Corn requires the most nitrogen (N) of cereal grain crops and N supply is correlated with grain yield.  Canopy reflectance has been used to assess crop N needs and to derive optimum application rates in mid-season corn.  Canopy reflectance has not been useful for N rate determination in early season corn because of low biomass and the sensing background can interfere, or overwhelm crop canopy reflectance measures.  Widespread adoption of canopy reflectance as a basis for generating in-season corn N rates would be more likely if N rate recommendations could be made early, i.e. by the V6 growth stage.  The objectives of this research were to: i) examine the influence of soil color, soil moisture, surface crop residues, and sensor orientation on normalized difference vegetation index (NDVI) readings from corn from planting through the V6 growth stage; and ii) evaluate the effect of sensor orientation and field of view at early corn growth stages on the relationship between NDVI and corn biomass, N uptake, and chlorophyll meter readings.  Soil color, soil moisture, crop residue type, and sensor orientation influenced reflectance and these factors were much more influential when sensing plants with low biomass.  Canopy reflectance was capable of differentiating between N rates in the field and altering sensor orientation did not minimize sensing background influence or improve the ability of the sensor to distinguish plant N status.  Even when canopy reflectance detected differences in crop N status, N rate prescription based on NDVI was consistently below the profitable estimated sidedress N rate. / Master of Science

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The site-specific management of alfalfa has not been well-evaluated, despite the economic importance of this crop. The objectives of this work were to i) characterize the effects of soil moisture deficits on alfalfa and alfalfa yield components and ii) evaluate the use of canopy reflectance patterns in measuring treatment-induced differences in alfalfa yield. A randomized complete block design with five replicates of subsurface drip irrigation (SDI) and rainfed treatments of alfalfa was established at the University of Kentucky Animal Research Center in 2003. Potassium, as KCl, was broadcast on split-plots on 1 October 2004 at 0, 112, 336, and 448 kg K2O ha-1. In the drought year of 2005, five harvests (H1 - H5) were taken from each split-plot and from four locations within each SDI and rainfed plot. One day prior to each harvest, canopy reflectance was recorded in each plot. Alfalfa yield, yield components, and leaf area index (LAI) were determined. In 2005, dry matter yields in two harvests and for the seasonal total were increased (Pandlt;0.05) by SDI, but SDI did not affect crown density. Herbage yield was strongly associated with yield components but yields were most accurately estimated from LAI. Canopy reflectance within blue (450 nm), red (660 nm) and NIR bands were related to LAI, yield components, and yield of alfalfa and exhibited low variance (cv andlt; 15%) within narrow ( 0.125 Mg ha-1) yield ranges. Red-based Normalized Difference Vegetation Indices (NDVIs) and Wide Dynamic Range Vegetation Indices (WDRVIs) were better than blue-based VIs for the estimation of LAI, yield components, and yield. Decreasing the influence of NIR reflectance in VIs by use of a scalar (0.1, 0.05, or 0.01) expanded the range of WDRVI-alfalfa yield functions. These results indicate that VIs may be used to estimate LAI and dry matter yield of alfalfa within VI-specific boundaries.

Ground-based spectral imaging devices offer an important supplement to satellite imagery. Hand-held, ground-based sensors allow rapid, inexpensive measurements that are not affected by the earth’s atmosphere. They also provide a basis for high altitude spectral indices. We quantified the spectral reflectance characteristics of hard red spring wheat (Triticum aestivum cv. Westbred 936) in research plots subjected to either nitrogen or water stress in a two year study. Both types of stress reduced ground cover, which was evaluated by digital photography and compared with ten spectral reflectance indices. On plots with a similar soil background, simple indices such as the normalized difference vegetation index, ratio vegetation index, and difference vegetation index were equal to or superior to more complex vegetation indices for predicting ground cover. Yield was estimated by integrating the normalized difference vegetation index over the growing season. The coefficient of determination (r2) between integrated normalized difference vegetation index and final yield was 0.86. Unfortunately, none of these indices were able to differentiate between the intensity of green leaf color and ground cover fraction, and thus could not distinguish nitrogen from water stress. We developed a reflective index that can differentiate nitrogen and water stress over a wide range of ground cover. The index is based on the ratio of the green and red variants of the normalized difference vegetation index. The new index was able to distinguish nitrogen and water stress from satellite data using wavelengths less than 1000 nm. This index should be broadly applicable over a wide range of plant types and environments.

