Denitrification is a critical process regulating the removal of bioavailable nitrogen (N) from natural and human-altered systems. While it has been extensively studied in terrestrial, freshwater, and marine systems, there has been limited communication among denitrification scientists working in these individual systems. Here, we compare rates of denitrification and controlling factors across a range of ecosystem types. We suggest that terrestrial, freshwater, and marine systems in which denitrification occurs can be organized along a continuum ranging from (1) those in which nitrification and denitrification are tightly coupled in space and time to (2) those in which nitrate production and denitrification are relatively decoupled. In aquatic ecosystems, N inputs influence denitrification rates whereas hydrology and geomorphology influence the proportion of N inputs that are denitrified. Relationships between denitrification and water residence time and N load are remarkably similar across lakes, river reaches, estuaries, and continental shelves. Spatially distributed global models of denitrification suggest that continental shelf sediments account for the largest portion (44%) of total global denitrification, followed by terrestrial soils (22%) and oceanic oxygen minimum zones (OMZs; 14%). Freshwater systems (groundwater, lakes, rivers) account for about 20% and estuaries 1% of total global denitrification. Denitrification of land-based N sources is distributed somewhat differently. Within watersheds, the amount of land-based N denitrified is generally highest in terrestrial soils, with progressively smaller amounts denitrified in groundwater, rivers, lakes and reservoirs, and estuaries. A number of regional exceptions to this general trend of decreasing denitrification in a downstream direction exist, including significant denitrification in continental shelves of N from terrestrial sources. Though terrestrial soils and groundwater are responsible for much denitrification at the watershed scale, per-area denitrification rates in soils and groundwater (kg Nkm � 2 � yr � 1 ) are, on average, approximately one-tenth the per-area rates of denitrification in lakes, rivers, estuaries, continental shelves, or OMZs. A number of potential approaches to increase denitrification on the landscape, and thus decrease N export to sensitive coastal systems exist. However, these have not generally been widely tested for their effectiveness at scales required to significantly reduce N export at the whole watershed scale.

/pdf/denitrification-across-landscapes-and-waterscapes-a-ei9e1947rb.pdf

Denitrification across landscapes and waterscapes: a synthesis.

Human activity and related land use change are the primary cause of accelerated soil erosion, which has substantial implications for nutrient and carbon cycling, land productivity and in turn, worldwide socio-economic conditions. Here we present an unprecedentedly high resolution (250 × 250 m) global potential soil erosion model, using a combination of remote sensing, GIS modelling and census data. We challenge the previous annual soil erosion reference values as our estimate, of 35.9 Pg yr−1 of soil eroded in 2012, is at least two times lower. Moreover, we estimate the spatial and temporal effects of land use change between 2001 and 2012 and the potential offset of the global application of conservation practices. Our findings indicate a potential overall increase in global soil erosion driven by cropland expansion. The greatest increases are predicted to occur in Sub-Saharan Africa, South America and Southeast Asia. The least developed economies have been found to experience the highest estimates of soil erosion rates.

/pdf/an-assessment-of-the-global-impact-of-21st-century-land-use-rdg7ojdzqi.pdf

An assessment of the global impact of 21st century land use change on soil erosion.

Recent advances in spectral-spatial classification of hyperspectral images are presented in this paper. Several techniques are investigated for combining both spatial and spectral information. Spatial information is extracted at the object (set of pixels) level rather than at the conventional pixel level. Mathematical morphology is first used to derive the morphological profile of the image, which includes characteristics about the size, orientation, and contrast of the spatial structures present in the image. Then, the morphological neighborhood is defined and used to derive additional features for classification. Classification is performed with support vector machines (SVMs) using the available spectral information and the extracted spatial information. Spatial postprocessing is next investigated to build more homogeneous and spatially consistent thematic maps. To that end, three presegmentation techniques are applied to define regions that are used to regularize the preliminary pixel-wise thematic map. Finally, a multiple-classifier (MC) system is defined to produce relevant markers that are exploited to segment the hyperspectral image with the minimum spanning forest algorithm. Experimental results conducted on three real hyperspectral images with different spatial and spectral resolutions and corresponding to various contexts are presented. They highlight the importance of spectral-spatial strategies for the accurate classification of hyperspectral images and validate the proposed methods.

