Using spectral distance, spectral angle and plant abundance derived from hyperspectral imagery to characterize crop yield variation

Vegetation indices (VIs) derived from remote sensing imagery are commonly used to quantify crop growth and yield variations. As hyperspectral imagery is becoming more available, the number of possible VIs that can be calculated is overwhelmingly large. The objectives of this study were to examine spectral distance, spectral angle and plant abundance (crop fractional cover estimated with spectral unmixing) derived from all the bands in hyperspectral imagery and compare them with eight widely used two-band and three-band VIs based on selected wavelengths for quantifying crop yield variability. Airborne 102-band hyperspectral images acquired at the peak development stage and yield monitor data collected from two grain sorghum fields were used. A total of 64 VI images were generated based on the eight VIs and selected wavelengths for each field in this study. Two spectral distance images, two spectral angle images and two abundance images were also created based on a pair of pure plant and soil reference spectra for each field. Correlation analysis with yield showed that the eight VIs with the selected wavelengths had r values of 0.73–0.79 for field 1 and 0.82–0.86 for field 2. Although all VIs provided similar correlations with yield, the modified soil-adjusted vegetation index (MSAVI) produced more consistent r values (0.77–0.79 for field 1 and 0.85–0.86 for field 2) among the selected bands. Spectral distance, spectral angle and plant abundance produced similar r values (0.76–0.78 for field 1 and 0.83–0.85 for field 2) to the best VIs. The results from this study suggest that either a VI (MSAVI) image based on one near-infrared band (800 or 825 nm) and one visible band (550 or 670 nm) or a plant abundance image based on a pair of pure plant and soil spectra can be used to estimate relative yield variation from a hyperspectral image.     

Spectral indices sensitively discriminating wheat genotypes of different canopy architectures

A field experiment of 18 wheat cultivars of erectophile, planophile and horizontal canopy architectures was conducted during the 2004–2005 growing seasons in Beijing (40°10.6′ N, 116°26.3′ E), China. Canopy reflectance (350–2500 nm) at different growth stages was measured and leaf area index (LAI) and leaf chlorophyll concentration (Chl) were determined at booting. The main objective of the study was to evaluate the ability of various vegetative indices (VIs) to detect canopy architectures in wheat genotypes. The chlorophyll-sensitive spectral indices, the modified chlorophyll absorption reflectance index (MCARI) and the transformed chlorophyll absorption reflectance index (TCARI), were very sensitive to canopy architectures in the wheat plants. The MCARI values were significantly (p < 0.05) larger for the horizontal genotypes than for the planophile ones, and also larger for the planophile genotypes than for the erectophile ones for the six growth stages. The TCARI had a similar power to MCARI for discriminating between different wheat canopy architectures. At booting, both MCARI and TCARI were only weakly related to Chl in the upper, middle and lower leaves. The results emphasized the difficulties of determining crop Chl from canopy reflectance. The mechanisms that cause the differences in MCARI and TCARI among the canopy architectures are discussed.     

Monitoring nitrogen,phosphorus and sulphur in hybrid rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.) using hyperspectral remote sensing

Till date, the remote sensing research on crop nutrient monitoring has focused mainly on biomass and nitrogen (N) estimation and only a few attempts had been made to characterize and monitor macronutrients other than N. Field experiments were undertaken to study the remote detection of macronutrient status of rice using hyperspectral remote sensing. The variability in soil available N, phosphorus (P) and sulphur (S) and their content in plants were created using artificial fertility gradient design. The leaf and canopy hyperspectral reflectance was captured from variable macronutrient status vegetation. Linear correlation analysis between the spectral reflectance and plant nutrient status revealed significantly (p < 0.05) higher correlation coefficient at 670, 700, 730, 1090, 1260, 1460 nm for the nutrient under study. Published and proposed vegetation indices (VIs) were tested for canopy N, P and S prediction. The results of the investigation revealed that, published VIs (NDVI hyper and NDVI broadbands) could retrieve canopy N with higher accuracy, but not P and S. The predictability of the visible and short wave infrared based VI NRI₁₅₁₀ ((R₁₅₁₀ ? R₆₆₀)/(R₁₅₁₀ + R₆₆₀)) was the highest (r = 0.81, p < 0.01) for predicting N. Based on the outcomes of linear correlation analysis new VIs were proposed for remote detection of P and S. Proposed VI P_670_1260 ((R₁₂₆₀ ? R₆₇₀)/(R₁₂₆₀ + R₆₇₀)) retrieved canopy P status with higher prediction accuracy (r = 0.67, p < 0.01), whereas significantly higher canopy S prediction (r = 0.58, p < 0.01) was obtained using VI S_670_1090 ((R₁₀₉₀ ? R₆₇₀)/(R₁₀₉₀ + R₆₇₀)). The proposed spectral algorithms could be used for real time and site-specific N, P and S management in rice. Nutrient specific wavelengths, identified in the present investigation, could be used for developing relatively low-cost sensors of hand-held instruments to monitor N, P and S status of rice plant.     

