Does harvesting Urochloa and Megathyrsus forages at short intervals confer an advantage on cumulative dry matter yields and quality?

BACKGROUND: Due to increasing demand for livestock products in sub-Saharan Africa, increasing livestock productivity is a priority. The core constraint is limited availability of feed of good quality. We assessed optimal harvesting time of three improved grasses, two Urochloa lines (Basilisk a selection from wild population, Cayman - a hybrid, a product of breeding) plus Mombasa, a Megathyrsus selection. All are released in Latin America and Kenya or in the registration in other regional countries. We assessed dry matter (DM) yields and quality at 4, 6, 8 and 12 weeks of age in two sites. RESULTS: DM yields (in t ha-1 ) were of the order Cayman (9.6-14.3) > Mombasa (8.0-11.3) > Basilisk (5.5-10.2) in one site, and Cayman (6.4-9.7) > Basilisk (4.9-7.6) > Mombasa (3.3-5.9) at site two. The harvesting regimes produced DM largely similar for weeks 4 and 6, 6 and 8, 8 and 12. Across the sites quality was of the order Cayman > Mombasa > Basilisk for neutral detergent fiber (NDF), metabolizable energy (ME) and crude protein (CP). With increasing harvesting interval, MJ ME ha-1 and kg CP ha-1 were inconsistent across both sites, but significant differences returned for MJ ME ha-1 unlike kg CP ha-1 . CONCLUSIONS: Harvesting at either 8 or 12 weeks is not recommendable as quality drops without an increase in DM yield that can compensate despite doubling and tripling time respectively, compared to 4 weeks. We recommend harvesting at 4 through 6 weeks for any of the three grasses based on yield against time, and demand at the intensified cut-and-carry smallholder systems. © 2021 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Survey data on income, food security, and dietary behavior among women and children from households of differing socio-economic status in urban and peri-urban areas of Nairobi, Kenya.

This article describes data collected to analyze consumer behaviors in vulnerable populations by examining key access constraints to nutritious foods among households of differing socio-economic status in urban and peri­urban areas of Nairobi, Kenya. The key variables studied include wealth status, food security, and dietary behavior indicators at individual and household level. Household food insecurity access scale (HFIAS), livelihood coping strategies (LCS), food expenditure share (FES), food consumption score (FCS), household dietary diversity score (HDDS), minimum dietary diversity-women(MDD-W), and child dietary diversity score (CDDS) indicators were used to measure food security. Household assets were used to develop an asset-based wealth index that grouped the study sample population into five wealth quantiles, while income levels were used to estimate FES. The hypothesis that guided the cross-sectional survey conducted to generate these data is that vulnerability to food insecurity and poverty are important drivers of food choice that influence household and individual dietary behavior. Data from this study was thus used to assess direction and strength of association between; household food insecurity, wealth status, women, children, and household dietary behavior in both urban and peri­urban populations sampled.

Survey data of intra-household decision making and smallholder agricultural production in Northern Uganda and Southern Tanzania.

This article provides a description of intra-household survey data that were collected in Uganda and Tanzania in 2014 and 2015, respectively. The surveys were implemented using a structured questionnaire administered among 585 households in Uganda and 608 in Tanzania. Information on decision making processes in agricultural production was collected from the principal adult male and female decision-makers in each household. The survey consisted of two parts. Firstly, the decision-makers, both male and female of each household were jointly interviewed. Secondly, individual interviews were carried out, questioning the decision-makers separately. The datasets include both household and individual level data containing numeric, categorical and string variables. The datasets have been shared publicly on the Harvard dataverse.