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Effect of dietary crude protein and forage contents on enteric methane emissions and nitrogen excretion from dairy cows simultaneously
Get PDFThe study aimed to examine, simultaneously, the effects of changing dietary forage and crude protein (CP) contents on methane (CH4) emissions and nitrogen (N) excretion from lactating dairy cows. Twelve post-peak lactating Holstein cows were randomly assigned to 4 treatments from a 2×2 factorial arrangement of two dietary forage levels [37.4% (LF) vs. 53.3% (HF) of DM] and two dietary CP levels [15.2% (LP) vs. 18.5% (HP) of DM] in a 4×4 Latin square design with four 18 d periods. Alfalfa hay was the sole source of dietary forage. Cows were fed and milked twice daily. During the first 14 d, cows were housed in a free-stall barn, where enteric CH4 emissions were measured using the GreenFeed system from d 8 to 14 in each period. Cows were then moved to metabolic cages, where total feces and urine output (kg/cow/d) were measured using total collection approach for 3 days. No dietary forage by CP interactions were detected for DMI, milk production, enteric CH4 emissions, or N excretions. Dry matter intake, milk production, and milk composition yield were increased by LF diet (P < 0.01). No difference was found between cows fed HP or LP diets, however, milk fat content increased in cows fed HP (P < 0.05). Enteric CH4 emissions, and CH4 per unit of DMI, ECM, total digested OM and NDF were not affected by dietary CP, but decreased by LF compared to HF (P < 0.01). Milk true protein N was not affected by dietary CP content but was higher for LF compared to HF. Greater dietary N was partitioned to true milk protein in cows fed LF compared to HF diet (P < 0.01). Urinary N excretion was greater in cows fed HP (P < 0.01), and lowest in cows fed LF diet (P < 0.01). Neither dietary CP nor forage content affected fecal N. Total N excretion (urinary plus fecal) did not differ between HP and LP, but tended to be lower in cows fed LF compared to HF diet (P = 0.09). Both milk urea N (P < 0.01) and blood urea N (P < 0.01) declined with decreasing dietary CP or forage contents. Based on purine derivative analysis, microbial protein synthesis in the rumen tended to be lower for high forage and low protein treatments (P < 0.09). Increasing dietary forage contents resulted in greater CH4 emission (g/kg of ECM) and manure N excretion (g/kg of ECM) intensities of lactating dairy cows. Cows receiving reduced CP diets had low manure N outputs and improved milk true protein production efficiencies, regardless of dietary forage content
Length of efficacy for control of curly top in sugar beet with seed foliar insecticides
Get PDFCurly top in sugar beet caused by Beet curly top virus (BCTV) is an important yield limiting disease that can be reduced via neonicotinoid and pyrethroid insecticides. However the length of efficacy of these insecticides is poorly understood, so a series of field experiments was conducted with the seed treatment Poncho Beta (60 g a.i. clothianidin + 8 g a.i. beta-cyfluthrin/100,000 seed) and foliar treatment Asana (55.48 g a.i. esfenvalerate/ha). A series of four experiments were conducted in 2014 and repeated in 2015 with four treatments (untreated check, Poncho Beta, Asana, and Poncho Beta + Asana) which were arranged in a randomized complete block design with 8 replications. To evaluate efficacy, viruliferous (contain BCTV strains) beet leafhoppers were released at different times for each experiment – either 8, 9, 10, or 11weeks after planting (1 to 4 weeks after Asana application). Over both years, in 30 of 32 comparisons for treatments with Poncho Beta and 14 of 16 comparisons for Asana, visual curly top ratings were decreased an average of 41% and 24%, respectively compared to the untreated check. Over both years, in 8 of 8 comparisons for treatments with Poncho Beta and 6 of 8 comparisons for Asana, root yields were increased an average of 39% and 32%, respectively compared to the untreated check. When considering estimated recoverable sucrose (ERS) over both years, the Poncho Beta treatments increased yield by 75% compared to the untreated check over the first two weeks. By week 10 only the Poncho Beta + Asana treatment consistently led to increases in ERS, while the influence of increasing host resistance may have made other treatments more difficult to separate. When considering all variables among all weeks and years, there was a tendency for the insecticides in the Poncho Beta + Asana treatment to complement each other to improve efficacy
Beet curly top resistance in USDA-ARS Kimberly germplasm lines, 2015
