Assessment of strip tillage systems for maize production in semi-arid Ethiopia: effects on grain yield and water balance

International audienceThe traditional tillage implement, the Maresha plow, and the tillage systems that require repeated and cross plowing have caused poor rainfall partitioning, land degradation and hence low water productivity in Ethiopia. Conservation tillage could alleviate these problems. However, no-till can not be feasible for smallholder farmers in semi-arid regions of Ethiopia because of difficulties in maintaining soil cover due to low rainfall and communal grazing and because of high costs of herbicides. Strip tillage systems may offer a solution. This study was initiated to test strip tillage systems using implements that were modified forms of the Maresha plow, and to evaluate the impacts of the new tillage systems on water balance and grain yields of maize (Zea mays XX). Experiments were conducted in two dry semi arid areas called Melkawoba and Wulinchity, in the central Rift Valley of Ethiopia during 2003?2005. Strip tillage systems that involved cultivating planting lines at a spacing of 0.75 m using the Maresha plow followed by subsoiling along the same lines (STS) and without subsoiling (ST) were compared with the traditional tillage system of 3 to 4 times plowing with the Maresha plow (CONV). Soil moisture was monitored to a depth of 1.8 m using Time Domain Reflectometer while surface runoff was measured using rectangular trough installed at the bottom of each plot. STS resulted in the least surface runoff (Qs=17 mm-season?1), the highest transpiration (T=196 mm-season?1), the highest grain yields (Y=2130 kg-ha?1) and the highest water productivity using total evaporation (WPET=0.67 kg-m?3) followed by ST (Qs=25 mm-season?1, T=178 mm-season?1, Y=1840 kg-ha?1, WPET=0.60 kg-m?3) and CONV (Qs=40 mm-season?1,T=158 mm-season?1, Y=1720 kg-ha?1, WPET=0.58 kg-m?3). However, when the time between the last tillage operation and planting of maize was more than 26 days, the reverse occurred. There was no statistically significant change in soil physical and chemical properties after three years of experimenting with different tillage systems

Analysis of spatial correlation in predictive models of forest variables that use LiDAR auxiliary information

[EN] Accounting for spatial correlation of LiDAR model errors can improve the precision of model-based estimators. To estimate spatial correlation, sample designs that provide close observations are needed, but their implementation might be prohibitively expensive. To quantify the gains obtained by accounting for the spatial correlation of model errors, we examined (i) the spatial correlation patterns of residuals from LiDAR linear models developed to predict volume, total and stem biomass per hectare, quadratic mean diameter (QMD), basal area, mean and dominant height, and stand density and (ii) the impact of field plot size on the spatial correlation patterns in a standwise managed Mediterranean forest in central Spain. For all variables, the correlation range of model residuals consistently increased with plot radius and was always below 60 m except for stand density, where it reached 85 m. Except for QMD, correlation ranges of model residuals were between 1.06 and 8.16 times shorter than those observed for the raw variables. Based on the relatively short correlation ranges observed when the LiDAR metrics were used as predictors, the assumption of independent errors in many forest management inventories seems to be reasonable and appropriate in practice.The authors wish to thank Jay Ver Hoef and Isabel Molina for their valuable comments on earlier versions of the manuscript. The U.S. Bureau of Land Management, the Spanish Ministry of Industry, Tourism and Trade, and the Spanish Ministry of Science and Innovation provided financial support in the framework of the projects "Use of LIDAR and other remote sensing data with FIA plots for mapping forest inventory in Southwest Oregon," InForest II TSI-020100-2009-815, and CGL2010-19591/BTE, respectively.Mauro, F.; Monleón, VJ.; Temesgen, H.; Ruiz Fernández, LÁ. (2017). Analysis of spatial correlation in predictive models of forest variables that use LiDAR auxiliary information. Canadian Journal of Forest Research. 47(6):788-799. https://doi.org/10.1139/cjfr-2016-0296S78879947

Marketing channels, dynamics and economic incentives for onion production in Ethiopia: A case study from Oromiya Regional State, Ethiopia

