Physical and financial evaluation of a group of high producing dairy farms in New Zealand : a thesis presented in partial fulfilment of the requirements for the degree of Master of Applied Science (MApplSc) in Pastoral Science, Institute of Natural Resources, Massey University, Palmerston North, New Zealand

Traditionally, New Zealand dairy production has been based on high pasture utilisation at high stocking rates, which resulted in low animal performance. Recently, a group of farmers (AGMARDT - Dairy Farm Monitoring Programme) gradually changed their production policy to a high production per hectare system achieved through high animal performance. The system is based on pre and post grazing herbage mass targets, strategic use of supplements to overcome pasture deficit and moderate stocking rates (2.7 cows/ha). This project evaluated the physical and financial characteristics of nine case study farms in the Southern North Island of New Zealand, involved in these changes. A one-year system study was conducted (2000/2001) in which physical and financial data were obtained to identify factors affecting farm production, efficiency and profitability. The results showed that the systems were effective and profitable, under the conditions in the 2000/2001 year. Average annual milksolids production per cow (411 kg MS/cow/year) and per hectare (1,100 kg MS/ha/year) for the case study farms were 33% higher than the national average. Average annual total intake for all farms was 5,257 kg DM/cow, 14,035 kg DM/ha, 59,656 MJ ME/cow and 159,232 MJ ME/ha. Mean economic farm surplus per ha for all case study farms (NZ$3,077/ha) was higher than regional averages (by 622 = 0.71) and per hectare (R 2 = 0.74) were closely correlated with pasture intake. Supplements (24$5.00/kg MS will probably not remain in the future, control of production costs should receive more emphasis, particularly supplement costs. Keywords: dairy system, pasture management, feed quality, pasture intake, supplement intake, animal performance, stocking rate, feed conversion efficiency, cost of milksolids production, profitability

Mental health and burnout syndrome among postgraduate students in medical and multidisciplinary residencies during the COVID-19 pandemic in Brazil : protocol for a prospective cohort study

Background: The COVID-19 pandemic has led to high levels of physical, psychological, and social stress among health care professionals, including postgraduate students in medical and multidisciplinary residencies. This stress is associated with the intense fear of occupational exposure to SARS-CoV-2, the virus known to cause COVID-19. These professionals are at risk of developing physical and mental illnesses not only due to the infection but also due to prolonged exposure to multidimensional stress and continued work overload. Objective: This study aims to evaluate the prevalence of symptoms suggestive of mental disorders and burnout syndrome and determine the risk factors for burnout among postgraduate students in medical and multidisciplinary residencies in Brazil during the COVID-19 pandemic. Methods: For this prospective cohort study with parallel groups, participants were recruited between July and September 2020 to achieve a sample size of at least 1144 participants. Research instruments such as Depression, Anxiety, and Stress Scale; Patient Health Questionnaire; Brief Resilient Coping Scale; and Oldenburg Burnout Inventory will be used to collect data. Data will be collected in 2 waves: the first wave will include data related to sample characterization and psychosocial evaluation, and the second wave will be launched 12 weeks later and will include an evaluation of the incidence of burnout as well as correlations with the potential predictive factors collected in the first wave. Additionally, we will collect data regarding participants’ withdrawal from work. Results: The recruitment took place from July 29 to September 5, 2020. Data analyses for this phase is already in progress. The second phase of the study is also in progress. The final data collection began on December 1, 2020, and it will be completed by December 31, 2020. Conclusions: We believe the findings of this study will help evaluate the impact of the COVID-19 pandemic on the mental health conditions of health professionals in Brazil as well as contribute to the planning and implementation of appropriate measures that can alleviate these mental health challenges. International Registered Report Identifier (IRRID): DERR1-10.2196/2429

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Multivariate logistic regression analysis of predictors associated with burnout (OLBI) among health residents.

Multivariate logistic regression analysis of predictors associated with burnout (OLBI) among health residents.</p

General characteristics of the study population.

General characteristics of the study population.</p

Differences between genders regarding the various variables studied.

Differences between genders regarding the various variables studied.</p

Differences between medical residents and nonmedical residents regarding the various characteristics studied—bivariate analyses (unadjusted).

Differences between medical residents and nonmedical residents regarding the various characteristics studied—bivariate analyses (unadjusted).</p