A gronomy J our n al • Volume 110 , I ssue 1 • 2 018 1 T he goal of an N recommendation system is to accurately estimate the gap between the N provided by the soil and the N required by the plant. Accurately estimating this gap depends on the ability of the recommendation system to accurately estimate fi eld or subfi eld specifi c economically optimal nitrogen rates (EONR). Current recommendation systems are not as accurate as needed to provide consistently reliable estimates of N needs across years at the fi eld or subfi eld scale. Uncontrollable factors like temperature, rainfall timing, intensity and amount, and interactions of temperature and rainfall with factors such as N source, timing and placement, plant genetics, and soil characteristics combine to make N rate recommendations for an individual fi eld or rates for subfi elds a process guided as much by science as by the best professional judgement of farmers and farm advisors.Substantial evidence has accumulated that EONRs can vary widely across fi elds, within fi elds and over years in the same fi eld for a wide range of crops and geographies. Examples ABSTRACTNitrogen fi xation by the Haber-Bosch process has more than doubled the amount of fi xed N on Earth, signifi cantly infl uencing the global N cycle. Much of this fi xed N is made into N fertilizer that is used to produce nearly half of the world's food. Too much of the N fertilizer pollutes air and water when it is lost from agroecosystems through volatilization, denitrifi cation, leaching, and runoff . Most of the N fertilizer used in the United States is applied to corn (Zea mays L.), and the profi tability and environmental footprint of corn production is directly tied to N fertilizer applications. Accurately predicting the amount of N needed by corn, however, has proven to be challenging because of the eff ects of rainfall, temperature, and interactions with soil properties on the N cycle. For this reason, improving N recommendations is critical for profi table corn production and for reducing N losses to the environment. Th e objectives of this paper were to review current methods for estimating N needs of corn by: (i) reviewing fundamental background information about how N recommendations are created; (ii) evaluating the performance, strengths, and limitations of systems and tools used for making N fertilizer recommendations; (iii) discussing how adaptive management principles and methods can improve recommendations; and (iv) providing a framework for improving N fertilizer rate recommendations.
In the present study, the quality of aerated and non-aerated compost teas and bioslurry as bio-fertilizers and its application on two plant species in different substrates were tested. Compost tea brewed from a mixture residues compost presented higher nutrient content than that brewed from grape marc composted. Aeration, with shorter extraction time, resulted in higher pH, but in general with lower nutrient concentration, while bioslurry, presented higher nitrogen content. No pathogen and toxic effects were detected in the bio-products. Finally, Bio-products were evaluated in ornamental plant species: Petunia hibrida and Impatiens walleriana , where compost teas and bioslurry presented highly variable properties and effects on plant growth, depending on the substrate and species used. While in sand no signi cant effect on plant biomass and pigments were observed, in compost and commercial substrate bioslurry presented values similar to the traditional fertilizer. Compost tea presented variable results with no differences between aerated and non-aerated, both increasing carotenoids in I. walleriana in sand. We conclude that aeration showed no differences in compost tea quality, whilst bioslurry demonstrated to increase plant biomass at similar values to traditional fertilizer. Our results demonstrated that alternative products are an e cient, safe, ecological, and economical alternative to traditional products. Highlights Non-aerated and longer brewing time enhances nutrient extraction. Compost achieved from diverse residues results in a nutrient richer byproducts. Bioslurry increased plant biomass at values similar to commercial fertilizer. The Bio-products effect was highly dependent on plant species and substrate used. It is necessary, therefore, to develop and implement the use of alternative products that minimize agricultural impacts. Bio-products are by-products from biological sources, that provide nutrients, and bene cial microorganisms that promote plant growth. Usually, bio-products are derived from organic wastes that exploit the presence of nutrients and bene c microorganisms, while the residues are treated and reduced. Composting is an alternative that allows to relatively safe treat a high quantity of organic waste, while a high-value product is generated [4, 5]. It is an aerobic biological process by which biodegradable organic materials are transformed into a homogeneous product assimilable by plants [6, 7, 8]. The ltered suspensions of compost in water, denominated compost teas, are able to extract soluble nutrients and bene c microorganisms to be used as plants fertilizer or as biopesticide [9, 10]. It may be obtained from two main processes: aerated and non-aerated. The rst one is usually associated to reduced elaboration time, but it is necessary the use of aerators and energy sources available, while in non-aerated, no energy and equipment are needed, but the time required may be extended. Despite non-aerated compost tea is usually indicated as phytotoxic and to bene ts pathogen growth, no r...
