Agricultural & Environmental Letters最新文献

In this study, an inexpensive Nix Pro (Nix Sensor Ltd.) color sensor was used to develop prediction models for soil iron (Fe) content. Thirty-eight soil samples were collected from five agricultural fields across the Animas watershed to develop and validate soil Fe prediction models. We used color space models to develop three different parameter sets for Fe prediction with Nix Pro. The different color space sets were used to develop three new predictive models for Nix Pro-based Fe content against the lab-based inductively coupled plasma analyzed Fe content. The model performances were assessed using the coefficient of determination, root mean square error, and model p-value. Three models (International Commission on Illumination's lightness, ±a axis (redness to greenness), and ± b axis (yellowness to blueness) [CIEL*a*b]; red, green, blue [RGB]; and cyan, magenta, yellow, key [black] [CMYK]) were significant in predicting the Fe content using colorimetric variables with R² ranging from 0.79 to 0.81. The mean square prediction error (MSPE) and Kling–Gupta efficiency (KGE) Index were calculated to validate models and CMYK was predicted to be a better model (MSPE = 0.13; KGE = 0.601) than CIEL*a*b and RGB models. The results suggest Nix Pro is useful in predicting soil Fe content.

Indicator of Reduction in Soils (IRIS) tubes or films are used with coatings of iron or manganese oxide to observe depth or occurrence of reducing conditions, with coating removal often assessed weekly. We evaluated the use of a rhizosphere camera to capture iron and manganese reduction (coating removal) at high temporal resolution. A rhizosphere tube was coated with iron and manganese oxide (two sections of each oxide) and inserted into a saturated column filled with a surface horizon from a wet soil (Fluvaquent). Images were taken hourly over 28 d and compared with Eh and pH data. Reducing conditions were observed for manganese and iron after 1 and 4 d, respectively. This technology builds upon an existing approach and could be used to evaluate real-time reducing soil conditions with IRIS as well as to improve oxide coating composition and tube/film development (e.g., coating thickness).

Effects of nitrogen (N) fertilization on soil organic carbon (SOC) and total N are well established, but their effects on soil acidification and emerging soil health indicators such as labile N and carbon (C) pools are not adequately documented. This research evaluated soil N and C pools and soil pH with long-term N management in continuous cotton (Gossypium hirsutum L.) production. Residual soil inorganic N, potentially mineralizable N and C, total N, SOC, pH, and electrical conductivity were measured after 17 yr of continuous N application. Comparison of five N rates (0, 56, 112, 168, and 224 kg ha^–1) showed an increase in residual inorganic N pools and decrease in pH with an increase in N application rate, while other parameters did not change significantly. Soil acidification was significant with 168 and 224 kg N ha^–1 rates. Soil pH dropped by 0.039 per kilogram increase in residual inorganic N. Optimizing N rate that minimizes residual inorganic N can reduce soil acidification.

There are a range of approaches to compare differences between or among optimum nitrogen (N) fertilizer rates resulting from different management practices; however, this goal lacks statistical standardization. To provide the statistical rigor needed to give clear recommendations for greater or less N need based on specific management practices, we propose a bootstrapping approach that resamples residuals with replacement. While bootstrapping is not new to data processing in agronomic fields, we provide an example of how to conduct residual-resampled bootstrapping with nonlinear regression to identify differences in response curves, optimum N rates, and maximum yields using the FertBoot package in R. Our example dataset provides clear evidence of the value of the bootstrapping approach, as it can aid in determining significant differences between even relatively small differences in optimum N rate. We encourage adoption of this approach as a way to accurately evaluate differences in optimum fertilizer levels between or among treatments to better inform future agronomic decision making.

The highly specialized maize (Zea mays L.) and soybean [Glycine max (L.) Merr.] production system that dominates midwestern U.S. agriculture has led to widespread on-farm and off-farm degradation of and damage to natural resources. The practice of extending maize–soybean rotations with small grains and forages has great potential to balance production and environmental goals, but adoption of these practices is low. Because little is known about farmers’ perspectives on extended rotations, we conducted social survey research with Iowa farmers to address this knowledge gap. Results show that farmers understand the potential benefits of extended rotations using small grains, but they perceive major barriers to use. The highest-rated barriers were structural, such as lack of markets. Structural barriers cannot be easily addressed by individual farmers, indicating that efforts to address negative impacts of specialized commodity production through extended rotations with small grains will require transformative changes in agri-food policies, programs, and ultimately markets.

