Agricultural & Environmental Letters最新文献

In a recent commentary article, Randall Jackson (2022) claims that U.S. maize croplands currently growing cattle feed can be converted to perennial pastures without incurring either a loss of beef production or agricultural expansion. Grass-finished cattle fatten up slower and reach lower slaughter weights than grain-finished cattle (Pelletier et al., 2010). Therefore, to support present beef production using only pastures, more finishing cattle must be raised and slaughtered. The author recognizes this and attempts to quantify whether current maize production regions could instead grow sufficient perennial grasses and forages. He finds that an additional 7.6 million finishing cattle must be raised to produce exclusively grass-fed beef. He then calculates that 4.9 million ha of maize croplands growing cattle feed could, instead, grow sufficient grass to support these cattle.

However, the author makes a fundamental oversight—those 7.6 million additional finishing cattle must come from somewhere; they need mothers. Finishing cattle are supported on the “back end” by large cow-calf and stocker herds on pastures, who replace the current finishing cattle when they are slaughtered. Unlike pigs and chickens who can have many offspring per year, cows have long gestation periods of 9 mo, like humans, birthing at most one offspring each year. Cow gestation periods are so long and cattle maturity is so slow that cattle on pastures outnumber finishing cattle in feedlots by nearly five to one (Figure 1).

To raise 7.6 million more grass-finished cattle, U.S. ranchers would need to raise 7.7 million more cows, along with 7.8 million more calves and stocker cattle on pastures. Altogether, an exclusively grass-finished system requires 23.1 million (30%) more cattle to produce the same quantity of beef (Table 1), not 7.6 million (10%) more as the author models. We published these findings in a study that was cited by the author (Hayek & Garrett, 2018), but he missed this central finding.

Larger grass-finished cattle herds require additional resources. Optimistically, a maximum of 71% of current production could be met if the United States shifted its maize feed crops for finishing cattle to perennial forages (Hayek & Garrett, 2018). We assumed a similar potential forage yield on current maize croplands of 10.3 dry matter (DM) ha^–1 yr^–1, which lies within the author's range of 8–12 DM ha^–1 yr^–1. Maintaining these yields requires fertilizer inputs: we assumed forages were produced using conventional hay and alfalfa production, and the author's range of 8–12 DM ha^–1 yr^–1 is derived from a study of U.S. Upper Midwest pastures that applied fertilizer at a rate of 57 kg N ha^–1 yr^–1 (Oates et al., 2011). These findings are consistent with multiple other studies, which demonstrate that grass-f

Drone flights are often only performed during the growing season, with no data collected once harvest has been completed, although they could be used to measure winter annual weed growth. Using a drone mounted with a multispectral sensor, we flew small plot corn (Zea mays L.) fertility, cover crop, and population studies at black layer and 0–14 d after harvest (DAH). Yields had positive correlations to normalized difference vegetation index (NDVI) at black layer but often had negative correlations to corn yields 0–14 DAH. After harvest, NDVI could be associated with weed growth, and negative correlations to yield could point to reduced corn canopy allowing light to reach late-season weeds. In fertility studies, excess nitrogen appears to increase weed biomass after harvest, which can be easily identified through drone imagery. Flights should be performed after corn harvest as weed growth may provide additional insight into management decisions.

Aminopeptidases are one of the extracellular hydrolytic enzymes that catalyze organic nitrogen (N) depolymerization and are commonly assayed using fluorogenic substrates. However, chromogenic substrates based on para-nitroaniline (pNA) developed for the study of aminopeptidases in the 1960s have been underutilized. To gauge the use of pNA substrates to assay soil aminopeptidases, a systematic literature review was conducted. We identified 61 studies that were nearly all limited to measuring leucine and/or glycine aminopeptidases, despite the commercial availability of at least six other aminopeptidase-specific pNA substrates. Assay parameters of scale (slurry vs. direct incubations), matrix type, buffer pH, substrate concentration, assay duration and temperature, termination, and colorimetry indicated a lack of standardization and a confusion of pNA with pNP substrates despite important differences in abiotic hydrolysis and absorbance maxima. Future studies should systematically evaluate and standardize these parameters and assess the sensitivity of other amino acid-specific aminopeptidases to carbon (C), N, and sulfur (S) cycling.

