Abstract
Cattle emit over 65% of enteric methane (CH4) in sub-Saharan Africa (SSA), making them the focus of many mitigation strategies targeting livestock emissions. Since measured feed intake data ...are sparse, emission factors for enteric CH4 (EFCH4) are mainly estimated indirectly from gross energy intake (GEI) using the net energy (NE) requirements for different metabolic processes in cattle. However, all NE requirement systems commonly used for cattle in SSA were developed for cattle in temperate regions. Therefore, we assessed the suitability of different enteric CH4 models for estimating the GEI of cattle in SSA. The Intergovernmental Panel on Climate Change (IPCC) and South African models were identified as the main tier 2-based methods used to estimate enteric CH4 emissions from cattle in SSA. In the IPCC model, EFCH4 was estimated as (GEI * Ym/100)/55.65, where Ym is the conversion factor (%) of gross energy in feed to CH4 and 55.65 the energy content of CH4 (MJ/kg). The GEI was estimated based on NE requirements for different metabolic processes in cattle as per the American National Research Council. In the South African model, EFCH4 was estimated as (Y/100 * GEI/55.22), where Y is the CH4 yield and 55.22 is the energy content of CH4; Y was calculated from the dry matter (DM) digestibility while GEI was calculated by predicting DM intake and multiplying it by 18.4 MJ (gross energy per kilogram DM). Also, the suitability of the British and German NE requirement systems was assessed as alternatives used for cattle nutrition in SSA. These NE systems were implemented in the IPCC model to yield the “AFRC” and “GfE” models, respectively. The four models were then evaluated using an evaluation dataset summarizing feed quality and DM intake results from 21 studies conducted in SSA, with 125 dietary treatments, and 822 cattle observations. The relative prediction error (RPE) and concordance correlation coefficient (CCC) were used to evaluate the models’ accuracy. Only the South African model estimated the GEI in dairy cattle with an acceptable RPE (18.9%) and highest CCC (0.87), while the other three models yielded estimates with RPE > 20%. None of the four models we assessed estimated GEI for other cattle (i.e., nondairy) with an RPE < 20% or CCC > 0.30. The inaccuracy in GEI estimates suggests an error of the same magnitude in EFCH4 estimates. Therefore, a concerted effort is needed to improve the accuracy of enteric CH4 estimation models for cattle in SSA.
•Uncertainties due to variability in the data and measurement error are often considered in estimates of the uncertainty associated with enteric emission factors, while model uncertainty has rarely been considered.
•This study provides the first estimate of model uncertainty that may be applied to livestock greenhouse gas inventories in sub-Saharan Africa.
Lay Summary
Accurate estimates of enteric methane (CH4) emissions are needed to ascertain the efficacy of mitigation strategies proposed for cattle in sub-Saharan Africa (SSA). Models used for cattle in SSA were developed using data on metabolic processes measured from cattle in temperate regions which may lead to inaccurate enteric CH4 estimates. Therefore, we assessed the suitability of different enteric CH4 models for estimating the gross energy intake (GEI) of cattle in SSA. The Intergovernmental Panel on Climate Change (IPCC) and South African models were identified as tier 2-based methods for estimating enteric CH4 emissions from cattle in SSA. Also, the British and German net energy systems were implemented in the IPCC model to yield two other models. These four models were evaluated using a dataset summarizing feed quality and intake results from 21 studies conducted in SSA. Only the South African model estimated the GEI of dairy cattle with an error of <20% (acceptable cutoff point), while the other three models yielded estimates with the error of >20%. None of the four models estimated GEI for nondairy cattle with an error of <20%. We provide the first estimate of model uncertainty that may be applied to enteric CH4 estimates from cattle in SSA.
•Milk yield gaps in Africa result from biophysical and socio-economic constraints.•Entry points for cellphone applications’ use to reduce cattle milk yield gaps in Africa were identified.•The actual ...contribution of a feed advisory tool to close milk yield gaps was assessed.•Use of a stand-alone smartphone application did not significantly reduce milk yield gaps.•Factors limiting the contribution of some ICTs to close milk yield gaps were discussed.