Prediction of yield and the contribution of legumes in legume-grass mixtures using field spectrometry

Productivity and botanical composition of legume-grass swards in rotation systems are important factors for successful arable farming in both organic and conventional farming systems. As these attributes vary considerably within a field, a non-destructive method of detection while doing other tasks would facilitate more targeted management of crops and nutrients in the soil–plant–animal system. Two pot experiments were conducted to examine the potential of field spectroscopy to assess total biomass and the proportions of legume, using binary mixtures and pure swards of grass and legumes. The spectral reflectance of swards was measured under artificial light conditions at a sward age ranging from 21 to 70 days. Total biomass was determined by modified partial least squares (MPLS) regression, stepwise multiple linear regression (SMLR) and the vegetation indices (VIs) simple ratio (SR), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and red edge position (REP). Modified partial least squares and SMLR gave the largest R ² values ranging from 0.85 to 0.99. Total biomass prediction by VIs resulted in R ² values of 0.87–0.90 for swards with large leaf to stem ratios; the greatest accuracy was for EVI. For more mature and open swards VI-based detection of biomass was not possible. The contribution of legumes to the sward could be determined at a constant biomass level by the VIs, but this was not possible when the level of biomass varied.     

Within-field nitrogen response in corn related to aerial photograph color

Precise management of nitrogen (N) using canopy color in aerial imagery of corn (Zea mays L.) has been proposed as a strategy on which to base the rate of N fertilizer. The objective of this study was to evaluate the relationship between canopy color and yield response to N at the field scale. Six N response trials were conducted in 2000 and 2001 in fields with alluvial, claypan and deep loess soil types. Aerial images were taken with a 35-mm slide film from ≥1100 m at the mid- and late-vegetative corn growth stages and processed to extract green and red digital values. Color values of the control N (0 kg N ha⁻¹) and sufficient N (280 kg N ha⁻¹ applied at planting) treatments were used to calculate the relative ratio of unfertilized to fertilized and relative difference color values. Other N fertilizer treatments included side-dressed applications in increments of 56 kg N ha⁻¹. The economic optimal N rate was weakly related (R ² ≤ 0.34) or not related to the color indices at both growth stages. For many sites, delta yield (the increase in yield between control N and sufficient N treatments) was related to the color indices (R ² ≤ 0.67) at the late vegetative growth stage; the best relationship was with green relative difference. The results indicate the potential for color indices from aerial photographs to be used for predicting delta yield from which a site-specific N rate could be determined.     

Maximum benefit of a precise nitrogen application system for wheat

Research is ongoing to develop sensor-based systems to determine crop nitrogen needs. To be economic and to achieve wide adoption, a sensor-based site-specific application system must be sufficiently efficient to overcome both the cost disadvantage of dry and liquid sources of nitrogen relative to applications before planting of anhydrous ammonia and possible losses if weather prevents applications during the growing season. The objective of this study is to determine the expected maximum benefit of a precision N application system for winter wheat that senses and applies N to the growing crop in the spring relative to a uniform system that applies N before planting. An estimate of the maximum benefit would be useful to provide researchers with an upper bound on the cost of delivering an economically viable precision technology. Sixty five site-years of data from two dryland winter wheat nitrogen fertility experiments at experimental stations in the Southern Plains of the U.S.A. were used to estimate the expected returns from both a conventional uniform rate anhydrous ammonia (NH₃) application system before planting and a precise topdressing system to determine the value of the latter. For prices of $0.55 and $0.33 kg⁻¹ N for urea-ammonium nitrate (UAN) and NH₃, respectively, the maximum net value of a system of precise sensor-based nitrogen application for winter wheat was about $22–$31 ha⁻¹ depending upon location and assumptions regarding the existence of a plateau. However, for prices of $1.10 and $0.66 kg⁻¹ N for UAN and NH₃, respectively, the value was approximately $33 ha⁻¹. The benefit of precise N application is sensitive to both the absolute and relative prices of UAN and NH₃.This is journal paper AEJ-260 of the Oklahoma Agricultural Experiment Station, project H-2574.     

