Feed Supply – Demand Databases as Decision Making Tools for Prioritizing Livestock Interventions to
Close Yield Gaps and Reduce Negative Environmental Foot Prints
Michael Blϋmmel-1*, Amare Haileslassie- 2, Anandan Samireddypalle-3, Mario Herrero-4, Y Ramana Reddy-5
and Dianne Mayberry-4
-1
International Livestock Research Institute (ILRI) PO Box 5689, Addis Ababa, Ethiopia
-2
International Water Management Institute (IWMI), PO Box 5689, Addis Ababa, Ethiopia
-3
International Livestock Research Institute (ILRI), c/o IITA, Ibadan, Nigeria
-4
Commonwealth Scientific and Industrial Research Organization, St Lucia, Australia
-5
International Livestock Research Institute (ILRI), c/o ICRISAT, Patancheru 502324, India
Abstract
Feed data bases describing feed supply - demand scenarios are important tools for researchers,
development practitioners and private sector for example to gauge opportunities and limitations for
increasing livestock production and to obtain information about potential feed surplus and deficit areas.
The two pillars of such feed data bases are assessment of quantitative and qualitative feed availability to
calculate present feed supply and livestock census data (livestock population, species composition, herd
structure, productivity levels) to estimate feed demand. The present paper proposes, and demonstrates,
that such feed supply-demand data bases can be further developed into decision making tools to
prioritize and compare various interventions for increasing livestock production and productivity. For
example feed-based interventions can be compared with herd-based interventions around animal
species, breed and reproduction and the possible interdependence of interventions can be explored and
1
modeled. In addition the implications of choices of interventions on environmental foot prints
particularly water requirements and greenhouse gas emissions can ultimately be estimated by such
tools. The paper presents example of how feed supply can be linked to water requirements based on
the variables: 1) reference evapo-transpiration (ETO) calculated from temperature, wind speed,
humidity and rainfall, 2) crop specific coefficient derived from crop phenology (Kc); and 3) length of
growing period (LGP). Huge differences were observed in the water use efficiencies of classes of feeds
but also among the same feeds when sourced from different districts.
Key words: Feed data base, Decision making tool, Livestock interventions, Yield gaps, Environmental foot
print
-----------------------------------------------------------------------------------------------------------------------------------------*Corresponding author (e-mail: m.blummel@cgiar.org)
Background
Feed resourcing and feeding are key issues in livestock production because feed costs largely determine
the economic benefits from the production of Animal Soured Food (ASF). Feed costs relative to ASF price
are increasing and checking and reversing this trend will be paramount for the survival of livestock
producers (IFIF, 2012). In addition, feed resourcing and feeding is at the very interface where positive
and negative effects from livestock are negotiated and urgent attention needs to be given for example
to water requirement for feed production (Blümmel et al., 2014). Singh et al. (2004) has drawn attention
to the sometimes surprisingly high water needs for extensive dairy production in India, calculating that
in the state of Gujarat, the heartland of the ‘white revolution’ (widespread increase in milk production),
an average of 3.4 m3 of water are required for the production of 1 kg of milk. The global average is 0.9
m3 and the authors’ traced high water needs to feed resourcing and production, concluding, based on
life cycle analysis, that on average 10 000 litre of water were required to produce the daily feed for one
2
single dairy animal. Conventionally the relationship between livestock and water is associated with
drinking water requirements and the fact that much more water is required for evapo-transpiration in
feed production still escapes the awareness of many livestock nutritionists. They rarely include water
requirement assessments in feed resourcing and ration design. Finally while many small holder will not
be able, or prepared, to invest in intensifying ASF production, they will still have livelihood options
around feed and fodder production such as forages as cash crops, fodder marketing and small scale feed
enterprises. In short, ample reason exist why feed production, feed resourcing and feeding as the nexus
largely determining beneficial and adverse effect of livestock should be given special attention in the
realm of livestock research and extension as well as natural resource management.
Feed supply – demand scenario as starting point
However, well-informed decision making in the area of feed production, feed resourcing and feeding
requires a good understanding of actual feed supply – demand scenarios. While this appears to be
common sense, few structured feed – demand supply scenarios actually exist (FAO, 2012). Through
coordinated central government and state efforts, India has systematically quantified feed and fodder
resources, building up a data base from district level (NIANP, 2003). Feed sources were classified into
greens (subclasses: cultivated fodder, grass from grazing, forests, fallows etc.), crop residues (subclasses:
coarse and fine cereal residues, leguminous residues) and concentrates (subclasses: grains, cakes, bran,
chunnies). Subclasses from crop residues (CR) are further differentiated to list the contributions from
specific crops (NIANP, 2003). Livestock population (from livestock census) and productivity data were
used to calculate feed demand factorially that is for maintenance and production. It is important to
realize that the data base was constructed essentially from available secondary data sets. For 2003 it
was calculated (Blümmel et al., 2014) that on a dry matter basis, crop residues were the single most
important fodder resource contributing about 71% to the overall feed resources. . Green fodder from all
sources contributed about 23% and the area under planted forage was rather stagnant during the last
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two decades, increasing merely from 297 120 000 tons in 1986 to 303 269 000 tons in 2003... Fodder
from common property resources, forests, pastures and fallow lands constituted less than 16% of the
available fodder. Also notable was that concentrates represented a very low proportion (< 7%) of the
available feed resources, and there was no indication of any rapid increase in their use. The countrywide
gap between feed demand and supply was minor for feed quantity (dry matter) but was large with
regards to feed quality, since the estimated annual feed dry matter deficit was only 6% while digestible
crude protein and total nutrients were estimated to fall short by 61 and 50%, respectively (NIANP 2003;
Blümmel at al., 2014).