/pdf/advances-in-spectral-spatial-classification-of-hyperspectral-vlt2ik4ef1.pdf

Advances in Spectral-Spatial Classification of Hyperspectral Images

The integration of cover crops into cropping systems brings costs and benefits, both internal and external to the farm. Benefits include promoting pest-suppression, soil and water quality, nutrient cycling efficiency, and cash crop productivity. Costs of adopting cover crops include increased direct costs, potentially reduced income if cover crops interfere with other attractive crops, slow soil warming, difficulties in predicting N mineralization, and production expenses. Cover crop benefits tend to be higher in irrigated systems. The literature is reviewed here along with Michigan farmer experience to evaluate promising cover crop species for four niches: Northern winter (USDA Hardiness Zones 5-6), Northern summer (Zones 5-6), Southern winter (Zones 7-8), and Southern summer (Zones 7-8). Warm season C 4 grasses are outstanding performers for summer niches (6-9 Mg ha -1 ), and rye (Secale cereale L.) is the most promising for winter niches (0.8-6 Mg ha -1 ) across all hardiness zones reviewed. Legume-cereal mixtures such as sudangrass (Sorghum sudanese L.)-cowpea (Vigna unguiculata L.) and wheat (Triticum aestivum L.)-red clover (Trifolium pretense L.) are the most effective means to produce substantial amounts (28 Mg ha -1 ) of mixed quality residues. Legume covers are slow growers and expensive to establish. At the same time, legumes fix N, produce high quality but limited amounts (0.5-4 Mg ha -1 ) of residues, and enhance beneficial insect habitat. Brassica species produce glucosinolate-containing residues (2-6 Mg ha -1 ) and suppress plant-parasitic nematodes and soil-borne disease. Legume cover crops are the most reliable means to enhance cash crop yields compared with fallows or other cover crop species. However, farmer goals and circumstances must be considered. If soil pests are a major yield limiting factor in cash crop production, then use of brassica cover crops should be considered. Cereal cover crops produce the largest amount of biomass and should be considered when the goal is to rapidly build soil organic matter. Legume-cereal or brassica-cereal mixtures show promise over a wide range of niches.

Evaluating Cover Crops for Benefits, Costs and Performance within Cropping System Niches

The adoption of agricultural innovations: A review

Conservation tillage systems such as no-till with winter rye cover cropping change soil chemical properties, which affect crop growth and the environment The objectives of this study were to investigate the effect of no-till and mulch-till systems, surface application of poultry litter, and winter rye (Secale cereale L) cover crop on soil pH, soil organic matter (SOM), and N and P concentrations in cotton (Gossypium hirsutum L) plots The study was done on a Decatur silt loam in north Alabama from 1996 to 1998 SOM under no-till and mulch-till systems in the 0–15 cm soil depth in November 1998 was 22 g kg−1 (P

Tillage, cover cropping, and poultry litter effects on selected soil chemical properties

Despite being one of the most profitable crops for the southeastern USA, cotton (Gossypium hirsutum L.) is considered to create a greater soil erosion hazard than other annual crops such as corn (Zea mays L.) and soybeans (Glycine max (L.) Merr.). Reduced tillage systems and cover cropping can reduce soil erosion and leaching of nutrients into ground water. The objectives of this study, which was conducted in north Alabama from 1996 to 1998, were to assess the impact of no-till and mulch-till systems with a winter rye (Secale cereale L.) cover crop and poultry litter on soil erosion estimates in cotton plots using the revised universal soil loss equation (RUSLE). Soil erosion estimates in conventional till plots with or without a winter rye cover crop and ammonium nitrate fertilizer were double the 11 t ha ˇ1 yr ˇ1 tolerance level for the Decatur series soils. However, using poultry litter as the N source (100 kg N ha ˇ1 ) gave soil erosion estimates about 50% below the tolerance level under conventional till. Doubling the N rate through poultry litter to 200 kg N ha ˇ1 under no-till system gave the lowest soil erosion estimate level. No-till and mulch-till gave erosion estimates which were about 50% of the tolerance level with or without cover cropping or N fertilization. This study shows that no-till and mulch-till systems with cover cropping and poultry litter can reduce soil erosion in addition to increasing cotton growth and lint yields, and thus improve sustainability of cotton soils in the southeastern USA. # 2001 Published by Elsevier Science B.V.

/pdf/predicting-soil-erosion-in-conservation-tillage-cotton-fveg4vrj9a.pdf

Predicting soil erosion in conservation tillage cotton production systems using the revised universal soil loss equation (RUSLE)

Modern agriculture uses large amounts of organic and inorganic nutrients to optimize productivity. Excessive nutrient applications sometime lead to adverse effects on the environment and human health. Precision agriculture is evolving with the objectives of minimizing these adverse effects by enabling farmers to manage nutrient applications more efficiently while sustaining precious environmental resources. To develop a method that uses nutrients more efficiently on cotton, a field experiment involving three sources and three rates of nitrogen with and without nitrification inhibitor was carried out in four replications at Belle Mina, AL during the 1994-97 crop seasons. In 1997, these plots were used to determine if there was a relationship between remotely sensed hyperspectral reflectance data and three field measurements that included cotton leaf chlorophyll (defined as measurements of five leaves using a Minolta Chlorophyll SPAD Meter to represent cotton canopy), soil nitrate-nitrogen, and cot...

/pdf/relationship-between-hyperspectral-reflectance-soil-nitrate-1kmxkf4hxy.pdf

K. C. Reddy

Papers

Tillage, cover cropping, and poultry litter effects on selected soil chemical properties

Predicting soil erosion in conservation tillage cotton production systems using the revised universal soil loss equation (RUSLE)

Relationship between hyperspectral reflectance, soil nitrate-nitrogen, cotton leaf chlorophyll, and cotton yield: A step toward precision agriculture

Pearl millet and cowpea yields in sole and intercrop systems, and their after-effects on soil and crop productivity

N2O and no emissions from poultry litter and urea applications to Bermuda grass