There are growing expectations that Information and Communication Technology (ICT) applications could help improve on-farm yields amongst smallholder farmers in developing countries, and consequently, food and nutrition security. However, few studies have quantified the actual contribution of ICT applications on farmers’ yields, and these studies predominantly focused on crop production. We assessed the potential of ICT applications to close milk yield gaps among small- and medium scale dairy cattle farmers in Africa. First, we developed a theoretical framework summarizing biophysical and socio-economic constraints that foster milk yield gaps and discussed which constraints can be addressed using ICT applications. Second, using a case study of a feeding advice application for dairy cattle pre-tested with farmers in rural Kenya, we analyzed how much stand-alone the application could contribute to close dairy cattle milk yield gaps. Our findings suggest that ICT applications could help address some existing biophysical and socio-economic constraints fostering milk yield gaps, including data collection for breeding programs, feeding management advice, and facilitating access to markets and capital. Our stand-alone ICT application closed yield gaps by 2 % to 6 % on representative farms. Several factors may explain the limited actual contribution of selected ICT applications to reduce existing milk yield gaps, including the quality of the input data and models used in ICT applications, and more structural constraints that cannot be addressed by digital tools. Therefore, although ICT applications could help address constraints to achieving higher milk yields on dairy farms, a significant contribution to improve yields may only be achieved when conditions surrounding their use are adequate.
Ruminant livestock systems in the (Sub-)Tropics differ from those in temperate areas. Yet, simulation models used to study resource use and productive performance in (sub-)tropical cattle production ...systems were mostly developed using data that quantify and characterize biological processes and their outcomes in cattle kept in temperate regions. Ergo, we selected the LIVestock SIMulator (LIVSIM) model, modified its cattle growth and lactation modules, adjusted the estimation of the animals' metabolizable energy and protein requirements, and adopted a semi-mechanistic feed intake prediction model developed for (sub-)tropical stall-fed cattle. The original and modified LIVSIM were evaluated using a meta-dataset from stall-fed dairy cattle in Ethiopia, and the mean bias error (MBE), the root mean squared error of prediction (RMSEP), and the relative prediction error (RPE) were used to assess their accuracy. The modified LIVSIM provided more accurate predictions of voluntary dry matter intake, final body weights 140 days postpartum, and daily milk yields than the original LIVSIM, as shown by a lower MBE, RMSEP, and RPE. Therefore, using data that quantify and characterize biological processes from (sub-)tropical cattle production systems in simulation models used in the (Sub-)Tropics can considerably improve their accuracy.
The livestock sector in low- and middle-income countries could contribute significantly to reduce the rate of growth and/or the level of greenhouse gas (GHG) emissions required to achieve the 1.5 °C ...target of the Paris Agreement. Yet, the sector is also expected to contribute to food and income security in these countries. Using an extensive dataset on the Ethiopian livestock sector, we assessed the potential of selected interventions to increase supply of animal source protein (ASP) and reduce GHG emissions intensity or absolute emissions at national level. The business as usual (BAU) scenario was modelled by extrapolating the historical trends observed during the base years (i.e. 2010 - 2020) to the period between 2021 and 2030. Four scenarios were modelled including structural changes in cattle herd (S1) and chicken flock (S3) composition, increased milk yields of dairy cattle (S2), and a combination of all strategies (S4). We found that the total ASP produced and supplied per capita, as well as total GHG emissions increased between 2021 and 2030 across all scenarios while emission intensities per unit ASP produced decreased. However, by 2030, the total GHG emissions in S1 (i.e. 146.7 MtCO
2
e) were lower than in the BAU scenario (i.e. 149.0 MtCO
2
e) while the total ASP supplied per capita was higher in the former (i.e. 6.84 kg) than the latter (i.e. 6.24 kg). These findings suggest that structural changes at herd level could reduce total GHG emissions and concomitantly increase ASP supply. Therefore, structural transformation could be a highly relevant policy option for low- and middle- income countries, where the livestock sector must address multiple goals including food and income security, and global climate commitments.