Quantifying spatial variability of indigenous nitrogen supply for precision nitrogen management in small scale farming

Understanding spatial variability of indigenous nitrogen (N) supply (INS) is important to the implementation of precision N management (PNM) strategies in small scale agricultural fields of the North China Plain (NCP). This study was conducted to determine: (1) field-to-field and within-field variability in INS; (2) the potential savings in N fertilizers using PNM technologies; and (3) winter wheat (Triticum aestivum L.) N status variability at the Feekes 6 stage and the potential of using a chlorophyll meter (CM) and a GreenSeeker active crop canopy sensor for estimating in-season N requirements. Seven farmer’s fields in Quzhou County of Hebei Province were selected for this study, but no fertilizers were applied to these fields. The results indicated that INS varied significantly both within individual fields and across different fields, ranging from 33.4 to 268.4 kg ha⁻¹, with an average of 142.6 kg ha⁻¹ and a CV of 34%. The spatial dependence of INS, however, was not strong. Site-specific optimum N rates varied from 0 to 355 kg ha⁻¹ across the seven fields, with an average of 173 kg ha⁻¹ and a CV of 46%. Field-specific N management could save an average of 128 kg N ha⁻¹ compared to typical farmer practices. Both CM and GreenSeeker sensor readings were significantly related to crop N status and demand across different farmer’s fields, showing a good potential for in-season site-specific N management in small scale farming systems. More studies are needed to further evaluate these sensing technology-based PNM strategies in additional farmer fields in the NCP.     

基于双波段光谱仪CGMD-302的小麦叶面积指数和叶干重监测

为探究双波段光谱仪CGMD-302在监测小麦长势上的可靠性与精准性,同时使用高光谱仪UniSpec SC与双波段光谱仪CGMD-302测试各生育时期小麦冠层信息,并定量分析了植被指数NDVI、RVI、DVI与叶面积指数和叶片干重之间的线性关系。结果表明,基于相同波段反射率计算出的高光谱仪植被指数和双波段光谱仪植被指数均能较好监测小麦群体长势。在CGMD-302监测的叶面积指数模型中,拟合方程的决定系数(R~2)均高于0.89,用以检验模型的均方根误差(RMSE)和相对误差(RE)分别小于0.792和0.225;叶片干重模型中,决定系数(R2)均高于0.85,用以检验模型的均方根误差(RMSE)和相对误差(RE)分别小于440kg/hm~2和0.239。通过分析发现,施氮270kg/hm~2既能保证产量又能兼顾品质,可作为适宜施氮量。适宜施氮量下,拔节期和孕穗期小麦适宜叶面积指数分别为:3.65±0.09和5.95±0.32;适宜叶干重分别为:(1 554±168)和(2 231±130)kg/hm~2。结合CGMD-302监测模型可推算出拔节期和孕穗期适宜冠层群体的植被指数区间并应用于冠层群体诊断。     

Effects of nitrogen fertilizer rates and ratios of base and topdressing on wheat yield, soil nitrate content and nitrogen balance

