Scientific Targets for Healthy Diets* Food group Food subgroup Reference diet (g/day) Possible ranges (g/day) Whole Grains All grains 232 0 to 60% of energy Tubers/Starchy Vegetables Potatoes, cassava 50 0 to 100 Vegetables All vegetables 300 200 to 600 Fruits All Fruits 200 100 to 300 Dairy Foods Dairy Foods 250 0 to 500 Beef, lamb, pork 14 0 to 28 Protein Sources Chicken, other poultry 29 0 to 58 Eggs 13 0 to 25 Fish 28 0 to 100 Dry beans, lentils, peas 50 0 to 100 Soy 25 0 to 50 Nuts 50 0 to 75 Added fats Unsaturated oils 40 20-80 Added sugars All sweeteners 31 0 to 31 * See Table 1 for a complete list of scientific targets for a 2500 kcal/day healthy reference diet The Commission has integrated, with the quantification of universal healthy diets, global scientific targets for sustainable food systems. The objective is to provide scientific boundaries to reduce environmental degradation arising from food production at all scales. The quantification of scientific targets for the safe operating space of food systems in the world, was done for the key environmental systems and processes where food production plays a dominant role in determining the state of the planet. There is strong scientific evidence that food production is among the largest drivers of global environmental change due to its contributions to greenhouse gas (GHG) emissions, biodiversity loss, freshwater use, eutrophication, and land-system change (as well as chemical pollution, which is not assessed by this Commission). In turn, food production depends upon the continued functioning of these biophysical systems and processes in regulating and maintaining a stable Earth system. These systems and processes thereby provide a necessary set of globally systemic indicators of what constitutes sustainable food production. The Commission concludes that these quantitative scientific targets for sustainable food systems, constitute universal and scalable planetary boundaries for the food system, (Table 2). However, the uncertainty range for these food boundaries remain high, due to the inherent complexity in Earth system dynamics from local ecosystems to the functioning of the biosphere and the climate system. Scientific Targets for Sustainable Food Production Earth system process Control variable Boundary Uncertainty Range Climate change GHG (CH4 and N2O) emissions 5 Gt CO2-eq yr-1 (4.7-5.4 Gt CO2-eq yr-1) Nitrogen cycling N application 90 Tg N yr-1 (65-90 Tg N yr-1) (90-130 Tg N yr-1) Phosphorus cycling P application 8 Tg P yr-1 (6-12 Tg P yr-1) (8-16 Tg P yr-1) Freshwater use Consumptive water use 2,500 km 3 yr-1 (1000-4000 km 3 yr-1) Biodiversity loss Extinction rate 10 E/MSY (1-80 E/MSY) Land-system change Cropland use 13 M km 2 (11-15 M km 2)

There is an ongoing debate on what constitutes sustainable intensification of agriculture (SIA). In this paper, we propose that a paradigm for sustainable intensification can be defined and translated into an operational framework for agricultural development. We argue that this paradigm must now be defined—at all scales—in the context of rapidly rising global environmental changes in the Anthropocene, while focusing on eradicating poverty and hunger and contributing to human wellbeing. The criteria and approach we propose, for a paradigm shift towards sustainable intensification of agriculture, integrates the dual and interdependent goals of using sustainable practices to meet rising human needs while contributing to resilience and sustainability of landscapes, the biosphere, and the Earth system. Both of these, in turn, are required to sustain the future viability of agriculture. This paradigm shift aims at repositioning world agriculture from its current role as the world’s single largest driver of global environmental change, to becoming a key contributor of a global transition to a sustainable world within a safe operating space on Earth.

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ABSTRACT Background Chicken production in the context of nutrition-sensitive agriculture may benefit child nutrition in low-income settings. Objectives This study evaluated effects of 1) a chicken production intervention [African Chicken Genetic Gains (ACGG)], and 2) the ACGG intervention with nutrition-sensitive behavior change communication (BCC) [ACGG + Agriculture to Nutrition (ATONU)], on child nutrition and health outcomes and hypothesized intermediaries. Methods Forty ACGG villages received 25 genetically improved chickens and basic husbandry guidance; of these, 20 ACGG + ATONU villages in addition received a nutrition-sensitive behavior change and homegardening intervention; 20 control clusters received no intervention. We assessed effects of the interventions on height-for-age z scores (HAZ), weight-for-age z scores (WAZ), and weight-for-height z scores (WHZ) at 9 (midline) and 18 mo (endline) through unadjusted and adjusted ordinary least squares (OLS) regressions. We examined the interventions’ effects on hypothesized intermediaries including egg production and consumption, dietary diversity, women's empowerment, income, child morbidities, anemia, and chicken management practices through OLS and log binomial models. Results Data included 829 children aged 0–36 mo at baseline. ACGG + ATONU children had higher midline HAZ [mean difference (MD): 0.28; 95% CI: 0.02, 0.54] than controls. The ACGG group had higher HAZ (MD: 0.28; 95% CI: 0.05, 0.50) and higher WAZ (MD: 0.18; 95% CI: 0.01, 0.36) at endline than controls; after adjusting for potential baseline imbalance, effects were similar but not statistically significant. At endline, differences in ACGG + ATONU children's HAZ and WAZ compared with controls were similar in magnitude to those of ACGG, but not statistically significant. There were no differences in anthropometry between the intervention groups. ACGG + ATONU children had higher dietary diversity and egg consumption than ACGG children at endline. Both interventions showed improvements in chicken management practices. The interventions did not increase anemia, diarrhea, fever, or vomiting, and the ACGG + ATONU group at midline showed reduced risk of fever. Conclusions A chicken production intervention with or without nutrition-sensitive BCC may have benefited child nutrition and did not increase morbidity. This trial was registered at clinicaltrials.gov as NCT03152227.

Background In order to protect health workers from SARS-CoV-2, there is need to characterise the different types of patient facing health workers. Our first aim was to determine both the infection and seroprevalence of SARS-COV-2 in health workers. Our second aim was to evaluate the occupational and demographic predictors of seropositivity to inform the country’s infection prevention and control (IPC) strategy. Methods and principal findings We invited 713 staff members at 24 out of 35 health facilities in the City of Bulawayo in Zimbabwe. Compliance to testing was defined as the willingness to uptake COVID-19 testing by answering a questionnaire and providing samples for both antibody testing and PCR testing. SARS-COV-2 antibodies were detected using a rapid diagnostic test kit and SAR-COV-2 infection was determined by real-time (RT)- PCR. Of the 713 participants, 635(89%) consented answering the questionnaire and providing blood sample for antibody testing while 560 (78.5%) agreed to provide nasopharyngeal swabs for the PCR COVID-19 testing. Of the 635 people (aged 18–73) providing a blood sample 39.1% reported a history of past COVID-19 symptoms while 14.2% reported having current symptoms of COVID-19. The most-prevalent co-morbidity among this group was hypertension (22.0%) followed by asthma (7.0%) and diabetes (6.0%). The SARS-CoV-2 sero-prevalence was 8.9%. Of the 560 participants tested for SARS-CoV-2 infection, 2 participants (0.36%) were positive for SAR-CoV-2 infection by PCR testing. None of the SARS-CoV-2 antibody positive people were positive for SAR-CoV-2 infection by PCR testing. Conclusion and interpretation In addition to clinical staff, several patient-facing health workers were characterised within Zimbabwe’s health system and the seroprevalence data indicated that previous exposure to SAR-CoV-2 had occurred across the full spectrum of patient-facing staff with nurses and nurse aides having the highest seroprevalence. Our results highlight the need for including the various health workers in IPC strategies in health centres to ensure effective biosecurity and biosafety.