This research was initiated to assess the market channels and develops value chain map and econometric model outputs for the onion in Ambo and Toke Kutaye districts of West Showa Zone, Oromiya Regional State, Ethiopia. Primary data were collected using interview guided questionnaires from 183 respondents’ of different actors in onion value chain and four focus group discussions of onion producers. Descriptive and inferential statistics; value chain mapping; marketing margin analysis; and econometrics analysis were used to analyze the data. About four marketing channels were identified in the study areas. The econometric result showed that  education level of household, onion farming experience, number of oxen owned, land size used for onion farming, amount of fertilizer used, access to extension services and family size of house hold were variables those significantly influenced the marketable supply of onion at farmers level. Multiple linear regression model indicated that variables like age, farm experience, family size, selling price and improved inputs were significant in affecting onion marketable supply. Thus, to increase the onion productivity, market channel and performances of all actors to maximize the profits of all value chain actors, it is important to integrate all concerned bodies of the onion value chains along with the supporting sectors

Incidence and Predictors of Pre-Eclampsia Among Pregnant Women Attending Antenatal Care at Debre Markos Referral Hospital, North West Ethiopia: Prospective Cohort Study.

BackgroundPre-eclampsia is a pregnancy-induced hypertension that occurs after 20 weeks of gestation. It is the leading cause of maternal and perinatal morbidity and mortality globally, but it is higher in developing countries. In Ethiopia, conducting research on the incidence and predictors of pre-eclampsia is crucial due to the paucity of information.MethodsA prospective cohort study was undertaken using 242 pregnant women between November 1, 2018 and March 30, 2019 at Debre Markos Referral Hospital. All eligible women who fulfilled the inclusion criteria were included in this study. Data were entered into the epic-data Version 4.2 and analyzed using the STATA Version 14.0 software. The Cox-proportional hazard regression model was fitted and Cox-Snell residual test was used to assess the goodness of fit. Pre-eclampsia free survival time was estimated using the Kaplan-Meier survival curve. Both bivariable and multivariable Cox-proportional hazard regression models were fitted to identify predictors of pre-eclampsia.ResultsThe overall incidence rate of pre-eclampsia was 3.35 per 100 person-years. Having a pre-existing history of diabetes mellitus [AHR=2.7 (95% CI=1.43-8.81)], having a history of multiple pregnancy [AHR=3.4 (95% CI=2.8-6.9)] and being ≥35 years old age [AHR=2.5 (95% CI=1.42-3.54)] were the significant predictors of pre-eclampsia.ConclusionThe incidence of pre-eclampsia was high in this study. Having (pre-existing diabetes and multiple pregnancy) and being ≥35 years old age were the significant predictors of pre-eclampsia. Inspiring pregnant women's health-seeking behavior should provide a chance to diagnose pre-eclampsia early to prevent the medical complication of pre-eclampsia

The State of Addis Ababa 2021: Towards a Healthier City

The 'State of Addis Vol. II: Toward a healthier city' was written by an international multidisciplinary team, as the pandemic was unfolding. The report assesses the relationship between urban form and function and the spread of the COVID-19 pandemic, in Addis Ababa. It explores what is meant by a healthy city, and why planning for and investing in a healthy city, matters to Addis Ababa. It goes on to investigate the state of health, urban infrastructure and social services in the city. The socio-economic and health impacts of the pandemic are also explored further, together with the institutional response to the public health emergency. The findings provide insights on the role of urban form and infrastructure to urban health and urban resilience. Finally, the authors highlight a post-pandemic agenda for a healthier, more resilient city

Analysis and comparison of nonlinear tree height prediction strategies for Douglas-fir forests

