributes including elevation, slope, soil brightness (SB) (red, green, and blue bands of image), EC_a (shallow and deep readings), pH, and soil organic matter (SOM) were also assessed. An analysis of treatment yields and landscape data, to assess for spatial dependency, along with semi variance analysis, and block kriging were used to produce kriged layers (10 m grids). Linear correlation and multiple linear regression analysis were used to determine associations between kriged average yields and landscape attributes. Yield monitor data revealed considerable variability in the three fields, with average yields ranging from 5.43 to 6.39 Mg ha^-1 and CVs ranging from 20% to 29%. Hybrids responded similarly to field variation while plant densities responded differentially. Economically optimum plant densities changed by around 5000 plants ha^-1 between high and low-yielding field areas, producing a potential savings in seed costs of $6.25 ha^-1. Variability in yield across the three landscapes was highly associated with landscape attributes, especially elevation and SB, with various combinations of landscape attributes accounting for 47%, 95%, and 76% of the spatial variability in grain yields for the 1997, -98, and -99 sites, respectively. Our results suggest site-specific management of plant densities may be feasible.     

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.     

Determining nugget:sill ratios of standardized variograms from aerial photographs to krige sparse soil data

Maps of kriged soil properties for precision agriculture are often based on a variogram estimated from too few data because the costs of sampling and analysis are often prohibitive. If the variogram has been computed by the usual method of moments, it is likely to be unstable when there are fewer than 100 data. The scale of variation in soil properties should be investigated prior to sampling by computing a variogram from ancillary data, such as an aerial photograph of the bare soil. If the sampling interval suggested by this is large in relation to the size of the field there will be too few data to estimate a reliable variogram for kriging. Standardized variograms from aerial photographs can be used with standardized soil data that are sparse, provided the data are spatially structured and the nugget:sill ratio is similar to that of a reliable variogram of the property. The problem remains of how to set this ratio in the absence of an accurate variogram. Several methods of estimating the nugget:sill ratio for selected soil properties are proposed and evaluated. Standardized variograms with nugget:sill ratios set by these methods are more similar to those computed from intensive soil data than are variograms computed from sparse soil data. The results of cross-validation and mapping show that the standardized variograms provide more accurate estimates, and preserve the main patterns of variation better than those computed from sparse data.     

葡萄产量构成相关性状的通径分析

通过调查测定巨峰、红富士、红瑞宝、玫瑰香、白香蕉5个品种葡萄的单株产量及与其相关的单株结果母枝数,以及结果母枝的剪留芽数、萌芽率、结果枝率、结果系数、果粒重、每穗果粒数、果穗重8个性状,应用逐个选入显著自变数的回归方法,得到包括单株结果母枝数、果粒重和每穗果粒数在内的最优多元线性回归方程。再对入选的3个主要性状进行通径分析,明确了提高葡萄单株产量,首先要具有一定的结果母枝数,这是形成枝叶和果穗的基础;其次要达到一定的果粒数;再次为增大果粒重。     

调亏灌溉对‘阳光玫瑰’葡萄产量品质和水分利用率的影响

为了探索杨凌地区最佳的‘阳光玫瑰’葡萄果实生长及品质的水分调控阈值。在‘阳光玫瑰’葡萄的花期、果实膨大期和着色成熟期分别设置轻（田间持水量65%的土壤含水量）、中（田间持水量55%的土壤含水量）两种干旱胁迫,以全生育期充分供水为对照,研究不同生育期调亏灌溉对‘阳光玫瑰’葡萄生理生长状态、果实产量、品质指标及水分利用率的影响。结果表明,与充分供水相比,花期中度干旱胁迫提高果实品质和产量,每667 m²产量增加5.52%,水分利用率提高8.58%;果实膨大期轻度干旱胁迫每667 m²产量减少4.42%;着色成熟期中度干旱胁迫显著提高果实品质,但每667 m²产量减少0.39%,水分利用率提高2.49%。综合生长指标、水分利用率、产量及品质指标四个方面得出花期中度水分胁迫最佳灌溉制度。     