Conservation Agriculture (CA) has been widely promoted in sub-Saharan Africa (SSA) as a sustainable agricultural practice, yet with debatable success. Most authors assume successful adoption, only if ...all three principles of CA are implemented: (1) minimum or zero tillage, (2) maintenance of a permanent soil cover, and (3) integration of crop rotations. Based on this strict definition, adoption has declined or remained stagnant. Presently, not much attention has been given to context-suited adaptation possibilities, and partial adoption has not been recognized as an entry point to full adoption. Furthermore, isolated success cases have not been analysed sufficiently. By applying the QAToCA approach based on focus group discussions complemented by semi-structured qualitative expert and farmer interviews, we assessed the reasons behind positive CA adaptation and adoption trends in Zambia. Main reasons behind Zambia's emerging success are (1) a positive institutional influence, (2) a systematic approach towards CA promotion - encouraging a stepwise adaptation and adoption, and (3) mobilization of strong marketing dynamics around CA. These findings could help to eventually adjust or redesign CA promotion activities. We argue for a careful shift from the 'dogmatic view' on adoption of CA as a packaged technology, towards adapting its principles to the small-scale farming context of SSA.
Abstract
Using empirical models to predict voluntary dry matter intake (VDMI) of cattle across production systems in the (Sub-)Tropics often yields VDMI estimates with low adequacy (i.e., accuracy ...and precision). Thus, we investigated whether semimechanistic conceptual mathematical models (CMM) developed for cattle in temperate areas could be adopted and adjusted to adequately predict VDMI of stall-fed cattle in the (Sub-)Tropics. The CMM of Conrad et al. (1964) (C1) and Mertens (1987) (M1) were identified and adopted for their simplicity in reflecting physicophysiological VDMI regulation. Both CMM use 2 equations that estimate the physiologically and physically regulated VDMI and retain the lower VDMI prediction as actual VDMI. Furthermore, C1 was modified by increasing the daily average fecal dry matter output from 0.0107 to 0.0116 kg/kg body weight, yielding the modified model C2. For M1, the daily neutral detergent fiber intake capacity was increased from 0.012 to 0.0135 kg/kg body weight and the daily metabolizable energy requirements for maintenance from 0.419 to 0.631 MJ/kg0.75 body weight, whereas the metabolizable energy requirements for gain was reduced from 32.5 to 24.3 MJ/kg body weight gain, yielding the modified model M2. Last, also the mean of the physically and physiologically regulated VDMI rather than the lower of both estimates was retained as actual VDMI to generate the models C3 (from C1), C4 (from C2), M3 (from M1), and M4 (from M2). The 8 CMM were then evaluated using a data set summarizing results from 52 studies conducted under (sub)tropical conditions. The mean bias, root mean square error of prediction (RMSEP) and concordance correlation coefficient (CCC) were used to evaluate adequacy and robustness of all CMM. The M4, C2, and C1 were the most adequate CMM i.e., lowest mean biases (0.07, −0.22, and 0.14 kg/animal and day, respectively), RMSEP (1.62, 1.93, and 2.0 kg/animal and day, respectively), and CCC (0.91, 0.86, and 0.85, respectively) and robust of the 8 CMM. Hence, CMM can adequately predict VDMI across diverse stall-fed cattle systems in the (Sub-)Tropics. Adjusting CMM to reflect the differences in feed quality and animal physiology under typical husbandry conditions in the (Sub-)Tropics and those in temperate areas improves the adequacy of their VDMI predictions.
