Tree planting for poverty reduction in less-favoured areas of the Ethiopian highlands

This paper assesses the potential impact of planting of eucalypt trees as a strategy to reduce poverty in a less-favoured area of the highlands of Ethiopia. Results from simulations with a bio-economic model for a less-favoured case study area in the highlands are combined with survey data at community, household and plot level to assess how general the results of the bio-economic model are. Application of the bio-economic model shows clearly that land degradation, population growth, stagnant technology and drought threaten food security. Household welfare and land quality are deteriorating rapidly in the area and interventions are urgently needed to avoid human disaster. Planting of eucalypts on land unsuitable for crop production may substantially increase household incomes if market outlets for trees can be found. Tree planting will not have severe negative effects on food production or land conservation. A policy combining promotion of tree planting and conservation of cropland may achieve win-win benefits in terms of increased household incomes as well as more sustainable land-use. Analysis of survey data from the Amhara Region of Ethiopia reveals that there is a large area of land that is unsuitable for crop production located close to all-weather roads in the less-favoured areas of the region. Few trees have been planted on this type of land up to now. The past policy seems to have discouraged tree planting except on homestead plots that are more suitable for food crops. This paper reports part of the IFPRI/ILRI project ‘Policies for Sustainable Land Management in the East African Highlands’. These two agencies have provided funds and logistical support for the work. The Norwegian Ministry of Foreign Affairs has provided funds for this research in the Amhara Region in Ethiopia. The authors also draw on earlier work funded by Research Council of Norway. Any correspondence should be directed to the first author.     

Porous, crystalline, covalent organic frameworks

Covalent organic frameworks (COFs) have been designed and successfully synthesized by condensation reactions of phenyl diboronic acid {C6H4[B(OH)2]2} and hexahydroxytriphenylene [C18H6(OH)6]. Powder x-ray diffraction studies of the highly crystalline products (C3H2BO)6.(C9H12)1 (COF-1) and C9H4BO2 (COF-5) revealed expanded porous graphitic layers that are either staggered (COF-1, P6(3)/mmc) or eclipsed (COF-5, P6/mmm). Their crystal structures are entirely held by strong bonds between B, C, and O atoms to form rigid porous architectures with pore sizes ranging from 7 to 27 angstroms. COF-1 and COF-5 exhibit high thermal stability (to temperatures up to 500 degrees to 600 degrees C), permanent porosity, and high surface areas (711 and 1590 square meters per gram, respectively).     

Inheritance of coffee leaf rust resistance and identification of AFLP markers linked to the resistance gene

The most important disease of Coffea arabica is coffee leaf rust caused by the fungus Hemileia vastatrix. The purpose of this study was to characterize the inheritance of coffee resistance gene(s) to race II of this pathogen and to identify and map molecular markers linked to this trait. Different populations were used: F₂ (160 plants), BCr (20), and BCs (135), derived from a cross between the resistant genotype Híbrido de Timor UFV 427-15 and the susceptible cultivar Catuaí Amarelo UFV 2143-236 (IAC 30). The segregation analysis showed that the resistance of Híbrido de Timor to race II of the H. vastatrix is conferred by a single dominant gene. The amplification of 176 AFLP (Amplified fragment length polymorphism) primer combinations using bulked segregant analysis (BSA) allowed the identification of three molecular markers linked to the resistance gene. Genetic mapping of these three markers in the F₂ population indicated that they are distributed on both sides, flanking the resistance gene. The markers E.CTC/M.TTT405 and E.CGT/M.TGT300 were found linked to the resistance gene at 8.69 cM (LOD 18.91) and 25.10 cM (LOD 5.37), respectively, while E.CCT/M.TTC230 was localized on the other side of the gene, at 20.50 cM (LOD 6.15). These markers are the first rust resistance markers identified in Híbrido de Timor and can be useful for marker assisted selection in coffee breeding programs.     

Genetics components of rice root architecture and carbon isotopic fractionation parameters: a tracer for breeding in a water-saving irrigation management

Journal of Crop Science and Biotechnology - Phenotyping is the major bottleneck in the effort to develop varieties of rice (Oryza sativa L.) suitable for growing under a water-saving irrigation...     