Unmanned aerial vehicle canopy reflectance data detects potassium deficiency and green peach aphid susceptibility in canola

There is growing evidence that potassium deficiency in crop plants increases their susceptibility to herbivorous arthropods. The ability to remotely detect potassium deficiency in plants would be advantageous in targeting arthropod sampling and spatially optimizing potassium fertilizer to reduce yield loss due to the arthropod infestations. Four potassium fertilizer regimes were established in field plots of canola, with soil and plant nutrient concentrations tested on three occasions: 69 (seedling), 96 (stem elongation), and 113 (early flowering) days after sowing (DAS). On these dates, unmanned aerial vehicle (UAV) multi-spectral images of each plot were acquired at 15 and 120 m above ground achieving spatial (pixel) resolutions of 8.1 and 65 mm, respectively. At 69 and 96 DAS, field plants were transported to a laboratory with controlled lighting and imaged with a 240-band (390–890 nm) hyperspectral camera. At 113 DAS, all plots had become naturally infested with green peach aphids (Hemiptera: Aphididae), and intensive aphid counts were conducted. Potassium deficiency caused significant: (1) increase in concentrations of nitrogen in youngest mature leaves, (2) increase in green peach aphid density, (3) decrease in vegetation cover, (4) decrease in normalized difference vegetation indices (NDVI) and decrease in canola seed yield. UAV imagery with 65 mm spatial resolution showed higher classification accuracy (72–100 %) than airborne imagery with 8 mm resolution (69–94 %), and bench top hyperspectral imagery acquired from field plants in laboratory conditions (78–88 %). When non-leaf pixels were removed from the UAV data, classification accuracies increased for 8 mm and 65 mm resolution images acquired 96 and 113 DAS. The study supports findings that UAV-acquired imagery has potential to identify regions containing nutrient deficiency and likely increased arthropod performance.     

Improving Map Accuracy of Soil Variables Using Soil Electrical Conductivity as a Covariate

Accurate characterization of soil properties across a field can be difficult, especially when compounded with the diverse landscapes used for pastureland. Indirect methods of data collection have the advantage of being rapid, noninvasive, and dense; they may improve mapping accuracy of selected soil parameters. The objective of this study was to determine if the use of soil electrical conductivity (EC) as a covariate improved mapping accuracy of five soil variables across four sampling schemes and two sampling densities in a central Iowa, USA pasture. In this study, cokriging methods were compared to kriging methods for the measured soil properties of soil pH, available P and K, organic matter and moisture. Maps resulting from cokriging each of the soil variables with soil EC exhibited more local detail than the kriged maps of each soil variable. A small, but inconsistent, improvement occurred in kriging variance and prediction accuracy of non-sampled sites when cokriging was implemented. The improvement was generally greater for soil variables more highly correlated with soil EC. This work indicates that cokriging of EC with less densely and invasively collected soil parameters of P, K, pH, organic matter (OM) and moisture does not consistently and substantially improve the characterization accuracy of pasture soil variability.     

Mapping and identifying basal stem rot disease in oil palms in North Sumatra with QuickBird imagery

