Recent breakthroughs in remote‐sensing technology have led to the development of high spectral resolution imaging sensors for observation of earth surface features. This research was conducted to evaluate the effects of organic matter content and composition on narrow‐band soil reflectance across the visible and reflective infrared spectral ranges. Organic matter from four Indiana agricultural soils, ranging in organic C content from 0.99 to 1.72%, was extracted, fractionated, and purified. Six components of each soil were isolated and prepared for spectral analysis. Reflectance was measured in 210 narrow (10‐nm) bands in the 400‐ to 2500‐nm wavelength range. Statistical analysis of reflectance values indicated the potential of high dimensional reflectance data in specific visible, near‐infrared, and middle‐infrared bands to provide information about soil organic C content, but not organic matter composition. Although reflectance in the visible bands (425–695 nm) had the highest correlation (r = −0.991 or better) with organic C content among the soils having the same parent material, these bands also responded significantly to Fe‐ and Mn‐oxide content. For soils formed on different parent materials, five long, middle‐infrared bands (1955–1965, 2215, 2265, 2285–2295, and 2315–2495 nm) gave the best correlation (r = −0.964 or better) with organic C content. Several wavebands were identified in which the soils were separable, but the reflectance response was dominated by soil factors other than organic matter content, indicating that choice of wavebands should not be based on spectral curve separability alone.

An understanding of plant response to row spacing and plant density is important in developing effective production systems for new crops. Optimum row spacing and plant population for grain amaranth (Amaranthus spp.) production in the northern Great Plains was evaluated at Prosper and Williston, ND, over 6 station‐years. Amaranth cultivars K283, K343, K432, and MT‐3 were established at populations of 74 000, 173 000, and 272 000 plants ha−1 in 30‐ and 76‐cm row spacings. Grain and biomass yield, plant height, harvest index, harvested plant population, and plant lodging were measured. Grain yields were similar among plant populations at each of the drier environments, averaging 1050 and 410 kg ha−1 for Prosper in 1989 and Williston in 1990, respectively. A 12% yield advantage, 160 kg ha−1, was observed at the lowest compared with the highest plant population at Prosper in 1990, but not in 1992. The main effect of row spacing on grain yield was not significant; however, the interaction of row spacing, plant population, and environment indicated population yield ranking differences at the 30‐cm row spacing among environments but not at the 76‐cm row spacing. The two A. cruentus L. cultivars, K283 and MT‐3, generally produced more grain than the two A. hypochondriacus L. × A. hybridus L. cultivars, K343 and K432, especially in dry environments. When considering yield, plant mortality, and potential harvest difficulties, the moderate population (173000 plants ha−1), 76‐cm row spacing, and generally higher‐yielding A. cruentus cultivars would be recommended.

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An understanding of water use is essential for evaluating the potential of new crops in areas where water is a limiting factor. This study was conducted to determine water use efficiency (WUE), depth of soil water extraction, and other agronomic characters of grain amaranth (Amaranthus spp.) produced in the northern Great Plains. Field experiments were conducted with four grain amaranth cultivars at Prosper, ND, during the 1989 through 1992 growing seasons. Volumetric soil water content was monitored with a neutron probe at eight soil profile depths during each growing season. Significant differences among cultivars were observed for biomass yield, biomass WUE, plant height, and harvest index (HI). The year × cultivar interaction was significant for grain yield, grain WUE, plant height, and HI. Maximum effective depth of soil water extraction was 122 cm in the less water‐stressed years, 1990 and 1992, and 154 cm in the more water‐stressed years, 1989 and 1991. Cultivars did not differ significantly for depth of soil water extraction or total water use (TWU). Maximum effective rooting depth occurred at early to full anthesis in 1989, 1990, and 1991 and at the late anthesis to grain fill stages in 1992. Approximately 70 to 75% of TWU occurred by the end of anthesis. Mean TWU and grain WUE values were 267 mm and 5.9 kg ha−1 mm−1, respectively. Amaranth's apparent ability to respond to water stress by increasing rooting depth makes it a potentially useful crop in North Dakota where soil moisture conditions vary considerably among growing seasons.

New instruments currently in design for Earth observational remote sensing from space offer promising advancements in the use of remote sensing for soil studies. Due to the enormous quantities of data generated by these high spectral resolution sensors, efficient use will only be feasible if new algorithms are developed to compress the data while maintaining information content. A spectral band selection algorithm is proposed to identify the important spectral bands for compression and classification of soil reflectance data. This algorithm was developed from the shape dominancy concept of Karhunen‐Loeve based optimal features. The high dimensional raw data were first transformed into much lower dimensionality by the derived spectral features. Canonical analysis was then used to transform the data under the maximal separability criterion into their final signal space where the data classification was performed. Soil data sets with and without stratification by soil order and climatic moisture zone were used to test the algorithm. The probabilities of correct classification of soil organic matter content using Landsat Multispectral Scanner (MSS) bands, Thematic Mapper (TM) bands, and the bands identified using the new band selection algorithm, were calculated and compared. The algorithm was successful in finding important spectral bands for soil organic matter content classification. The probabilities of correct classification obtained by using these bands were 0.850 and 0.883 for unstratified data sets, as compared to 0.600 to 0.640 for MSS bands and 0.640 to 0.653 for TM bands. Probabilities of correct classification for climate‐stratified data ranged from 0.910 to 0.980 using the calculated bands. The overall data compression ratio achieved using the algorithm was greater than 10 with no loss in classification accuracy.