Get PDFCurly top caused by Beet curly top virus is a widespread disease problem vectored by the beet leafhopper in semiarid sugar beet production areas. Host resistance is the primary defense against this problem, but resistance in commercial cultivars is only low to intermediate. In order to identify novel sources of curly top resistance, 14 sugar beet lines were screened in a disease nursery in 2015. The lines were arranged in a randomized complete block design with six replications. A curly top epiphytotic was created by releasing six viruliferous beet leafhoppers per plant at the four-to six-leaf growth stage on 24 Jun. Foliar symptoms were evaluated on 13 and 20 Jul using a scale of 0-9 (0 = healthy and 9 = dead) in a continuous manner. Curly top symptom development was uniform and no other disease problems were evident in the plot area. The disease pressure in the test was moderately severe with good symptom development in the susceptible check. Based on the overall visual rating, KDH13 and KDH4-9 performed the same as the resistant check and were better than all other entries. Additionally, ELISA data also indicated that these two lines had significantly lower virus titer than all other entries including the resistant check. These germplasm lines will be released to the general public, so they can be utilized to improve resistance in commercial cultivars
Experimental sugar beet cultivars evaluated for rhizomania resistance and storability in Idaho, 2015
Get PDFRhizomania caused by Beet necrotic yellow vein virus (BNYVV) and storage losses are serious sugar beet production problems. To identify sugar beet cultivars with resistance to BNYVV and evaluate storability, 32 commercial cultivars were screened by growing them in a sugar beet field infested with BNYVV in Kimberly, ID during the 2015 growing season in a randomized complete block design with 6 replications. At harvest on 28-29 September 2015, roots were dug and evaluated for symptoms of rhizomania and also placed in an indoor commercial sugar beet storage building. After 136 days in storage, samples were evaluated for surface rot, weight loss, and sucrose loss. Surface root rot ranged from 8 to 81%, weight loss ranged from 8.0 to 21.0%, sucrose losses ranged from 25 to 89%, and estimated recoverable sucrose ranged from 439 to 8,261 lb/A. Given these response ranges, selecting cultivars for rhizomania resistance and combining this resistance with storability will lead to considerable economic benefit for the sugar beet industry
Evaluation of neural network modeling to predict non-water-stressed leaf temperature in wine grape for calculation of crop water stress index
Get PDFPrecision irrigation management in wine grape production is hindered by the lack of a reliable method to easily quantify and monitor vine water status. Mild to moderate water stress is desirable in wine grape for controlling vine vigor and optimizing fruit yield and quality. A crop water stress index (CWSI) that effectively monitors plant water status has not been widely adopted in wine grape because of the need to measure well-watered and non-transpiring leaf temperature under identical environmental conditions. In this study, we calculated a daily CWSI for the wine grape cultivars Syrah and Malbec (Vitis vinifera L.) by estimating well-watered leaf temperature with an artificial neural network (NN) model and non-transpiring leaf temperature based on the cumulative probability of the measured difference between ambient air and deficit-irrigated grapevine leaf temperature. We evaluated the reliability of this methodology by comparing the calculated CWSI to midday leaf water potential and irrigation amount in replicated plots of above ground, drip-irrigated vines provided with 30, 70 or 100% of their estimated evapotranspiration demand under warm, semiarid field conditions in southwestern Idaho USA. Infrared and environmental sensors were used to monitor leaf temperature and weather conditions throughout berry development. The input variables for the NN model with lowest error were 15-minute average values for air temperature, relative humidity, solar radiation and wind speed collected between 13:00 and 15:00 MDT. A feed-forward perceptron NN model was developed for each cultivar because of their difference in well-watered leaf temperature. Predicted and measured well-watered leaf temperature had correlation coefficients of 0.91 and 0.86 for ‘Syrah’ and ‘Malbec’, respectively. Non-transpiring leaf temperature for both cultivars was air temperature plus 15 degrees Celsius. The daily CWSI consistently differentiated between deficit irrigation amounts, irrigation events, and rainfall and explained between 51 and 70% of the variability in midday leaf water potential. The methodology used to calculate a daily CWSI for wine grape in this study provided a real-time indicator of vine water status that could be automated for use as a decision-support tool in a precision irrigation system
Dairy manure applications and soil health implications