A gronomy J our n al • Volume 10 0 , I s sue 3 • 2 0 0 8 ABSTRACT Active sensor refl ectance assessments of corn (Zea mays L.) canopy N status are advocated to direct variable N applications and improve N use effi ciency (NUE). Our goals were to determine: (i) growth stage and (ii) sensor vegetation index with greatest sensitivity in assessing N status and grain yield. Variable crop N was generated by supplying N at diff erent amounts and times in three fi eld studies. Chlorophyll meter (CM) and sensor data were gathered at two vegetative (V11 and V15) and two reproductive (R1 and R3) growth stages, using the Crop Circle sensor that measures refl ectance in visible (590 nm) and near infrared (NIR) (880 nm) bands. Sensor data were converted to the normalized diff erence vegetation index (NDVI 590 ) and chlorophyll index (CI 590 ) values. Grain yields were also determined. Sensor indices were more highly correlated with CM readings for vegetative vs. reproductive growth (r 2 of 0.85 vs. 0.55). Th e CM vs. CI 590 slope was over twice the NDVI 590 slope value, indicating CI 590 was more sensitive than NDVI 590 in assessing canopy greenness. Indices did not diff er in ability to distinguish yield variation. Results indicate sensor CI 590 values collected during vegetative growth are best suited to direct variable N applications. 68583. Mention of commercial products and organizations in this article is solely to provide specifi c information. It does not constitute endorsement by USDA-ARS over other products and organizations not mentioned. Th e USDA-ARS is an equal opportunity/affi rmative action employer and all agency services are available without discrimination.
Variable‐rate technology may provide a means of increasing fertilizer use efficiency by matching applications to specific conditions at a given field location. Effective implementation of this technology depends on accurately characterizing the spatial variability of soil parameters used to define the application rate. Kriging and inverse‐distance‐squared are two commonly used techniques for characterizing this spatial variability and interpolating between sampled points. To assess the accuracy of these techniques, data sets obtained from grid sampling two field research sites were used in a prediction‐validation comparison of ordinary kriging and inverse‐distance methods using powers p = 1, 2, and 4. The accuracy of the inverse‐distance methods tended to increase with the power of distance for data sets with a coefficient of variation less than about 25% (typical of soil organic matter). However, for data sets with greater variation (such as soil NO3−), inverse‐distance prediction methods using high distance powers (2 or 4) can give very inaccurate predictions. The accuracy of predictions from kriging was generally unaffected by the coefficient of variation, and was relatively high for all of the sampling configurations considered in this study. These tendencies were also observed using 48‐ and 72‐m subsamples, although the use of wider sampling spacings greatly reduced the information in the maps constructed by each method. Careful thought should be given to the choice of sample spacing and interpolation method to be used before data are collected. Summary statistics, and the coefficient of variation in particular, are simple measures that can give an indication of the relative accuracy of the inverse‐distance and kriging mapping approaches.
Sweet sorghum [SS; Sorghum bicolor (L.) Moench] is a potential biofuel crop for the Great Plains. Sweet sorghum was compared with corn [Zea mays (L.)] and grain sorghum for potential ethanol yield, energy use efficiency, and greenhouse gas (GHG) emissions at seven dryland site‐years in Nebraska. Seasonal rainfall ranged from approximately 340 to 660 mm. Soils were deep with medium texture at all site‐years. The effects of seeding rate, N rate, and cultivar on SS performance were evaluated. Sweet sorghum sugar yield was not affected by seeding rate and N application at six of seven site‐years, but yield was increased by 19% at one site‐year. Calculated ethanol yield and net energy yield were 33 and 21% more, respectively, with the grain crops compared with SS, but mean net energy yield of an earlier‐maturing SS cultivar was comparable with the grain crops. The mean ratio of energy produced in ethanol per total energy invested was 23% less for grain crops compared with SS. Mean life cycle GHG emissions were 53% and 66 to 69% less compared with gasoline for SS and grain crops, respectively. Very efficient use of the ethanol coproducts was assumed for the grain crops while SS bagasse was assumed to be returned to the field. At least one SS cultivar is competitive with grain crops for some biofuel criteria, but SS is not competitive with grain crops for total or net liquid transportation fuel produced per hectare.
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