Application of nitrogen (N) fertilizer is one of the most important measures that increases grain yield and improves grain quality in winter wheat (Triticum aestivum L.) production. Presently, there is a large number of investigations (experiments) in the field on different nitrogen fertilizer application regimes. However, there still exists a serious problem of low nitrogen use efficiency, especially in winter wheat high yield conditions: unsuitable nitrogen fertilizer, which often leads to lower yield and large accumulation of nitrate in the soil, bringing a potential risk to the environment. In order to explore the optimal regime of nitrogen fertilizer application suitable for environment and economy, a field experiment on the different rate and ratio of base and topdressing of nitrogen fertilizer at the different growth periods of winter wheat was conducted. The field experiment was undertaken from the fall of 2003 to the summer of 2004 in the village of Zhongcun in Longkou city, in the Shandong Province of China. The field experiment with three repeats for each treatment was designed in a split-plot. The major plot was applied with urea at a nitrogen fertilizer rate of three levels, namely, 0 kg·hm⁻² (CK), 168 kg·hm⁻² (A), and 240 kg·hm⁻² (B). In the sub-plot, the ratios of base and topdressing nitrogen fertilizer at the different development periods of wheat were 1/2:1/2 (A1 and B1), 1/3:2/3 (A2 and B2) and 0:1 (A3 and B3). Treatment B1 was under a regime used now in the local region. It was found that the amount of N accumulation in plants had no significant difference between treatments applied with nitrogen fertilizer. The grain yield and grain protein content were all elevated remarkably by applying nitrogen fertilizer compared with those of treatment CK. There was no significant difference in the grain yield and grain protein content between A2 and B2 and B3. However, when compared with those of B2 and B3, in A2 there was an increase in nitrogen use efficiency and residual soil NO₃ ⁻-N and N losses were reduced. Under the condition of the same rate of nitrogen fertilizer, increasing topdressing nitrogen rate clearly elevated the grain yield, grain protein content and nitrogen use efficiency. The results indicated that the residual soil NO₃ ⁻-N in A1 and B1 accumulated higher than that of CK in 80–160 cm soil layers at the jointing stage, but that of A2 had no significant difference compared with that of CK in 0–200 cm soil layers. At the maturity stage, more residual soil NO₃ ⁻-N was detected in B2, B3 and A3 than that in CK in 120–180 cm soil layers, which could not be absorbed by the roots of wheat, but led to be eluviated easily. The amount of soil NO₃ ⁻-N accumulation in treatment A2 had no significant difference compared with that of treatment CK in the 100–200 cm soil layer. In conclusion, A2, whose nitrogen fertilizer rate was 168 kg·hm⁻² and the ratio of base and topdressing was 1/3:2/3, had a higher grain yield and grain protein content, and heightened N use efficiency and minimized the risk of NO₃ ⁻-N leaching. This should be one of the most appropriate nitrogen fertilizer application regimes in wheat production in local regions in China. __________ Translated from Acta Ecologica Sinica, 2006, 26(11): 3661–3669 [译自: 生态学报]     

基于高光谱遥感的叶片总初级生产力GPP反演

为探究利用高光谱植被指数反演叶片总初级生产力(GPP)的模型,以湖北省武汉大学试验田油菜和小麦叶片高光谱反射率和光照强度(PARin)为数据源,利用7种植被指数与PARin的乘积分别反演2种植被叶片GPP,构建线性及非线性回归模型,并对模型进行验证。结果表明:1)从油菜生理特点出发,需要分生育期建模。在选择的7种植被指数中,花期SR构建的一次模型效果最优,建模和验模R2分别为0.80和0.82,RMSE不超过2.85g/(m~2·d);荚果期选择CIred edge和MTCI为优选模型,建模和验模R2为0.84和0.72,RMSE3.91g/(m~2·d);全时期基于红边波段的CIred edge、MTCI为优选模型,建模集R2达到0.80,RMSE3.67g/(m~2·d),验模R2达到0.65,RMSE3.92g/(m~2·d);2)小麦中NDVI模型效果最优,建模集R2=0.59,RMSE=2.80g/(m~2·d),验模R2=0.67,RMSE=3.39g/(m~2·d)。将油菜与小麦做对比,基于红边波段的植被指数CIred edge和MTCI对2种植被差异不敏感,R2为0.72~0.73,表明CIred edge和MTCI模型可以用于小麦和油菜叶片GPP的统一反演。     

Use of soil nitrogen parameters and texture for spatially-variable nitrogen fertilization