Get PDFDairy manure applications can potentially improve soil health by adding organic matter (OM) to the soil. However, intensive dairy manure applications can cause salt accumulations on arid, irrigated soils, impairing soil health, which can reduce crop growth and yield. Soil organic matter, a major contributor to soil health, increased by 0.02% for every ton of manure-derived organic matter applied. While soil OM increases typically improve soil health, salt accumulations from manure applications had antagonistic effects on soil health. As manure application rates and frequencies increased, soil properties became increasingly saline-sodic, as indicated by elevated EC and SAR values. Aggregate stability also decreased at the heaviest annual manure application rate, likely a consequence of clay dispersion caused by sodium in the added manure. One concern with some of the new soil health tests is that they do not account for the negative effects of salt accumulations. For example, the Haney “soil health score” increased with increasing organic matter and nutrient content, not taking into account the fact that EC and SAR increased to levels above the recommended salinity/sodicity thresholds for salt sensitive and even salt tolerant crops. The decline in aggregate stability also revealed that soil structure was compromised at the higher soil health scores. A newly proposed N mineralization estimation procedure is the “Haney – Additional N” test, which relates carbon dioxide respiration measurements to soil organic C to organic N ratios to estimate how much N will be available to plants in addition to the nitrate and ammonium traditionally measured in preplant soil tests. Unfortunately, the “Haney – Additional N” test severely under-predicted mineralizable N pools by 20-fold. Thus, use of this test for estimating N fertilizer applications would drastically under-estimate plant available N in the soil, which would lead to greatly over-estimating the N requirement of a crop to be grown on that field. Including salinity and sodicity parameters should be considered in future soil health evaluation programs, especially in semi-arid irrigated regions like southern Idaho where saline and sodic soil conditions can occur
Antibiotics in agroecosystems: Introduction to the special section
Get PDFThe presence of antibiotic drug residues, antibiotic resistant bacteria, and antibiotic resistance genes in agroecosystems has become a significant area of research in recent years, and is a growing public health concern. While antibiotics are utilized for human medicine and agricultural practices, the majority of antibiotic use occurs in food animals where these drugs have historically been used for growth promotion, in addition to prevention and treatment of disease. The widespread use of antibiotics combined with the application of human and animal wastes to agricultural fields introduces antibiotic-related contamination into the environment. While overt toxicity in organisms directly exposed to antibiotic in agroecosystems is generally not an issue due to concentrations generally lower than therapeutic doses, the impacts of introducing antibiotic contaminants are unknown, and concerns have arisen about the health of humans, animals and ecosystems (One Health). Despite increases in research focused on the fate and occurrence of antibiotics and antibiotic resistance over the past decade, standard methodologies and practices for analyzing environmental samples are limited, and future research needs are becoming evident. To address these issues in detail, this special section was developed with a framework of five core review papers that address the (i) overall state of science of antibiotics and antibiotic resistance in agroecosystems with a causal model; (ii) chemical analysis of antibiotics in the environment; (iii) necessity for background and baseline data for studies of antibiotic resistance in agroecosystems with a decision-making tool to assist in designing research studies; as well as (iv) culture- and (v) molecular-based methods for analyzing antibiotic resistance in the environment. With a focus on the core review papers, this introduction to the special section summarizes the current state of science for analyzing antibiotics and antibiotic resistance in agroecosystems, while also discussing current knowledge gaps and future research priorities. This introduction also contains a glossary of terminologies that are commonly used throughout the special section. By defining these terminologies, it is hoped to provide a common language that clearly defines the linkages across the narratives of each paper
Leuconostoc spp. associated with root rot in sugar beet and their interaction with rhizoctonia solani
Get PDFRhizoctonia root and crown is an important disease problem in sugar beet caused by Rhizoctonia solani and also shown to be associated with Leuconostoc. Since, the initial Leuconostoc studies were conducted with only a few isolates and the relationship of Leuconostoc with R. solani is poorly understood, a more thorough investigation was conducted. A total of 203 Leuconostoc isolates were collected from recently harvested sugar beet roots in southern Idaho and southeastern Oregon during 2010 and 2012: 88 and 85% L. mesenteroides, 6 and 15% L. pseudomesenteroides, 2 and 0% L. kimchi, and 4 and 0% unrecognized Leuconostoc sp., respectively. Based on 16S rRNA sequencing, haplotype 11 (L. mesenteroides isolates) comprised 68 to 70% of the isolates both years. In pathogenicity field studies with commercial sugar beet cultivar B-7, all Leuconostoc isolates had more rot (P <0.0001; alpha = 0.05) when combined with R. solani than when inoculated alone both years. Also, 46 of the 52 combination treatments over the two years, had significantly more rot (P <0.0001; alpha = 0.05) than the fungal check. Therefore, the data support that a synergistic interaction leads to more rot when both Leconostoc and R. solani are present in sugar beet roots