Many systems for field-based, high-throughput phenotyping (FB-HTP) quantify and characterize the reflected radiation from the crop canopy to derive phenotypes, as well as infer plant function and health status. However, given the technology's nascent status, it remains unknown how biophysical and physiological properties of the plant canopy impact downstream interpretation and application of canopy reflectance data. In that light, we assessed relationships between leaf thickness and several canopy-associated traits, including normalized difference vegetation index (NDVI), which was collected via active reflectance sensors carried on a mobile FB-HTP system, carbon isotope discrimination (CID), and chlorophyll content. To investigate the relationships among traits, two distinct cotton populations, an upland (Gossypium hirsutum L.) recombinant inbred line (RIL) population of 95 lines and a Pima (G, barbaderise L.) population composed of 25 diverse cultivars, were evaluated under contrasting irrigation regimes, water-limited (WL) and well-watered pm conditions, across 3 years. We detected four quantitative trait loci (QTL) and significant variation in both populations for leaf thickness among genotypes as well as high estimates of broad-sense heritability (on average, above 0.7 for both populations), indicating a strong genetic basis for leaf thickness. Strong phenotypic correlations (maximum r = -0.73) were observed between leaf thickness and NDVI in the Pima population, but not the RIL population. Additionally, estimated genotypic correlations within the RIL population for leaf thickness with CID, chlorophyll content, and nitrogen discrimination (r(gij) = -0.32, 0.48, and 0.40, respectively) were all significant under WW but not WL conditions. Economically important fiber quality traits did not exhibit significant phenotypic or genotypic correlations with canopy traits. Overall, our results support considering variation in leaf thickness as a potential contributing factor to variation in NDVI or other canopy traits measured via proximal sensing, and as a trait that impacts fundamental physiological responses of plants.

With the increasing scarcity of water resources, soil moisture stress is the single most threat to global soybean production causing extensive yield losses. The objectives of this study were to investigate soil moisture stress effects on all aspects of soybean growth and development processes and to develop functional algorithms that could be used for field management decisions and in soybean crop modeling. To fulfill these objectives, six experiments were conducted; one in vitro osmotic stress study on seed germination, four studies by imposing five soil moisture treatments, 100, 80, 60, 40, and 20% of daily evapotranspiration of the control at different growth stages using sunlit plant growth chambers, and one transgenerational study on seed germination at different osmotic levels and offspring growth at three irrigation treatments (100, 66, and 33% based on field capacity) for plants grown at different soil moisture levels. Two cultivars from maturity group V, Asgrow AG5332 and Progeny P5333RY, with different growth habits were used in all these studies. Midday leaf water potential, plant height, mainstem nodes, gas-exchange traits, canopy reflectance, and several yield components including pod weight, seed yield, and seed quality were measured. Soil moisture stress decreased biomass, net photosynthesis, yield, individual seed weight, maximum seed germination, protein, fatty acids, sucrose, N, and P and increased oil, stachyose, Fe, Mg, Zn, Cu, and B contents. Overall, Asgrow AG5332 was more tolerant to drought stress than Progeny P5333RY. Soil moisture stress induced changes in seed quality that were correlated with seed germination and seedling vigor in the F1 generation. These data can be used to build a model-based decision support system capable of predicting yield under field conditions.

Diss. (sammanfattning) Uppsala : Sveriges lantbruksuniv., 2007. / Härtill 4 uppsatser.