For decades, agronomists have invested time and resources to identify the optimum nitrogen (N) rates for cereal crops. The most common method for estimating the agronomic optimum N rate (AONR) is to design a field experiment with several N fertilizer rates and fit a regression model to the yield observations. Here, we concentrate on its accuracy and precision given choices of experimental design and statistical analysis. Our first finding is that the choice of functional form has a large agronomic effect on the estimate of the AONR, and this depends on the data-generating model. Our second finding is that improving the precision and accuracy of AONR estimates will demand an increase in the number of N rates and replications. Finally, we propose that using either the best-fitting model or a weighted model is preferable to always choosing either the linear-plateau (negative bias) or quadratic-plateau (positive bias) models.

It is common to use mass-based units (e.g., kg ha^–1) to describe cover crop seeding rates. However, this convention obscures important information about seed size and resulting plant density in the field, which may be linked to cover crop performance and ecosystem services. Seed counts of 27 lots of commercially available winter rye (Secale cereale L.) spanned a wide range from 28,000 to 50,000 seeds kg^–1. If the lots with the lowest and highest seed counts were seeded at a common mass-based seeding rate of 125 kg ha^–1, it would result in a nearly twofold difference in density-based seeding rate, or 3.0 and 5.6 million live seeds ha^–1. Including density-based metrics such as live seeds per area and resulting in-field plant density in research will help advance our understanding of cover crop management, and these efforts will make it easier for farmers and policymakers to tailor cover cropping practices for specific goals.

Studies show a strong relationship between soil health and crop yield, but those relating soil health and grain quality are limited. We studied the relationship between soil health and grain protein and oil content from a corn (Zea mays L.) field in Texas. Protein and oil content data were collected in the field with a CropScan monitor. Soil health values were measured at 202 locations using the Haney Soil Health Tool. We first mapped protein and oil content using apparent electrical conductivity (ECa) and 14 terrain attributes as predictors, and we then quantified the relationship with data from sample locations. Soil health was positively correlated with protein and oil content, but the relationship was rather weak. Soil health accounted for up to 13% of the variability in protein (p < .001) and between 2 and 17% in oil content (p < .1) depending on soil map unit. Their spatial distribution was mostly influenced by elevation, ECa, and wetness index. We do not recommend estimating grain protein and oil content with the Haney Soil Health Tool; however, we suggest investigating such relationship across different soil and agronomic conditions for further verification.

The decline in groundwater supply in the Texas High Plains is forcing some growers to convert center-pivot irrigated cropland to dryland production. Transitioning toward reduced water input can lead to declines in soil health. We assessed short-term changes in soil health indicators in two transition scenarios: (a) from high irrigation method to low irrigation method (center pivot to subsurface drip) and (b) from high irrigation method to dryland (center pivot to dryland), in comparison to continuous center-pivot management. We monitored changes in chemical and biological indicators in four fields for each transition scenario and in three pivot-irrigated fields. There were declines in soil water content, potassium (K), sodium (Na), and soil organic carbon with transition from irrigation to reduced irrigation and dryland. Severe drought in the final year revealed reduced amounts of multi-enzyme activities, total ester-linked fatty acid methyl ester (EL-FAME), and total fungi. Transitioning to low water-input management in this environment complicates efforts to maintain microbial components of soil health. Longer-term comparisons are needed to detect slow changes in soil health indicators on producers’ fields.