Using an extensive Douglas-fir data set from southwest Oregon, we examined the (1) performance and suitability of selected prediction strategies, (2) contribution of relative position and stand-density measures in improving tree height (h) prediction values, and (3) effect of different subsampling designs to fill in missing h values in a new stand using a regional nonlinear model. Nonlinear mixed-effects models (NMEM) substantially improved the accuracy and precision of height prediction over the conventional nonlinear fixed-effects model (NFEM) that assumes the observations are independent, particularly when a few trees are subsampled for height. The predictive performance of a correction factor on a NFEM with relative position and stand-density measures was comparable to that of a NMEM when four or more trees were subsampled for height. When two or more heights were randomly subsampled, the NMEM efficiently explained the differences in the height–diameter relationship because of the variations in relative position of trees and stand density without having to incorporate them into the model. When only one height was subsampled, selecting the largest diameter tree in the stand would result in a lower predicted root mean square error (RMSE) than randomly selecting the height, regardless of the model form or fitting strategy used.A` l’aide d’une banque de donne´es exhaustive sur le sapin Douglas du sud-ouest de l’Oregon, nous avons examine ´ (1) la performance et la pertinence des strate´gies de pre´diction se´lectionne´es, (2) la contribution de la position relative de l’arbre et de la densite´ du peuplement pour ame´liorer la pre´diction de la hauteur des arbres et (3) l’effet de diffe´rents dispositifs d’e´chantillonnage pour imputer la hauteur manquante dans un nouveau peuplement a` l’aide d’un mode`le non line´aire re´gional. Les mode`les non line´aires a` effets mixtes (MNLEM) ame´liorent substantiellement l’exactitude et la pre´cision des pre´dictions de la hauteur comparativement au mode`le non line´aire a` effets fixes conventionnel (MNLEF). Ce dernier suppose que les observations sont inde´pendantes, particulie`rement lorsque peu d’arbres sont e´chantillonne´s pour e´valuer la hauteur. La performance pre´dictive d’un facteur de correction pour le MNLEF base´ sur la mesure de la position relative de l’arbre et de la densite´ du peuplement est comparable a` celle du MNLEM lorsque quatre arbres ou plus sont e´chantillonne´s pour e´valuer la hauteur. Lorsque deux hauteurs ou plus sont e´chantillonne´es ale´atoirement, le MNLEM explique efficacement les diffe´rences dans la relation hauteur-diame`tre dues aux variations de la position relative des arbres et de la densite´ sans avoir a` les incorporer formellement dans le mode`le. Lorsqu’une seule hauteur est e´chantillonne´e, le choix du plus gros arbre dans le peuplement pourrait entraıˆner une erreur de pre´diction plus faible que lorsque la hauteur est se´lectionne´e au hasard, peu importe la forme du mode`le ou la strate´gie d’ajustement utilise´e

Evaluating observed versus predicted forest biomass: R-squared, index of agreement or maximal information coefficient?

The accurate prediction of forest above-ground biomass is nowadays key to implementing climate change mitigation policies, such as reducing emissions from deforestation and forest degradation. In this context, the coefficient of determination ($${R^2}$$) is widely used as a means of evaluating the proportion of variance in the dependent variable explained by a model. However, the validity of $${R^2}$$ for comparing observed versus predicted values has been challenged in the presence of bias, for instance in remote sensing predictions of forest biomass. We tested suitable alternatives, e.g. the index of agreement ($$d$$) and the maximal information coefficient ($$MIC$$). Our results show that $$d$$ renders systematically higher values than $${R^2}$$, and may easily lead to regarding as reliable models which included an unrealistic amount of predictors. Results seemed better for $$MIC$$, although $$MIC$$ favoured local clustering of predictions, whether or not they corresponded to the observations. Moreover, $${R^2}$$ was more sensitive to the use of cross-validation than $$d$$ or $$MIC$$, and more robust against overfitted models. Therefore, we discourage the use of statistical measures alternative to $${R^2}$$ for evaluating model predictions versus observed values, at least in the context of assessing the reliability of modelled biomass predictions using remote sensing. For those who consider $$d$$ to be conceptually superior to $${R^2}$$, we suggest using its square $${d^2}$$, in order to be more analogous to $${R^2}$$ and hence facilitate comparison across studies

A comparison of selected parametric and imputation methods for estimating snag density and snag quality attributes

Snags (standing dead trees) are an essential structural component of forests. Because wildlife use of snags depends on size and decay stage, snag density estimation without any information about snag quality attributes is of little value for wildlife management decision makers. Little work has been done to develop models that allow multivariate estimation of snag density by snag quality class. Using climate, topography, Landsat TM data, stand age and forest type collected for 2356 forested Forest Inventory and Analysis plots in western Washington and western Oregon, we evaluated two multivariate techniques for their abilities to estimate density of snags by three decay classes. The density of live trees and snags in three decay classes (D1: recently dead, little decay; D2: decay, without top, some branches and bark missing; D3: extensive decay, missing bark and most branches) with diameter at breast height (DBH)P12.7 cm was estimated using a nonparametric random forest nearest neighbor imputation technique (RF) and a parametric two-stage model (QPORD), for which the number of trees per hectare was estimated with a Quasipoisson model in the first stage and the probability of belonging to a tree status class (live, D1, D2, D3) was estimated with an ordinal regression model in the second stage. The presence of large snags with DBHP50 cm was predicted using a logistic regression and RF imputation. Because of the more homogenous conditions on private forest lands, snag density by decay class was predicted with higher accuracies on private forest lands than on public lands, while presence of large snags was more accurately predicted on public lands, owing to the higher prevalence of large snags on public lands. RF outperformed the QPORD model in terms of percent accurate predictions, while QPORD provided smaller root mean square errors in predicting snag density by decay class. The logistic regression model achieved more accurate presence/absence classification of large snags than the RF imputation approach. Adjusting the decision threshold to account for unequal size for presence and absence classes is more straightforward for the logistic regression than for the RF imputation approach. Overall, model accuracies were poor in this study, which can be attributed to the poor predictive quality of the explanatory variables and the large range of forest types and geographic conditions observed in the data.Elsevier - Education Use Statement: This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues. This is the publisher’s final pdf. The published article is copyrighted by Elsevier and can be found at: http://www.elsevier.com/Keywords: Snag size class, Nearest neighbor imputation, Snag decay class, Ordinal regression, Snag densityKeywords: Snag size class, Nearest neighbor imputation, Snag decay class, Ordinal regression, Snag densit