Recent studies have demonstrated the potential importance of using soil texture to modify fertilizer N recommendations. The objective of this study was to determine (i) if surface clay content can be used as an auxiliary variable for estimating spatial variability of soil NO₃–N, and (ii) if this information is useful for variable rate N fertilization of non-irrigated corn [Zea mays (L.)] in south central Texas, USA across years. A 64 ha corn field with variable soil type and N fertility level was used for this study during 2004–2007. Plant and surface and sub-surface soil samples were collected at different grid points and analyzed for yield, soil N parameters and texture. A uniform rate (UR) of 120 kg N ha⁻¹ in 2004 and variable rates (VAR) of 0, 60, 120, and 180 kg N ha⁻¹ in 2005 through 2007 were applied to different sites in the field. Distinct yield variation was observed over this time period. Yield and soil surface clay content and soil N parameters were strongly spatially structured. Corn grain yield was positively related to residual NO₃–N with depth and either negatively or positively related to clay content depending on precipitation. Residual NO₃–N to 0.60 and 0.90 m depths was more related to corn yield than from shallower depths. The relationship of clay content with soil NO₃–N was weak and not temporally stable. Yield response to N rate also varied temporally. Supply of available N with depth, soil texture and growing season precipitation determined proper N management for this field.     

3种三唑类杀菌剂对映体对镰刀菌的选择性活性

【目的】研究3种三唑类杀菌剂对映体对3种镰刀菌Fusarium spp.的杀菌活性。【方法】采用生长速率法,测定戊唑醇、腈菌唑和苯醚甲环唑各对映体对串珠镰刀菌F.moniliforme、再育镰刀菌F.proliferatum和禾谷镰刀菌F.graminearum的杀菌活性,并分析各对映体EC50值。【结果】3种三唑类手性杀菌剂对映体对供试串珠镰刀菌、再育镰刀菌和禾谷镰刀菌菌株的杀菌活性存在差异,(-)-戊唑醇(+)-戊唑醇,二者杀菌活性相差32~208倍;(+)-腈菌唑(-)-腈菌唑,二者杀菌活性相差1.4~6.4倍;在苯醚甲环唑对映体之间,(2R,4R)-苯醚甲环唑活性最高,4个对映体之间的杀菌活性相差3.7~15.5倍。【结论】3种三唑类手性杀菌剂对映体对串珠镰刀菌、再育镰刀菌和禾谷镰刀菌的杀菌活性均表现出选择性差异,(-)-戊唑醇、(+)-腈菌唑和(2R,4R)-苯醚甲环唑表现出较高的杀菌活性。本研究结果可为筛选活性高的杀菌剂、减少农药使用提供理论参考。     

不同氮肥与矿化度水平微咸水喷灌对冬小麦光合特征及产量的影响

为寻求合理的微咸水利用方式,揭示微咸水喷灌下作物生理生长响应机理,在河北低平原地区开展了冬小麦微咸水喷灌试验,研究了225 kg·hm-2施氮量条件下不同矿化度(2、3 g·L-1)微咸水灌溉和不同施氮量条件(高氮275 kg·hm-2,中氮225 kg·hm-2,低氮175 kg·hm-2)下2 g·L-1矿化度微咸水喷灌对冬小麦光合特性、生长和产量的影响.结果 表明,与淡水喷灌相比,采用矿化度3 g·L-1微咸水喷灌会导致冬小麦叶片蒸腾速率(Tr)、气孔导度(Gs)和净光合速率(Pn)显著下降,但叶片水分利用效率提高了37.0％～106.8％;2 g·L-1微咸水喷灌高氮和中氮处理的Gs、Pn与淡水喷灌无显著差异,且拔节期高氮处理的叶片Pn略高于淡水喷灌处理5.0％.2 g·L-1微咸水喷灌对冬小麦的叶面积指数和株高影响不显著,3 g·L-1矿化度微咸水喷灌对冬小麦的叶面积指数和群体密度造成不利影响.2 g·L-1微咸水喷灌3个处理冬小麦产量与淡水喷灌处理均无显著差异,但3 g·L-1矿化度处理冬小麦平均产量比淡水喷灌处理显著降低了25.9％.综上所述,矿化度不大于2 g·L-1的咸淡混合水喷灌适用于河北低平原地区冬小麦田间灌溉,添加适量氮肥会促进作物产量的增加.     

