Scenario analysis of crop-livestock integration system for increased high-quality fodder productivity among smallholder cattle farmers in Rwanda
Description
There 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.
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Steps to reproduce
Data were collected through a cross-sectional survey in 393 farms selected in 3 sectors of the lowlands and the highlands by trained enumerators. Fodder and crop residues quality in terms of DM, CP, and ME was obtained through laboratory analysis by lab technicians and modelling equations using farm design models by Groot et al. (2016). DM, CP, and ME requirements for body maintenance of owned cattle were estimated through standards by NASEM (2023) and modelling equations using LIGAP’s dairy models (Van DE Linden, 2021). Variables Definition DMIR Calves =BW x 2.5/100 DMIR Bulls =BW x 3/100 DMIR Dry cows =BW x 2/100 DMIR Heifers =BWx 2.5/100 DMIR Lactating =BW x 3.5/100 DMIR Pregnant BW x 2.75/100 CPR =BW^0.75 x 6.27 MER =BW^0.75 x 0.589 HI =Grain yield per ha/ Total biomass per ha DM Biomass crop residues/year =Grain yield per household x HI x (% DM)x number of seasons CP crop residues/year =(%Nitrogen x 6.25) ME crop residues =2.2 + (0.136 × G24) + (0.057 × CP) + (0.0029 × CP2) ; G24 = Gv 24 h 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 Cover crop area =Cropping area x 2/3 DM fodder biomass per year DM fodder productivity per household x number of cuts in a year CP fodder =% Nitrogen x 6.25 ME fodder =2.2 + (0.136 × G24) + (0.057 × CP) + (0.0029 × CP2) ; G24=Gv 24h 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
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Funders
- United States Agency for International DevelopmentUnited StatesGrant ID: award number EEM-G-00-04-00013