Glanded (Gd) cottonseed (Gossypium hirsutum L.) contains scattered gossypol glands. Glandless (Gl) cottonseed is a new type of seed containing only trace levels of gossypol. This work quantitatively compared the content and migration pattern of Gd and Gl protein isolates. Both protein samples were subjected to sodium dodecyl sulfate (SDS)-gel electrophoresis, and the protein gel bands were separated into seven partitions for peptide mass spectroscopic analysis. While multiple peptide fragments (isoformers) of vicilin and legumin proteins were present in both samples, the percentage of vicilins in total seed protein was higher in Gd (74.9%) than in Gl (63.4%). In contrast, legumin proteins were more abundant in Gl (30.4%) than Gd (23.6%). Minor protein components such as lipid-related oleosins and vicilin-like antimicrobial peptides 2-2 were also observed at a relatively higher incidence in Gl compared with Gd, potentially reflecting a need for increased protein-related defense capability in the absence of gossypol against natural predators or adverse growth environment.

This study determined the effect of hybrid breeding on cultivar diversity in rice (Oryza sativa L.) production in China. The results showed that hybrid breeding led to increases in the Shannon index of cultivar diversity by 29–184% during the period 2011–2015 compared with the period 1986–1990 for 10 major hybrid rice-producing provinces in China. There was a significant exponential relationship between the Shannon index of cultivar diversity and the number of hybrid cultivars and the total number of cultivars across the 10 provinces and the two 5-yr periods. The results of this study also demonstrate that hybrid rice breeding resulted in a cultivar diversity that came close to saturation in some provinces, such as Anhui, Hunan, Jiangxi, and Sichuan, and highlight the urgent need for a reconsideration of the development of hybrid rice industrialization in China to avoid wasting resources caused by overbreeding.

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 Agricultural & Environmental Letters Editorial Board express their thanks to all those scientists who reviewed manuscripts in 2021. We extend our apologies and thanks to those reviewers whose names have been inadvertently omitted from this list.

Adeli, Ardeshir, USDA, United States

Akula, Umakanth, ICAR-Indian Institute of Millets Research, Hyderabad, India

Archer, David, USDA-ARS-NGPRL, Mandan, North Dakota, United States

Arzani, Ahmad, Isfahan University of Technology, Isfahan, Islamic Republic of Iran

Barcellos, Diego

Barnes, Ed, Cotton Inc., Cary, North Carolina, United States

Berti, Marisol, North Dakota State University, Fargo, North Dakota, United States

Bir, Courtney, Oklahoma State University System, Stillwater, Oklahoma, United States

Buda, Anthony, USDA-ARS, University Park, Pennsylvania, United States

Chatterjee, Amitava, Oxford, Mississippi, United States

Culman, Steven, Ohio Agricultural Research and Development Center, Wooster, Ohio, United States

Daigh, Aaron, North Dakota State University, Fargo, North Dakota, United States

De Guzman, Christian, University of Arkansas System, Stuttgart, Arkansas, United States

Delhom, Chris, USDA-ARS Mid South Area, United States

Dick, Warren, The Ohio State University, Wooster, Ohio, United States

Dorau, Kristof, Universität zu Köln

Eagle, Alison, Environmental Defense Fund, Raleigh, North Carolina, United States

Feleke, Shiferaw, International Institute of Tropical Agriculture, Dar es Salaam, United Republic of Tanzania

Franzluebbers, Alan, USDA, Raleigh, North Carolina, United States

Ganie, Zahoor

Goos, R., NDSU, Fargo, North Dakota, United States

Govindasamy, Prabhu, Indian Grassland and Fodder Research Institute, Jhansi, India

Graham, Jennifer, US Geological Survey Northeast Region, United States

Grusak, Mike, USDA-ARS Plains Area, Fargo, North Dakota, United States

Guillen-Portal, Fernando, Texas A and M University College Station, College Station, Texas, United States

Haden, Ryan, The Ohio State University, Wooster, Ohio, United States

Hadrich, Joleen, University of Minnesota, St. Paul, Minnesota, United States

Hall, Clifford

He, Zhongqi, USDA-ARS, New Orleans, Louisiana, United States

Houser, Matthew, Indiana University System, Bloomington, Indiana, United States

Huggins, Trevis, USDA-ARS Southeast Area, Stuttgart, Arkansas, United States