The application of remote sensing technology and precision agriculture in the oil palm industry is in development. This study investigated the potential of high resolution QuickBird satellite imagery, which has a synoptic overview, for detecting oil palms infected by basal stem rot disease and for mapping the disease. Basal stem rot disease poses a major threat to the oil palm industry, especially in Indonesia. It is caused by Ganoderma boninense and the symptoms can be seen on the leaf and basal stem. At present there is no effective control for this disease and early detection of the infection is essential. A detailed, accurate and rapid method of monitoring the disease is needed urgently. This study used QuickBird imagery to detect the disease and its spatial pattern. Initially, oil palm and non oil palm object segmentation based on the red band was used to map the spatial pattern of the disease. Secondly, six vegetation indices derived from visible and near infrared bands (NIR) were used for to identify palms infected by the disease. Finally, ground truth from field sampling in four fields with different ages of plant and degrees of infection was used to assess the accuracy of the remote sensing approach. The results show that image segmentation effectively delineated areas infected by the disease with a mapping accuracy of 84%. The resulting maps showed two patterns of the disease; a sporadic pattern in fields with older palms and a dendritic pattern in younger palms with medium to low infection. Ground truth data showed that oil palms infected by basal stem rot had a higher reflectance in the visible bands and a lower reflectance in the near infrared band. Different vegetation indices performed differently in each field. The atmospheric resistant vegetation index and green blue normalized difference vegetation index identified the disease with an accuracy of 67% in a field with 21 year old palms and high infection rates. In the field of 10 year old palms with medium rates of infection, the simple ratio (NIR/red) was effective with an accuracy of 62% for identifying the disease. The green blue normalized difference vegetation index was effective in the field of 10 years old palms with low infection rates with an accuracy of 59%. In the field of 15 and 18 years old palms with low infection rates, all the indices showed low levels of accuracy for identifying the disease. This study suggests that high resolution QuickBird imagery offers a quick, detailed and accurate way of estimating the location and extent of basal stem rot disease infections in oil palm plantations.     

Spatial variation of fiber quality and associated loan rate in a dryland cotton field

A field study was conducted in 2006 in a dryland cotton field in Texas, USA, to explore the spatial variation of cotton fiber quality and the loan rate associated with it. A total of 66 cotton samples were hand-harvested, and the fiber quality properties investigated included the High Volume Instrument measurements of micronaire, length, uniformity, strength, elongation, reflectance (Rd) and yellowness (+b). Conventional statistics showed a generally low level of variation in fiber quality with coefficients of variation <10%. Variogram analysis showed that all fiber quality properties were spatially correlated. Contour maps of individual fiber quality properties were produced from block kriged estimates. Fiber length, uniformity, strength and Rd were positively correlated, and all of these were negatively correlated with +b. The spatial distribution of most fiber quality properties was similar to that of soil apparent electrical conductivity, suggesting that water holding capacity could be a limiting factor for cotton fiber quality. Maps of individual fiber quality properties were combined with the United State Department of Agriculture—Commodity Credit Corporation Loan Schedule for Upland Cotton to create a loan rate map that is associated with fiber quality. A loan rate difference of 20 cents kg^–1 was observed within the field. This level of difference indicated that fiber quality at the field level can have a large impact on producers’ revenue. A site-specific management system encompassing both lint yields and fiber quality is strongly recommended for cotton production.     

Geostatistical modelling of within-field soil and yield variability for management zones delineation: a case study in a durum wheat field

The paper proposes a geostatistical approach for delineating management zones (MZs) based on multivariate geostatistics, showing the use of polygon kriging to compare durum wheat yield among the different MZs (polygons). The study site was a durum wheat field in southern Italy and yield was measured over three crop seasons. The first regionalized factor, calculated with factorial cokriging, was used to partition the field into three iso-frequency classes (MZs). For each MZ, the expected value and standard deviation of yield were estimated with polygon kriging over the three crop seasons. The yield variation was only in part related to soil properties but most of it might be ascribable to different patterns of meteorological conditions. Both components of variation (plant and soil) in a cropping system should then be taken into account for an effective management of rainfed durum wheat in precision agriculture. The proposed approach proved multivariate Geostatistics to be effective for MZ delineation even if further testing is required under different cropping systems and management.     