PRRSV2 genetic diversity defined by RFLP patterns in the United States from 2007 to 2019

The genetic diversity of porcine reproductive and respiratory syndrome virus (PRRSV) increases over time. In 1998, restriction-fragment length polymorphism (RFLP) pattern analysis was introduced to differentiate PRRSV wild-type strains from VR2332, a reference strain from which a commercial vaccine (Ingelvac PRRS MLV) was derived. We have characterized here the PRRSV genetic diversity within selected RFLP families over time and U.S. geographic space, using available ISU-VDL data from 2007 to 2019. The 40,454 ORF5 sequences recovered corresponded to 228 distinct RFLPs. Four RFLPs [2-5-2 (21.2%), 1-7-4 (15.6%), 1-4-4 (11.8%), and 1-8-4 (9.9%)] represented 58.5% of all ORF5 sequences and were used for cluster analysis. Over time, there was increased detection of RFLPs 2-5-2, 1-7-4, 1-3-4, 1-3-2, and 1-12-4; decreased detection of 1-4-2, 1-18-4, 1-18-2, and 1-2-2; and different detection trends for 1-8-4, 1-4-4, 1-26-1, 1-22-2, and 1-2-4. An over-time cluster analysis revealed a single cluster for RFLP 2-5-2, supporting that sequences within RFLP 2-5-2 are still relatively conserved. For 1-7-4, 1-4-4, and 1-8-4, there were multiple clusters. State-wise cluster analysis demonstrated 4 main clusters for RFLP 1-7-4 and 1-8-4, and 6 for RFLP 1-4-4. For the other RFLPs, there was a significant genetic difference within them, particularly between states. RFLP typing is limited in its ability to discriminate among different strains of PRRSV. Understanding the magnitude of genetic divergence within RFLPs helps develop PRRSV regional control programs, placement, herd immunization strategies, and design of appropriate animal movements across borders to minimize the risk of PRRSV transmission.     

Evaluation of Soy Protein Concentrate and Meat and Bone Meal as a Replacement for Fish Meal in the Diet of Nile Tilapia Fingerlings

This experiment evaluated soy protein concentrate (SPC) and meat and bone meal as a replacement for fish meal protein (FP) in the diet of Nile tilapia fingerlings. Five diets were evaluated including a control diet (50.0% crude protein [CP], of which half the protein came from fish meal [FM]), and four diets in which FM was replaced by increasing amounts of SPC (25.5, 50.0, 75.5, and 100.0%) in combination with meat and bone meal. At the conclusion of a 60‐d growth trial, the following parameters were determined: final weight (FW), final length (FL), weight gain (WG), feed intake (FI), apparent feed conversion (AFC), survival (SUR), and production cost. The estimate of the best replacement value was determined by polynomial regression and the linear response plateau. The results showed significant (P < 0.05) improvement for WG and AFC ratio as FM was gradually replaced by SPC. The parameters FL and FI reached their optimum value at a replacement level of 100% SPC. These results show that it is feasible to use a combination of SPC and meat and bone meal in a balanced feed formulation as an alternative protein source in the diets of Nile tilapia fingerlings.     

Mega‐environment identification for soybean (Glycine max) breeding and production in Brazilian Midwest region

Mega‐environment (ME) identification is the first step for evaluating, selecting and recommending genotypes within a target region (TR). The present study aimed to (a) identify MEs, using GGE biplot methods, in Brazilian edaphoclimatic region (ECR) 402 of soybean cultivation, located in the Mato Grosso State (the TR) and (b) compare the performance of genotypes within the TR and in each ME using fixed and mixed models. Data from three years of soybean yield trials, 19 genotypes and 22 environments were used. The biplots GGE, GGL + GGE and GGS + GGE were implemented to identify the MEs. Two MEs were identified in the TR. ME1 presents a higher altitude, farms which use a higher level of fertilizer inputs and a higher occurrence of the soybean cyst nematode (SCN) than ME2. When selection and recommendation are made based on MEs, genotypes with both broadly and specific adaptation can be selected. This action can improve grain yield in the entire target region.     

Visualization and application of disease diagnosis codes for population health management using porcine diseases as a model

Accurate and timely results of diagnostic investigations and laboratory testing guide clinical interventions for the continuous improvement of animal health and welfare. Infectious diseases can severely limit the health, welfare, and productivity of populations of animals. Livestock veterinarians submit thousands of samples daily to veterinary diagnostic laboratories (VDLs) for disease diagnosis, pathogen monitoring, and surveillance. Individual diagnostic laboratory reports are immediately useful; however, aggregated historical laboratory data are increasingly valued by clinicians and decision-makers to identify changes in the health status of various animal populations over time and geographical space. The value of this historical information is enhanced by visualization of trends of agent detection, disease diagnosis, or both, which helps focus time and resources on the most significant pathogens and fosters more effective communication between livestock producers, veterinarians, and VDL professionals. Advances in data visualization tools allow quick, efficient, and often real-time scanning and analysis of databases to inform, guide, and modify animal health intervention algorithms. Value is derived at the farm, production system, or regional level. Visualization tools allow client-specific analyses, benchmarking, formulation of research questions, and monitoring the effects of disease management and precision farming practices. We present here the approach taken to visualize trends of disease occurrence using porcine disease diagnostic code data for the period 2010 to 2019. Our semi-automatic standardized creation of a visualization platform allowed the transformation of diagnostic report data into aggregated information to visualize and monitor disease diagnosis.