Allometric equations, wood density and partitioning of aboveground biomass in the arboretum of Ruhande, Rwanda

Open Access Journal; Published online: 10 Nov 2020There is growing interest in plantation forests throughout Africa because of their role in environment, economy and people's livelihoods. However, the contribution of planted forests to climate mitigation is poorly understood, partly due to lack of allometric equations for biomass estimation. This study aimed to determine wood density and biomass fractions in aboveground components, and to develop biomass estimation equations for multispecies plantation forests in the arboretum of Ruhande in Rwanda. Allometric equations were developed by regressing diameter at breast height (DBH) alone or in combination with height or wood density or age of trees against the biomass of 45 trees harvested from a 200-ha site. Biomass estimates obtained from destructively sampled trees were up-scaled to estimate the amount of carbon stocked in the arboretum of Ruhande, assuming a stem density of 250 stems per ha. Wood density varied among the species but not tree size. The greatest fraction of aboveground biomass was allocated to stems (71–77%) compared to branches (19–27%) and leaves (1–8%) and varied by species. Equations developed fit the data well with DBH explaining over 90% of the observed variation in aboveground and stem biomass. Including height or wood density as supporting parameters reduced the relative error for aboveground biomass by 6.4 and 8.0% and improved model fit by 2.1 and 2.9%, respectively. Akaike information criterion (AIC) showed that wood density (AIC=63.6) and height (AIC=48.2) were the most suitable parameters to support DBH as a proxy for aboveground and stem biomass, respectively. Allometric equations developed in this study are useful tool for estimating carbon stocks of plantation forests in Rwanda and can enhance the accuracy of biomass predictions where site-specific equations rather than generalized models are recommended. Further studies focusing on development of allometric equations on belowground biomass in such systems are recommended

Investigating infection management and antimicrobial stewardship in surgery: a qualitative study from India and South Africa

Appendix A. Supplementary data: The following is the Supplementary data to this article: Multimedia component 1. Avalable online at https://www.sciencedirect.com/science/article/pii/S1198743X20307734#appsec1 .This work was supported by the Economic and Social Science Research Council (ESRC) and the National Institute for Health Research, UK Department of Health (HPRU-2012-10047) in partnership with Public Health England. This study is part of the ASPIRES project (Antibiotic use across Surgical Pathways—Investigating, Redesigning and Evaluating Systems) (https://www.imperial.ac.uk/arc/aspires/). ASPIRES aims to address antimicrobial resistance and improve clinical outcomes optimizing antibiotic usage along surgical pathways. The support of ESRC as part of the Antimicrobial Cross Council initiative supported by the seven UK research councils, and also the support of the Global Challenges Research Fund, is gratefully acknowledged

Investigating infection management and antimicrobial stewardship in surgery: a qualitative study from India and South Africa

OBJECTIVES: To investigate the drivers for infection management and antimicrobial stewardship (AMS) across high-infection-risk surgical pathways. METHODS: A qualitative study-ethnographic observation of clinical practices, patient case studies, and face-to-face interviews with healthcare professionals (HCPs) and patients-was conducted across cardiovascular and thoracic and gastrointestinal surgical pathways in South Africa (SA) and India. Aided by Nvivo 11 software, data were coded and analysed until saturation was reached. The multiple modes of enquiry enabled cross-validation and triangulation of findings. RESULTS: Between July 2018 and August 2019, data were gathered from 190 hours of non-participant observations (138 India, 72 SA), interviews with HCPs (44 India, 61 SA), patients (six India, eight SA), and case studies (four India, two SA). Across the surgical pathway, multiple barriers impede effective infection management and AMS. The existing implicit roles of HCPs (including nurses and senior surgeons) are overlooked as interventions target junior doctors, bypassing the opportunity for integrating infection-related care across the surgical team. Critically, the ownership of decisions remains with the operating surgeons, and entrenched hierarchies restrict the inclusion of other HCPs in decision-making. The structural foundations to enable staff to change their behaviours and participate in infection-related surgical care are lacking. CONCLUSIONS: Identifying the implicit existing HCP roles in infection management is critical and will facilitate the development of effective and transparent processes across the surgical team for optimized care. Applying a framework approach that includes nurse leadership, empowering pharmacists and engaging surgical leads, is essential for integrated AMS and infection-related care

Determining and managing maize yield gaps in Rwanda

Smallholder maize growers are experiencing significant yield gaps due to sub-optimal agricultural practices. Adequate agricultural inputs, particularly nutrient amendments and best management practices, are essential to reverse this trend. There is a need to understand the cause of variations in maize yield, provide reliable early estimates of yields, and make necessary recommendations for fertilizer applications. Maize yield prediction and estimates of yield gaps using objective and spatial analytical tools could provide accurate and objective information that underpin decision support. A study was conducted in Rwanda at Nyakiliba sector and Gashora sector located in Birunga and Central Bugesera agro-ecological zones, with the objectives of (1) determining factors influencing maize yield, (2) predicting maize yield (using the Normalized Difference Vegetation Index (NDVI) approach), and (3) assessing the maize yield gaps and the impact on food security. Maize grain yield was significantly higher at Nyakiliba (1.74 t ha−1) than at Gashora (0.6 t ha−1). NDVI values correlated positively with maize grain yield at both sites (R2 = 0.50 to 0.65) and soil fertility indicators (R2 = 0.55 to 0.70). Maize yield was highest at 40 kg P ha−1 and response to N fertilizer was adequately simulated at Nyakiliba (R2 = 0.85, maximum yield 3.3 t ha−1). Yield gap was 4.6 t ha−1 in Nyakiliba and 5.1 t ha−1 in Gashora. Soil variables were more important determinants of social class than family size. Knowledge that low nutrient inputs are a major cause of yield gaps in Rwanda should prioritize increasing the rate of fertilizer use in these agricultural systems

Modelling climate change impacts on maize yields under low nitrogen input conditions in sub‐Saharan Africa

Smallholder farmers in sub‐Saharan Africa (SSA) currently grow rainfed maize with limited inputs including fertilizer. Climate change may exacerbate current production constraints. Crop models can help quantify the potential impact of climate change on maize yields, but a comprehensive multi‐model assessment of simulation accuracy and uncertainty in these low‐input systems is currently lacking. We evaluated the impact of varying [CO2], temperature and rainfall conditions on maize yield, for different nitrogen (N) inputs (0, 80, 160 kg N ha‐1) for five environments in SSA, including cool sub‐humid Ethiopia, cool semi‐arid Rwanda, hot sub‐humid Ghana and hot semi‐arid Mali and Benin using an ensemble of 25 maize models. Models were calibrated with measured grain yield, plant biomass, plant N, leaf area index, harvest index and in‐season soil water content from two‐year experiments in each country to assess their ability to simulate observed yield. Simulated responses to climate change factors were explored and compared between models. Calibrated models reproduced measured grain yield variations well with average rRMSE of 26%, although uncertainty in model prediction was substantial (CV = 28%). Model ensembles gave greater accuracy than any model taken at random. Nitrogen fertilization controlled the response to variations in [CO2], temperature and rainfall. Without N fertilizer input, maize (i) benefited less from an increase in atmospheric [CO2], (ii) was less affected by higher temperature or decreasing rainfall and (iii) was more affected by increased rainfall because N leaching was more critical. The model inter‐comparison revealed that simulation of daily soil N supply and N leaching plays a crucial role in simulating climate change impacts for low‐input systems. Climate change and N input interactions have strong implications for the design of robust adaptation practices across SSA, because the impact of climate change will be modified if farmers intensify maize production with more mineral fertilizer