Winter and growing season nitrogen mineralization from fall-applied composted or stockpiled solid dairy manure
Get PDFAdequate characterization of nitrogen (N) mineralization with time from manure and other organic sources is needed to maximize manure N use efficiency, decrease producer costs, and protect groundwater quality. The objective of our two-year field study at Parma, ID, was to quantify in situ N mineralization with time as affected by a one-time fall application of solid dairy manure, either composted or stockpiled. The experiment included five treatments: a non-N fertilized control, two first-year rates of stockpiled solid dairy manure (21.9 and 43.8 Mg/ha, dry wt.) and two rates (53.1 and 106.1 Mg/ha, dry wt.) of composted dairy manure (hereafter termed compost). Net N mineralization (mineralization less immobilization) was determined to a depth of 0.3 m by repeatedly measuring soil inorganic N (ammonium-N + nitrate-N) concentrations in buried polyethylene bags. Overwinter mineralization was measured between amendment incorporation in fall and sugarbeet (Beta vulgaris L.) planting the following spring. In-season mineralization was measured in situ for seven consecutive incubation periods during the c. 220-d growing season for furrow-irrigated sugarbeet. Net N mineralization often varied among amendments and from year to year through mid-season, likely due to seasonal variation in temperature, annual differences in amendment properties, and other factors. In early spring 2003 after a warmer-than-normal winter, immobilization exceeded mineralization, regardless of treatment. In-season net N mineralization peaked between mid-August and early September (DOYs 230 to 251) each year, regardless of treatment. Annual (c. 11-mo) net N mineralization in 2003 averaged 52 kg N/ha, similar among treatments. In 2004, annual net N mineralization was similar between rates within amendments and averaged 250 kg N/ha where manure treated, 150 kg N/ha where compost treated, and 106 kg N/ha where untreated. On average in 2004, 31% of compost’s annual net N mineralization occurred before the growing season and 69% during the season while essentially all of manure’s net mineralization occurred during the season. None of the amendments’ total N was, in net, mineralized in 2003 but in 2004 on average, 2% of compost’s and 16% of manure’s total N was mineralized, similar between rates within amendments. When estimating annual net N mineralization from fall-applied organic amendments, one must account for abnormal temperatures, including those overwinter
Nutritional and environmental effects on ammonia emissions from dairy cattle housing: A meta-analysis
Get PDFNitrogen (N) excreted in urine by dairy cows can be potentially transformed to ammonia (NH3) and emitted to the atmosphere. Dairy production contributes to NH3 emission, which can create human respiratory problems and odor issues, reduces manure quality, and is an indirect source of nitrous oxide (N2O). The objective of this study was to (i) investigate environmental factors and measurement method that influence NH3 from dairy housing, and (ii) identify key explanatory variables in the prediction of NH3 emissions from dairy barns using a meta-analytical approach. Data from 25 studies were used for the preliminary analysis and data from 10 studies reporting 87 treatment means were used for the meta-analysis. Season, flooring type, manure handling and housing type and system significantly affected NH3 emission rates as well as the measurement method used to quantify the NH3 emission. Ammonia emissions rates from open-lot and scrape systems were considerably greater and those from deep pit systems lower compared to U.S. Environmental Protection Agency (USEPA) estimates used in national inventory calculations. For nutritional effect analysis, the between-study variability (heterogeneity) of the mean emission was estimated using random-effect models and had a significant effect (P < 0.01). Therefore, random-effect models were extended to mixed-effect models to explain heterogeneity. Available dietary and animal variables were included as fixed effects in the mixed-effect models. The final mixed-effect model included dietary crude protein, milk yield and dry matter intake, explaining 45.5% of the heterogeneity in NH3 emissions. A unit increase in milk yield (kg/d) resulted in 4.9 g cow/d reduction in NH3 emissions, and a unit increase in diet crude protein content (%) and dry matter intake (kg/d) resulted in 10.2 and 16.3 g cow/d increase in NH3 emissions, respectively. Ammonia emissions from dairy barns are driven by several factors including housing system, season and diet. Crude protein content of the diet, dry matter intake and milk production are important animal related factors that significantly affect ammonia emission from dairy facilities