晋西沿黄枣区新引入制干品种的试验研究

为改变晋西沿黄枣区制干枣品种单一、品质较差的状况,筛选出适宜该枣区栽培的优良制干枣品种,以引入到该枣区的12个制干枣品种为研究对象,对各品种的主要物候特性、树体生长结果表现、单株产量、抗裂性、抗缩果病性和果实品质进行了观测分析。结果表明,临黄1号、圆铃枣、相枣、赞皇大枣、陕北长枣、方木枣和木枣抗裂1号等7个品种能较好地适应该枣区的气候和土壤条件,丰产性较好,制干品质优良,并且具有较强的抗裂性和抗缩果病性,与该枣区主栽品种中阳木枣和保德油枣相比,具有明显的综合优势,可以作为替代品种进行栽培推广。     

Foliage temperature extraction from thermal imagery for crop water stress determination

Crop water stress determination methods from canopy temperatures, derived from the surface energy balance equations, treat the canopy temperature under the assumption that the canopy behaves as a virtual “big-leaf”, covering the ground surface. Introduction of very high-resolution thermal imagery, 0.01–0.3-m pixel size, acquired from low altitude platforms, enabled finely detailed observation of the whole canopy, raising the question how to select the relevant canopy temperatures. One approach is to select the sunlit leaves confirming to the “big leaf” energy balance paradigm. However, thermal imagery alone is incomplete and needs additional marking or synchronized visible imagery for interpretation, which makes the process complicated and expensive. The other approach, used in reference surface based water stress evaluation, is to use full frame pixel statistics without pattern recognition by selecting the mean temperature of the cold fraction from the pixel histogram. That greatly simplifies processing for large-scale aerial thermography. Here are presented the results of experiments conducted in cotton and vine grapes, where both approaches were evaluated simultaneously. Ground referenced thermal and visible images were overlapped, and sunlit, shaded and whole canopy leaves were selected for crop temperature evaluation. The pixel histograms of the same images were analyzed in a two-step method, after discarding soil pixels where their temperature was 7 °C higher than air temperature at step one, and calculation of the mean temperatures of the lowest 33 and 100 % of the remaining pixels for step two. Several crop water stress indices were compared with leaf and stem water potentials and stomatal conductance. Good agreement was found between both image segmentation and histogram analysis methods, demonstrating the suitability of both methods in canopy temperature evaluation for crop water stress evaluation.     

Soil Phosphorus and Potassium Mapping Using a Spatial Correlation Model Incorporating Terrain Slope Gradient

Variable-rate fertilizer application requires knowledge of the spatial distribution of soil nutrients within fields. Grid soil sampling might be used for acquiring this information, but is often too expensive for resolving spatial patterns in soil nutrients at the scale of precision fertilizer application. The objective of this study was to determine whether grid sampling efficiency can be improved using cokriging estimates with slope gradient as a secondary variable, which is easily obtained from high-resolution digital elevation models. Soils in two northern Montana wheat fields were sampled at the nodes of a 100-m diagonal grid. Soil test phosphorus and potassium maps were constructed with kriging and cokriging. Co-kriging uses the spatial correlation between two variables to predict for the less intensively sampled variable of interest, often with less estimation error than a univariate method such as kriging. The average estimation variance for cokriging compared to kriging was reduced for all values of the correlation considered. The additional complexity of cokriging might be justified provided a secondary variable exists that is spatially cross correlated with the primary variable of interest.     

不同株行距对核桃光合作用的影响

以香玲和西林3号核桃为材料,采用LI-COR6400XT便携式光合作用测定系统在不同品种相同株行距及相同品种不相同株行距情况下,测定西林3号和香玲核桃的光合参数,并利用LAI2200c冠层分析仪测定叶面积指数等指标。结果表明:1)相同株行距下,西林3号的净光合速率(Pn)和蒸腾速率(Tr)均高于香玲;同一品种株行距越大,Pn及Tr越大。2)同一株行距下,西林3号的水分利用效率(WUE)均高于香玲;同一品种,水分利用效率(WUE)和光能利用效率(LUE)随株行距的增大而升高。3)在5m×6m株行距下,西林3号和香玲的叶面积指数(LAI)均明显低于2.5m×6m、4m×4m、5m×3m株行距下的叶面积指数(LAI),过高的叶面积指数会降低核桃树的光合作用。4)胞间CO2浓度(Ci)和气孔限制值(Ls)在不同株行距,不同品种的变化趋势基本相同,表明品种和株行距不会影响由气孔因素和非气孔因素造成的光合速率下降。5)随着株行距的增加,核桃的单株产量和单果重以及亩产量和亩产值均有提升,说明株行距对核桃的品质及产量产值会产生明显的影响。通过测定数据得出不同株行距对核桃的生长发育状况影响较大,净光合速率、气孔导度、水分利用效率和光能利用效率等指标随株行距的增加均有所上升,叶面积指数明显下降,核桃单株产量及质量随株行距的增加均有提高,其中在5m×6m株行距条件下,核桃光合作用最强烈,叶面积指数最小,单株产量和单果重量最高,亩产量和亩产值最大。因此在相同立地条件下,核桃栽植的适宜株行距为5m×6m。     

