Scenario analysis of crop-livestock integration system for increased high-quality fodder productivity among smallholder cattle farmers in Rwanda

Description

here is a perennial shortage of fodder among smallholder farmers in most sub-Saharan countries, including Rwanda. Forages and crop residues productivity per day were investigated in terms of DM, CP, and ME productivity vis-à-vis their requirement by existing cattle in the volcanic highlands and the eastern savanna regions of Rwanda. Different production scenarios were predicted if farmers adopt Mucuna pruriens or Desmodium intortum in an intercropping system and accommodate either Napier Cenchrus purpureus, Napier Kakamega 1, Brachiaria molato2, or the Napier pakchong-1 variety on their farm boundaries. Data on cattle requirements and fodder productivity were analyzed using a general linear regression model in SPSS. A paired sample t-test was conducted to examine the results before and after predicting additional biomass productivity. Results showed that the current perennial fodder productivity was 784 kg DM, 48 860 g CP, and 3 497 MJ (ME), while their yearly requirement by existing cattle was 3 960 kg DM, 178 704 g CP, and ME of 16 776 MJ. Predictive values of the intercropping system show that the productivity of fodder annually can increase its DM content more than 5 times, from 784 to 4 878 kg DM/year, CP and ME content in fodder can increase 10 times, from 48 860 g CP to 662 112 g CP/year and from 3 497mj to 34 920 MJ per year. The present study proposed planting on contour strips a grass fodder that yields at least 60tons DM per ha per year, and to use a cover fodder that yields around 20 tons of dry matter per ha per year. The adoption of the crop/fodder integration system would lead to the year-round availability of fodder for livestock across all seasons.

Steps to reproduce

A minimum sample size of 385 households was determined according to Fisher et al. (1998). Thereafter, a total of 248 and 145 households in the lowlands and the highlands, respectively, were selected for data collection using a simple random sampling procedure where each household owning a dairy cow had an equal opportunity to belong in the study Mohsin (2021). Data were collected on general land management that included cropping area and fodder area, cattle number, and their body weight. The crop yields were documented in NIRS reports of seasonal yields by district in 2020-2021. Biomass yield was calculated according to Alemu (2021) based on the dry matter content (supplementary Table 4). The fodder yields were calculated based on referenced information of tropical on-farm yields where minimum agronomic practices are applied (Table 2). Determination of the maintenance requirement of existing cattle (Table 1). Calculation of the production gaps and predictions of production in case an intercropping system is adopted. The interview was conducted in the vernacular language (Kinyarwanda) after pre-testing the questionnaire. Variables Definition References DMIR Calves BW x 2.5/100 NASEM, 2021 DMIR Bulls BW x 3/100 NASEM, 2021 DMIR Dry cows BW x 2/100 NASEM, 2021 DMIR Heifers BWx 2.5/100 NASEM, 2021 DMIR Lactating BW x 3.5/100 Atalay and Kahriman, 2020 DMIR Pregnant BW x 2.75/100 NASEM, 2021 CPR BW^0.75 x 6.27 Van Del Linden MER BW^0.75 x 0.589 Van Del Linden HI Grain yield per ha/ Total biomass per ha Alemu, 2021 DM Biomass crop residues/year Grain yield per household x HI x (% DM)x number of seasons Alemu, 2021 CP crop residues/year (%Nitrogen x 6.25) NASEM, 2023 ME crop residues 2.2 + (0.136 × G24) + (0.057 × CP) + (0.0029 × CP2) ; G24 = Gas volume after 24 h Groot and Oomen, 2016 Land size Surface area calculated based on the length and width of a land Cropping area Surface area calculated based on the length and width of a given plot Fodder area Length of farm boundaries times 1 meter width Umunezero et al.,2016 Cover crop area Cropping area x 2/3 Umunezero et al.,2016 DM fodder biomass per year DM fodder productivity per household x number of cuts in a year CP fodder % Nitrogen x 6.25 NASEM, 2023 ME fodder 2.2 + (0.136 × G24) + (0.057 × CP) + (0.0029 × CP2) ; G24 = Gas volume after 24 h Groot and Oomen, 2016 Gap DM productivity DMIRcattle - DM (Fodder + crop residues) Gap CP productivity CPRcattle – CP (Fodder + Crop residues) Gap ME productivity MERcattle – ME (Fodder + Crop residues)

Institutions

• University of Nairobi Faculty of Agriculture

Categories

Funders

• United States Agency for International Development

  United States

  Grant ID: award number EEM-G-00-04-00013

Licence