There is a large gap between genomewide association studies (GWAS) and developing markers that can be used in marker-assisted selection (MAS) schemes for cultivar improvement. This study is a prototype for developing markers using segregating single nucleotide polymorphisms (SNPs) for panicle architecture and grain shape traits identified by GWAS in the Rice Diversity Panel-1 and colocalized in QTL regions revealed by linkage mapping in the Estrela × NSFTV199 rice (Oryza sativa L.) population. Markers were developed from sequence variants suitable for reliable detection in regions surrounding the most significant SNPs identified in GWAS. Once developed, the markers were validated in three Japonica subspecies biparental populations, used to improve QTL mapping resolution, and employed to select potential parents for use in MAS. All marker alleles segregated in the rice tropical japonica subpopulation.

Maintaining the editorial standards of a scientific journal is an important responsibility because the publications of a society are one of its major services to its members. This task can only be accomplished with the advice of a large number of colleagues who are invited to review manuscripts. Their critical comments and helpful suggestions have played a major part in making Agricultural & Environmental Letters a success. The members of the A&EL Editorial Board express their thanks to all those scientists who reviewed manuscripts in 2020. We extend our apologies and thanks to those reviewers whose names have been inadvertently omitted from this list.

Studies are lacking on the performance of napiergrass (Pennisetum purpureum Schum.) fertilized with swine (Sus scrofa domestica)-lagoon effluent. This study (2011–2013) determined biomass yield, nutrient removal, nutritive value, and ethanol yield in cultivar ‘Merkeron’ at a single, late-season harvest. Effluent irrigations provided 727 kg ha^–1 nitrogen (N) annually (3-yr average). Napiergrass removed 92% of N and 73% of phosphorus (P) applied in 2013, the peak year of production (58.9 Mg ha^–1). As compared to stems, leaves had greater (p < .01) crude protein (32 vs. 100 g kg^–1) and less acid detergent fiber (482 vs. 340 g kg^–1). Ethanol yield was approximately 36% lower in stems than leaves (98 vs. 153 g kg^–1), and xylose yield was 7% lower (170 vs 183 g kg^–1); however, stems account for a larger amount of lignocellulosic biomass for estimating bioethanol production than leaves. Ethanol yield potential was approximately 109 g kg^–1 grass biomass.

Phosphorus (P) removal structures are a new best management practice for filtering dissolved P in non-point drainage from legacy P soils through use of P sorption materials (PSMs). Structures must be designed according to characteristics of the site (hydrology and constraints) and PSMs to be utilized, as well as user-defined goals (P removal, lifetime, and flow rate), making it a cumbersome process. A freely available P Transport Reduction App (P-TRAP) allows users to quickly produce a custom design or evaluate a hypothetical or existing structure. The software includes a library of P removal flow-through curves for many different PSMs conducted under various conditions of inflow P concentration and retention time. Design output includes the necessary PSM mass and orientation, pipe requirement, and a table of annual P removal. The software enables conservationists and engineers to quickly compare cost and efficiency among possible designs.

Raindrop impact derives from the kinetic energy of falling raindrops. Determining the kinetic energy of rainfall requires the size distribution and terminal velocity of raindrops, which necessitates complex instrumentation. To avoid this, empirical relations have been developed that relate rainfall intensity and the rate of kinetic energy, i.e., time-specific kinetic energy (KE_time). In this study, a dynamic rain gauge system (DRGS) was used to quantify the KE_time generated by a rainfall simulator without need of measuring raindrop size distributions or impact velocities. In a series of 10 rainfall tests, the KE_time and rainfall intensity were 860.9 (±88.6) J m² h⁻¹ and 72.1 (±1.9) mm h⁻¹, respectively. Estimated KE_time was found to agree well with the power-law relation presented by Petrů and Kalibová for high-intensity simulated rainfall, which are the conditions when higher deviations occur. The DRGS may be a useful tool in quantifying the KE_time of rainfall simulators in hopes to better understand raindrop impact mechanisms.