A comparison of selected parametric and imputation methods for estimating snag density and snag quality attributes

Snags (standing dead trees) are an essential structural component of forests. Because wildlife use of snags depends on size and decay stage, snag density estimation without any information about snag quality attributes is of little value for wildlife management decision makers. Little work has been done to develop models that allow multivariate estimation of snag density by snag quality class. Using climate, topography, Landsat TM data, stand age and forest type collected for 2356 forested Forest Inventory and Analysis plots in western Washington and western Oregon, we evaluated two multivariate techniques for their abilities to estimate density of snags by three decay classes. The density of live trees and snags in three decay classes (D1: recently dead, little decay; D2: decay, without top, some branches and bark missing; D3: extensive decay, missing bark and most branches) with diameter at breast height (DBH) ≥ 12.7 cm was estimated using a nonparametric random forest nearest neighbor imputation technique (RF) and a parametric two-stage model (QPORD), for which the number of trees per hectare was estimated with a Quasipoisson model in the first stage and the probability of belonging to a tree status class (live, D1, D2, D3) was estimated with an ordinal regression model in the second stage. The presence of large snags with DBH ≥ 50 cm was predicted using a logistic regression and RF imputation. Because of the more homogenous conditions on private forest lands, snag density by decay class was predicted with higher accuracies on private forest lands than on public lands, while presence of large snags was more accurately predicted on public lands, owing to the higher prevalence of large snags on public lands. RF outperformed the QPORD model in terms of percent accurate predictions, while QPORD provided smaller root mean square errors in predicting snag density by decay class. The logistic regression model achieved more accurate presence/absence classification of large snags than the RF imputation approach. Adjusting the decision threshold to account for unequal size for presence and absence classes is more straightforward for the logistic regression than for the RF imputation approach. Overall, model accuracies were poor in this study, which can be attributed to the poor predictive quality of the explanatory variables and the large range of forest types and geographic conditions observed in the data.Keywords: Snag size class, Ordinal regression, Snag density, Snag decay class, Nearest neighbor imputatio

Comparison of uncertainty in per unit area estimates of aboveground biomass for two selected model sets

Uncertainty in above ground forest biomass (AGB) estimates at broad-scale depends primarily on three sources of error that interact and propagate: measurement error, model error, and sampling error. Using Monte Carlo simulations, we compare the total propagated error for two sets of regional-level component equations for lodgepole pine AGB, and for two sets of high-precision instruments by accounting for all three of these sources of error. The two sets of models compared included a set of newly-developed component ratio method (CRM) equations, and a set of component AGB equations currently used by the Forest Inventory and Analysis (FIA) unit of the United States Department of Agriculture (USDA) Forest Service. Relative contributions for measurement, model, and sampling error using the current regional equations were 5%, 2% and 93%, respectively, and 13%, 55% and 32%, respectively using the CRM equations. Relative standard error (RSE) values for the current regional and CRM equations with all three error types accounted for were 20.7% and 36.8%, respectively. Results for the model comparisons indicate that per acre estimates of AGB using the CRM equations are far less precise than those produced with the current set of regional equations. Results for the instrument comparisons indicate the terrestrial lidar scanning reduce uncertainty in broad-scale estimates of AGB attributed to measurement error.Keywords: Pacific Northwest, Measurement error, Sampling error, Model erro