Using empirical models to predict voluntary dry matter intake (VDMI) of cattle across production systems in the (Sub-)Tropics often yields VDMI estimates with low adequacy (i.e., accuracy and ...precision). Thus, we investigated whether semimechanistic conceptual mathematical models (CMM) developed for cattle in temperate areas could be adopted and adjusted to adequately predict VDMI of stall-fed cattle in the (Sub-)Tropics. The CMM of Conrad et al. (1964) (Cl) and Mertens (1987) (Ml) were identified and adopted for their simplicity in reflecting physicophysiological VDMI regulation. Both CMM use 2 equations that estimate the physiologically and physically regulated VDMI and retain the lower VDMI prediction as actual VDMI. Furthermore, C1 was modified by increasing the daily average fecal dry matter output from 0.0107 to 0.0116 kg/kg body weight, yielding the modified model C2. For M1, the daily neutral detergent fiber intake capacity was increased from 0.012 to 0.0135 kg/kg body weight and the daily metabolizable energy requirements for maintenance from 0.419 to 0.631 MJ/kg0.75 body weight, whereas the metabolizable energy requirements for gain was reduced from 32.5 to 24.3 MJ/kg body weight gain, yielding the modified model M2. Last, also the mean of the physically and physiologically regulated VDMI rather than the lower of both estimates was retained as actual VDMI to generate the models C3 (from C1), C4 (from C2), M3 (from M1), and M4 (from M2). The 8 CMM were then evaluated using a data set summarizing results from 52 studies conducted under (sub)tropical conditions. The mean bias, root mean square error of prediction (RMSEP) and concordance correlation coefficient (CCC) were used to evaluate adequacy and robustness of all CMM. The M4, C2, and C1 were the most adequate CMM i.e., lowest mean biases (0.07, -0.22, and 0.14 kg/ animal and day, respectively), RMSEP (1.62, 1.93, and 2.0 kg/animal and day, respectively), and CCC (0.91, 0.86, and 0.85, respectively) and robust of the 8 CMM. Hence, CMM can adequately predict VDMI across diverse stall-fed cattle systems in the (Sub-)Tropics. Adjusting CMM to reflect the differences in feed quality and animal physiology under typical husbandry conditions in the (Sub-)Tropics and those in temperate areas improves the adequacy of their VDMI predictions.
Ruminant livestock systems in the (Sub-)Tropics differ from those in temperate areas. Yet, simulation models used to study resource use and productive performance in (sub-)tropical cattle production ...systems were mostly developed using data that quantify and characterize biological processes and their outcomes in cattle kept in temperate regions. Ergo, we selected the Livestock SIMulator (LIVSIM) model, modified its cattle growth and lactation modules, adjusted the estimation of the animals' metabolizable energy and protein requirements, and adopted a semi-mechanistic feed intake prediction model developed for (sub-)tropical stall-fed cattle. The original and modified LIVSIM were evaluated using a meta-dataset from stall-fed dairy cattle in Ethiopia, and the mean bias error (MBE), the root mean squared error of prediction (RMSEP), and the relative prediction error (RPE) were used to assess their accuracy. The modified LIVSIM provided more accurate predictions of voluntary dry matter intake, final body weights 140 days postpartum, and daily milk yields than the original LIVSIM, as shown by a lower MBE, RMSEP, and RPE. Therefore, using data that quantify and characterize biological processes from (sub-)tropical cattle production systems in simulation models used in the (Sub-)Tropics can considerably improve their accuracy. Simple Summary: Models can play an important role in identifying and filling knowledge gaps related to sustainable resource use in (sub-)tropical livestock production systems. Yet, most simulation models used to study cattle production systems in the (Sub-)Tropics were developed using data that quantify and characterize biological processes of cattle kept in temperate regions, which may reduce the accuracy of predictions. Therefore, we adopted some published data that quantify and characterize biological processes of cattle kept in (sub-)tropical production systems to modify an existing ruminant livestock herd model. Then, the accuracy of predictions of feed intake and productive performance from the original and modified models were evaluated using meta data from (sub-)tropical stall-fed cattle. The modified model predicted voluntary dry matter intake and productive performance more accurately than the original model. Consequently, adopting relevant data that correctly describe the biological processes in (sub-)tropical cattle production systems is the way forward for improving simulation models for these systems. Keywords: cattle; (Sub-)Tropics; dry matter intake; model accuracy; ruminants; fiber
•Metropolitan dairying is vital for milk supply in the Global South.•Feed scarcity drives farmers to use lake fodder and food leftovers for their cows.•There is a risk of regular intake of heavy ...metals from uncontrolled feed sources.•While considerable heavy metal concentrations are found in cattle feces, cow milk is safe.•Regulating lake fodder use and informing farmers about contamination risks can reduce the latter.
