The problem of precision and trend detection posed by small elephant populations in West Africa

The precision of elephant estimates from aerial sample surveys and dung counts is inversely proportional to abundance. West African elephant populations are already small, and the power of a monitoring programme to detect changes in abundance diminishes as the population shrinks in size. Thus it will be difficult to evaluate the effects on elephant numbers of new management policies in West Africa. The same will be true of monitoring schemes for antelope and primate populations that are hunted for bushmeat. Elephant estimates from dung counts are more precise than those from aerial sample surveys, and changes in elephant numbers are more likely to be detected in the subregion by dung counts than by aerial sample surveys.     

A decision-tree model to detect post-calving diseases based on rumination,activity, milk yield,BW and voluntary visits to the milking robot

Early detection of post-calving health problems is critical for dairy operations. Separating sick cows from the herd is important, especially in robotic-milking dairy farms, where searching for a sick cow can disturb the other cows’ routine. The objectives of this study were to develop and apply a behaviour- and performance-based health-detection model to post-calving cows in a robotic-milking dairy farm, with the aim of detecting sick cows based on available commercial sensors. The study was conducted in an Israeli robotic-milking dairy farm with 250 Israeli-Holstein cows. All cows were equipped with rumination- and neck-activity sensors. Milk yield, visits to the milking robot and BW were recorded in the milking robot. A decision-tree model was developed on a calibration data set (historical data of the 10 months before the study) and was validated on the new data set. The decision model generated a probability of being sick for each cow. The model was applied once a week just before the veterinarian performed the weekly routine post-calving health check. The veterinarian’s diagnosis served as a binary reference for the model (healthy–sick). The overall accuracy of the model was 78%, with a specificity of 87% and a sensitivity of 69%, suggesting its practical value.     

Host‐size decisions of female parasitoid wasps seeking hidden hosts

1. Choice of host size may play a critical role in parasitoid success, a task that takes on added complications when dealing with concealed hosts, but most such studies of insect behaviour have only taken place in the laboratory. 2. This study investigates the success of a wasp (Alabagrus texanus: Braconidae) in finding host caterpillars Herpetogramma theseusalis (Crambidae) of the most effectively handled size hidden in shelters, in both the field and the laboratory. 3. First, the study tested wasp preference and success in parasitizing large, middle‐sized and small caterpillars (> 5, 3–5, < 3 mm) presented in the open, one at a time, in the laboratory. The wasps attacked (inserted or attempted to insert their ovipositor) a higher proportion of middle‐sized (3–5 mm) caterpillars compared with either small (< 3 mm) or large (> 5 mm) caterpillars. Naïve wasps attacked large caterpillars more often than did experienced wasps. Wasps responded to increasing caterpillar size by increasing the number of legs used to pin their prey rather than by increasing handling time. 4. The frequencies of visits to shelters in the field containing a majority of either large or middle‐sized caterpillars were then compared, followed by a test providing the wasps with similar choices under controlled laboratory conditions. Wasps most frequently visited shelters containing a majority of middle‐sized caterpillars both in the field and under controlled laboratory conditions. 5. The combined results confirmed that the wasps can size‐select their hosts both in the field and in laboratory tests.     

Atomic Resolution Cryo-EM Structure of β-Galactosidase

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Can you trust the parametric standard errors in nonlinear least squares? Yes,with provisos

Background

Questions about the reliability of parametric standard errors (SEs) from nonlinear least squares (LS) algorithms have led to a general mistrust of these precision estimators that is often unwarranted.

Impact of automatic milking systems on dairy cattle producers’ reports of milking labour management,milk production and milk quality

Automatic milking systems (AMS), or milking robots, are becoming widely accepted as a milking technology that reduces labour and increases milk yield. However, reported amount of labour saved, changes in milk yield, and milk quality when transitioning to AMS vary widely. The purpose of this study was to document the impact of adopting AMS on farms with regards to reported changes in milking labour management, milk production, milk quality, and participation in dairy herd improvement (DHI) programmes. A survey was conducted across Canada over the phone, online, and in-person. In total, 530 AMS farms were contacted between May 2014 and the end of June 2015. A total of 217 AMS producers participated in the General Survey (Part 1), resulting in a 41% response rate, and 69 of the respondents completed the more detailed follow-up questions (Part 2). On average, after adopting AMS, the number of employees (full- and part-time non-family labour combined) decreased from 2.5 to 2.0, whereas time devoted to milking-related activities decreased by 62% (from 5.2 to 2.0 h/day). Median milking frequency was 3.0 milkings/day and robots were occupied on average 77% of the day. Producers went to fetch cows a median of 2 times/day, with a median of 3 fetch cows or 4% of the herd per robot/day. Farms had a median of 2.5 failed or incomplete milkings/robot per day. Producers reported an increase in milk yield, but little effect on milk quality. Mean milk yield on AMS farms was 32.6 kg/cow day. Median bulk tank somatic cell count was 180 000 cells/ml. Median milk fat on AMS farms was 4.0% and median milk protein was 3.3%. At the time of the survey, 67% of producers were current participants of a DHI programme. Half of the producers who were not DHI participants had stopped participation after adopting AMS. Overall, this study characterized impacts of adopting AMS and may be a useful guide for making this transition.     

Targeted subfield switchgrass integration could improve the farm economy,water quality,and bioenergy feedstock production

Progress on reducing nutrient loss from annual croplands has been hampered by perceived conflicts between short‐term profitability and long‐term stewardship, but these may be overcome through strategic integration of perennial crops. Perennial biomass crops like switchgrass can mitigate nitrate‐nitrogen (NO₃‐N) leaching, address bioenergy feedstock targets, and – as a lower‐cost management alternative to annual crops (i.e., corn, soybeans) – may also improve farm profitability. We analyzed publicly available environmental, agronomic, and economic data with two integrated models: a subfield agroecosystem management model, Landscape Environmental Assessment Framework (LEAF), and a process‐based biogeochemical model, DeNitrification‐DeComposition (DNDC). We constructed a factorial combination of profitability and NO₃‐N leaching thresholds and simulated targeted switchgrass integration into corn/soybean cropland in the agricultural state of Iowa, USA. For each combination, we modeled (i) area converted to switchgrass, (ii) switchgrass biomass production, and (iii) NO₃‐N leaching reduction. We spatially analyzed two scenarios: converting to switchgrass corn/soybean cropland losing >US$ 100 ha^?1 and leaching >50 kg ha^?1 (‘conservative’ scenario) or losing >US$ 0 ha^?1 and leaching >20 kg ha^?1 (‘nutrient reduction’ scenario). Compared to baseline, the ‘conservative’ scenario resulted in 12% of cropland converted to switchgrass, which produced 11 million Mg of biomass and reduced leached NO₃‐N 18% statewide. The ‘nutrient reduction’ scenario converted 37% of cropland to switchgrass, producing 34 million Mg biomass and reducing leached NO₃‐N 38% statewide. The opportunity to meet joint goals was greatest within watersheds with undulating topography and lower corn/soybean productivity. Our approach bridges the scales at which NO₃‐N loss and profitability are usually considered, and is informed by both mechanistic and empirical understanding. Though approximated, our analysis supports development of farm‐level tools that can identify locations where both farm profitability and water quality improvement can be achieved through the strategic integration of perennial vegetation.