Mapping wheat nitrogen uptake from RapidEye vegetation indices

Mapping wheat nitrogen (N) uptake at 5 m spatial resolution could provide growers with new insights regarding nitrogen-use efficiency at the field scale. This study explored the use of spectral information from high resolution (5 × 5 m) RapidEye satellite data at peak leaf area index (LAI) to estimate end-of-season cumulative N uptake of wheat (Triticum spp.) in a heterogeneous, rainfed system. The primary objectives were to evaluate the usefulness of simple, widely used vegetation indices (VIs) from RapidEye as a tool to map crop N uptake over three growing seasons, farms and growing conditions, and to examine the usefulness of remotely sensed N uptake maps for precision agriculture applications. Data on harvested wheat N was collected at twelve plots over three seasons at four farms in the Palouse region of Northern Idaho and Eastern Washington. Seventeen commonly used spectral VIs were computed for images collected during ‘peak greenness’ (maximum LAI) to determine which VIs would be most appropriate for estimating wheat N uptake at harvest. The normalized difference red-edge index was the top performing VI, explaining 81 % of the variance in wheat N uptake with a regression slope of 1.06 and RMSE of 15.94 kg/ha. Model performance was strong across all farms over all three seasons regardless of crop variety, allowing the creation of high accuracy wheat N uptake maps. In conclusion, for this particular agro-ecosystem, mid-season VIs that incorporate the use of the NIR and red-edge bands are generally better predictors of end-of-season crop N uptake than VIs that do not include these bands, thereby further enabling their use in precision agriculture applications.     

Estimating total leaf nitrogen concentration in winter wheat by canopy hyperspectral data and nitrogen vertical distribution

The use of remote sensing to monitor nitrogen(N) in crops is important for obtaining both economic benefit and ecological value because it helps to improve the efficiency of fertilization and reduces the ecological and environmental burden. In this study, we model the total leaf N concentration(TLNC) in winter wheat constructed from hyperspectral data by considering the vertical N distribution(VND). The field hyperspectral data of winter wheat acquired during the 2013–2014 growing season were used to construct and validate the model. The results show that:(1) the vertical distribution law of LNC was distinct, presenting a quadratic polynomial tendency from the top layer to the bottom layer.(2) The effective layer for remote sensing detection varied at different growth stages. The entire canopy, the three upper layers, the three upper layers, and the top layer are the effective layers at the jointing stage, flag leaf stage, flowering stages, and filling stage, respectively.(3) The TLNC model considering the VND has high predicting accuracy and stability. For models based on the greenness index(GI), mND705(modified normalized difference 705), and normalized difference vegetation index(NDVI), the values for the determining coefficient(R2), and normalized root mean square error(nRMSE) are 0.61 and 8.84%, 0.59 and 8.89%, and 0.53 and 9.37%, respectively. Therefore, the LNC model with VND provides an accurate and non-destructive method to monitor N levels in the field.     

不同施氮水平和杂草清除时间对半湿润地区农田氮素利用及产量的影响

在半湿润地区中等肥力土垫旱耕人为土上进行田间试验,通过对冬小麦不同生育期杂草和作物样品的采集与分析,研究不同施肥及杂草处理对氮肥肥效的影响。结果表明:作物吸氮量随施氮量增加而增加,杂草吸氮量随吸氮量增加而降低;在不同杂草处理全生育期不清除杂草(A)、越冬前清除杂草(B)、返青期清除杂草(C)和拔节期清除杂草(D)的各个处理中,A、B、C和D杂草吸氮量分别占农田植物(作物 杂草)地上部分总吸氮量的1.98%、1.39%、3.99%和3.82%。籽粒产量随施氮量增加而增加,施氮量为135 kg N.hm-2时产量最高,达5 645.1 kg.hm-2,施氮量高于135 kg N.hm-2时,产量趋于稳定。氮肥利用率、氮肥利用效率、氮肥农学效率和氮肥生理效率均随施氮量增加而降低;从不同杂草处理看,以全生育期不清除杂草处理氮肥利用率最高,达到43.8%,返青期清除杂草处理氮肥利用率最低,为26.3%,二者间差异达显著水平;氮肥利用效率以越冬期清除杂草处理最高,为42.8 kg.kg-1N,全生育期不清除杂草处理最低,为40.6kg.kg-1N;氮肥生理效率以返青期清除杂草处理最高,为57.5 kg.kg-1N,显著高于其他杂草处理。综合氮效率及产量效率,小麦农田杂草处理时间应该相对较早,以越冬期和返青期清除杂草较好。     