Milk consumption plays a pivotal role in human nutrition, especially for children, due to its high protein, calcium, and vitamin contents that aid cognitive development. Nonetheless, potential hazards from heavy metal presence in milk, due to environmental exposure and intake through feed, have gained global attention. Given paucity of data we investigated the seasonal variation in the quantitative intake of heavy metals (cadmium: Cd, chromium: Cr, lead: Pb) by dairy cows in the South Indian megacity of Bengaluru, and the resulting heavy metal concentrations in milk and feces. The research involved 39 dairy farms across urban and peri‑urban areas, where 281 feed, 329 milk, and 183 feces samples were collected along with management data during monsoon (July 2020 - August 2020), winter (November 2020 – February 2021) and summer (March 2022 – June 2022) seasons. During the summer season, Cd concentration in lake fodder averaged 1.6 mg/kg DM, which was higher than the 0.6 mg/kg DM during winter and 0.9 mg/kg DM during the monsoon season. The higher concentration resulted in an increased daily Cd intake of 13.7 mg/cow/day. Similarly, Cr and Pb intake were also highest in summer (Cr: 46.3 mg/cow/day, Pb: 11.7 mg/cow/day), whereby for both elements the concentrations were higher in non-lake feed than in lake fodder. With the exception of a few milk samples, Cr and Pb concentrations were below threshold levels and did not compromise milk safety, and Cd levels never raised concerns. Nevertheless, elevated Cr and Pb concentrations (Cr: 9.8–16.9 mg/kg DM, Pb: 4.1–10.6 mg/kg DM) were determined in feces across seasons, potentially endangering the environment and reintroducing these elements into the food chain through manure application on agricultural land. Rather than uniformly discouraging the use of lake fodder in Bengaluru, authorities on the one hand should caution fodder usage in pollution-prone areas, and on the other hand take measures to reduce contamination levels at those sites. Furthermore, it appears necessary to implement more stringent control measures for other types of feedstuffs. Raising farmers' awareness of the problem of heavy metals in the food chain could promote their compliance with regulations without the need for complete feed bans, which are difficult to enforce in a highly contested urban environment.
•The coupled MPMAS-LUCIA-LIVSIM model framework (MLL) is presented.•Established models have been amended with herd movement and management routines.•MLL simulates soils, plants, animal herds and ...human decisions at landscape scale.•MLL is designed to simulate herd management and its ecological impact in savannas.•The study applies MLL in a 20-year simulation in the Borana region, Ethiopia.
Increasing livestock densities on limited grazing areas in African savannahs lead to resource degradation through overgrazing, aggravated by drought. Assessing herd management strategies over longer periods at landscape scale is important to propose options for sustainable land use. This requires an understanding of processes related to hydrology, nutrient cycling, herd movement, pasture degradation, and animal resilience that involve dynamic soil-plant-animal interactions and human decisions about stocking rates, livestock purchases and sales.
We present the coupled model system MPMAS-LUCIA-LIVSIM (MLL), the combination of a spatially explicit agent-based model for human decision-making (MPMAS), a spatially distributed landscape model for water flows, nutrient cycles and plant growth (LUCIA), and a herd model (LIVSIM) representing grazing, body weight, nutrition and excreta of individual animals. MLL represents daily vegetation growth in response to grazing and organic inputs, monthly animal performance influenced by forage availability and quality, and herders’ management in response to resource status. New modules for selective grazing, resprouting of pasture, herd movement and model coupling were developed for MLL.
The test case of a pastoral system in the Ethiopian Borana region demonstrates the capabilities of MLL to simulate key soil-plant-animal-human interactions under climate-related management scenarios with varying access to grazing land, changing cattle prices and different spending / saving behaviour of herders. 20-year simulations showed the negative impact of consecutive drought years on vegetation biomass, on herd development and movement and how reserving grazing areas for dry seasons could mitigate overgrazing and improve income. Seasonality and drought response of vegetation growth, selective grazing of different plant parts, resprouting after grazing, calving intervals, milk yields and lactation in response to forage supply and quality as well as herder reactions to shocks were plausibly represented.
Building upon this successful proof-of-concept, MLL can be used to identify robust management options for improved grazing systems in savannahs in follow-up research.
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