A system for collecting spatially variable terrain data

Data describing spatially variable parameters and state-variables is required to better understand catchment behaviour and to improve land management. Remote sensing can provide some of this data economically; however, there exists a need for cheaper methods of ground based measurement of variables which cannot be remotely sensed, both for ground truth data and for high resolution data. A system for ground based collection of spatially variable data has been developed. It is based on an all terrain vehicle fitted with a position fixing system and a variety of instruments including a Time Domain Reflectometry soil moisture meter, a soil penetrometer, soil corer, and infrared and near-infrared/visible radiometers. Field testing of the position fixing system and of the soil moisture monitoring equipment is described. Example soil moisture data sets are presented.     

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.     

枯草芽孢杆菌rocG基因插入失活突变株的构建

枯草芽孢杆菌中有2个编码谷氨酸脱氢酶(GDH)的基因:rocG基因和gudB基因。其中Bacillus subtilis 168菌株的rocG基因编码有活性的GDH,而gudB基因编码没有活性的GDH。敲除168菌株的rocG基因后,gudB基因会自发突变为gudB1基因,后者编码有活性的GDH(gudB1-GDH)。为了研究gudB1-GDH在代谢中的作用及调控机制,构建了pMUTin4-rocG插入突变重组质粒,将其转化至B.subtilis 168,成功获得了1株rocG失活突变株BS-△rocG。对BS-△rocG和野生株的生长情况及GDH酶活进行了比较,发现BS-△rocG生长情况和野生株相当,但其GDH酶活为4.641U/mg蛋白,高于野生株的3.042U/mg蛋白。由此推测BS-△rocG中,gudB可能自发突变为gudB1,从而能够编码有活性的GDH,这一结果为研究枯草芽孢杆菌gudB1-GDH在代谢中的作用及调控机制奠定了基础。     

“津绿80”优质中熟大白菜杂交新品种选育

利用自交不亲和系Ｊ９－４０５和杨１９４配制成优质中熟大白菜一代杂交新品种“津绿８０”,生育期８０～８５天,株高６０ｃｍ,球高５２ｃｍ,开展度６５ｃｍ,单株重３．５～４．０ｋｇ,为高桩直筒花心类型,株形直立紧凑,外叶少,叶色深绿,中肋浅绿色,叶纹适中,品质佳,２年品比试验每６６６．７ｍ２产量达７１０４．２～７２９５．０ｋｇ,比对照“津青９号”增产２９．１％～３２．６％。对病毒病、霜霉病和软腐病的抗性显著高于对照品种。     

地黄根腐病的诊断及科农牌1号生物制剂对地黄根腐病的防治效果

根据田间调查,对地黄根腐病的表症诊断和室内病原菌分离鉴定,明确了地黄根腐病的主要种类为枯萎病、白绢病、立枯病、疫病、枯腐病、黑色根腐病。在室内用科农牌1号生物制剂对地黄地病土及病组织汁液进行测定,抑菌效果为97.1%～100%。田间用运城1号种衣剂加20%甲基异柳磷乳油处理地黄根茎,用科农牌1号生物制剂处理土壤,防效为81.3%,小区产量比空白对照高58.6%。科农牌1号生物制剂无毒、无公害,是保护生态环境的一个新产品。