Estimation of economic and environmental potentials of variable rate versus uniform N fertilizer application to spring barley on morainic soils in SE Norway

Spring barley was grown for 4 years (2001–2004) in field trials at two sites on morainic soil in central SE Norway, with five N level treatments: 0, 60, 90, 120 and 150 kg N ha^-1. Regression analyses showed that a selection of soil properties could explain 95–98% of the spatial yield variation and 47–90% of the yield responses (averaged over years). A strategy with uniform fertilizer application of 120 kg N ha⁻¹ (U _N120) was compared with two variable-rate (VR) strategies, with a maximum N rate of either 150 kg N ha⁻¹ (VR_N150) or 180 kg N ha⁻¹ (VR_N180). These strategies were tested using either Norwegian prices (low price ratio of N fertilizer to yield value; P_N/P_Y), or Swedish prices (high P_N/P_Y). The VR_N180 strategy had the highest potential yield and net revenue (yield value minus N cost) at both sites and under both price regimes. Using this strategy with Norwegian prices would increase the profit of barley cropping as long as at least 40 and 31% of the estimated potential increase in net revenue was realized, respectively. Using Swedish prices, uniform application appeared to be as good as or even better economically than the VR methods, when correcting for extra costs of VR application. The environmental effect of VR compared with uniform application, expressed as N not accounted for, showed contrasting effects when using Norwegian prices, but was clearly favourable using Swedish prices, with up to 20% reduction in the amount of N not accounted for.     

Combining chlorophyll meter readings and high spatial resolution remote sensing images for in-season site-specific nitrogen management of corn

The chlorophyll meter (CM) has been commonly used for in-season nitrogen (N) management of corn (Zea mays L.). Nevertheless, it has limited potential for site-specific N management in large fields due to difficulties in using it to generate N status maps. The objective of this study was to determine how well CM readings can be estimated using aerial hyper-spectral and simulated multi-spectral remote sensing images at different corn growth stages. Two field experiments were conducted in Minnesota, USA during 2005 involving different N application rates and timings on a corn-soybean [Glycine max (L.) Merr.] rotation field and a corn-corn rotation field. Four flights were made during the growing season using the AISA Eagle Hyper-spectral Imager and CM readings were collected at four or five different growth stages. The results indicated that single multi-spectral and hyper-spectral band or vegetation index could explain 64–86% and 73–88% of the variability in CM readings, respectively, except at growth stage V9 in the corn-soybean rotation field where no band or vegetation index could explain more than 37% of the variability in CM readings. Multiple regression analysis demonstrated that the combination of 2–4 broad-bands or 3–8 narrow-bands could explain 41–92% or 61–94% of the variability in CM readings across the two fields and different corn growth stages investigated. It was concluded that the combination of CM readings with high spatial resolution hyper-spectral or multi-spectral remote sensing images can overcome the limitations of using them individually, thus offering a practical solution to N deficiency detection and possibly in-season site-specific N management in large continuous corn fields or at later stages in corn-soybean rotation fields.     

Development of an in-season estimate of yield potential utilizing optical crop sensors and soil moisture data for winter wheat

When utilizing optical sensors to make in-season agronomic recommendations in winter wheat, one parameter often required is the in-season grain yield potential at the time of sensing. Current estimates use an estimate of biomass, such as normalized difference vegetation index (NDVI), and growing degree days (GDDs) from planting to NDVI data collection. The objective of this study was to incorporate soil moisture data to improve the ability to predict final grain yield in-season. Crop NDVI, GDDs that were adjusted based upon if there was adequate water for crop growth, and the amount of soil profile (0–0.80 m) water were incorporated into a multiple linear regression model to predict final grain yield. Twenty-two site-years of N fertility trials with in-season grain yield predictions for growth stages ranging from Feekes 3 to 10 were utilized to calibrate the model. Three models were developed: one for all soil types, one for loamy soil textured sites, and one for coarse soil textured sites. The models were validated with 11 independent site-years of NDVI and weather data. The results indicated there was no added benefit to having separate models based upon soil types. Typically, the models that included soil moisture, more accurately predicted final grain yield. Across all site years and growth stages, yield prediction estimates that included soil moisture had an R² = 0.49, while the current model without a soil moisture adjustment had an R² = 0.40.