The various components of the NIANP (2003) feed data base are presented in Figure 1. Feed supply is
calculated from cropping and land use pattern and various conversion factors such as harvest indices,
milling and oil extraction efficiencies and biomass yield estimates for rangelands, common property
resources (CRP) and so on. Feed demand is derived from livestock census and productivity assumptions
about weight gains, milk production etc. and by using standard animal nutritional relationships.
Figure 1: Structure, components and functionality of the NIANP Feed Data Base (Adopted from Blümmel
et al., 2014)
Crop data
Land use
Production of cereals,
pulses, oilseeds
& others
Harvest index
Livestock census
Area under CPR,
forests, pastures, fallows
etc.,
Extraction rates
Productivity
Cattle, buffalo. sheep. goat,
equines, camels, yak, mithun ,
pigs, commercial poultry,
Biomas s
productivit
wise and
Numbers age
y
Potential feed resources available
Crop
residues
Protein
Concentrates
Green
Feed requirements
Crop
residues
Energy
Protein
Concentrates
Greens
Energy
Adequate
Surplus
Deficit
Feed balance
4
The information which are presented in an aggregated form in Figure 1 are actually quite detailed when
disaggregated as evident from Table 1 where the level of detail available on feed supply is high,
extending in essence from the major feed and fodder categories such as crop residues, concentrates and
greens down to individual crop level. Feed availability would be in tons aggregated to district level.
Similarly even (on a national scale) minor animal species such as Mithun, Yak etc. are registered. More
importantly, classification according to sex, age, purpose and productive status, in other words herd
structures, are detailed (Table 1). Feed requirements are calculated factorially from maintenance
requirements and production and are expressed on dry matter basis (where intake is estimated by
constants based on body weight), digestible crude protein (DCP) and total digestible nutrients (TDN).
The feed data base as it is conveys a range of information to the user. On the balance side i.e. does feed
supply meet feed demand, the user can see if available feed biomass meets intake demand, and if DCP
and TDN meet nutrient demand, or not. Degree of surplus or deficit in any of these three variables
would be listed. Policymaker, development agent and private sector can use the information to decide
for example on investments for and location of livestock value chains and opportunities for spatial feed
transactions from surplus to deficit areas and so on. In addition, decision can be made about on specific
feed interventions. For example knowledge of the proportional contributions of major feed categories
(crop residues, concentrates and greens) to overall feed resources will help identifying strategic entry
points for feed improvement. For example if crop residues provide major feed resources, investment in
their upgrading by plant breeding and physical-chemical-biological treatment seems sensible. If maize is
the major crop focusing on maize stover will be advisable. Along similar lines if feed biomass quantity is
below voluntary feed intake level, investment in increasing feed quantity will be beneficial. In contrast,
where feed biomass availability is adequate but DCP and TDN are lacking, investment in improving feed
quality will have priority. This can be taken further and where for example TDN is sufficient but DCP is
lacking, investment in protein sources will have priority (and of course vice versa). Obviously a critical
5
interface effecting all these conclusion and ultimately decisions is voluntary feed intake (VFI). The data
base as it is uses constants based on live weights to calculate estimate VFI, which in the final analysis is
probably too much of an oversimplification, even when allowing for the need for simplicity in feed
resource – supply demand scenarios. We therefore suggest use VFI predictions based on feed and
fodder quality traits. As a further modification we suggest to use metabolizable energy (ME) instead of
TDN since we think that ME expresses the impact of energy related feed quality more clearly than does
TDN and provides a better interface for matching feed quality and animal requirement.
6
Table 1: Main and sub categories of feed stuffs and animal species and herd structures used in the feed
data base of NIANP (2003).
FEED COMPONENT
ANIMAL COMPONENT
Main Category
Sub Category
Crop
Species
Herd Structure
+ Cop residues
+ Fine straw
+ Concentrate
+ Coarse straw
Sorghum
+ Cattle
CB Male: < 1 y
+ Greens
+ Legume straw
Pearl millet
+ Sheep
CB Male: 1-3 y
Maize
+ Goat
CB Male: Breeding
Oats
+ Yak
CB Male: Agriculture
Ragi
+ Mithun
CB Male: Bullock
Small millet
+ Horse
CB Male: Other
Other cereals
+ Mules
CB Female: < 1 y
Mandua
+ Donkey
CB Female: 1-3 y
Kodo
+ Camel
CB Female: In milk
Kakoon
+ Pig
CB Female: Dry
+ Poultry
CB Female: Heifer
+ Buffalo
CB Female: Other
IN Male: < 1 y
IN Male: 1-3 y
IN Male: Breeding
IN Male: Agriculture
IN Male: Bullock
IN Male: Other
IN Female: < 1 y
IN Female: 1-3 y
IN Female: In milk
IN Female: Dry
IN Female: Heifer
IN Female: Other
7
Framework to combine feed resources data base and water input requirement estimates
Estimation of water investment in livestock feed production is data intensive and complex (Haileslassie
et al., 2011). Compounding factors are for example multiple use of agricultural water, limited knowledge
of water productivity on natural pasture, CPR, forest, extent of use of plant biomass for feeding etc.
However, the major challenges in estimating feed-mediated livestock water productivity lies in defining
and describing feed resources and feed usage rather than in estimating water depletion. The feed data
base tool as conceived and constructed by NIANP (2003) and described in the previous chapter provides
a very good starting point. It simply needs to be connected and combined with modules that estimate
total evapotranspired water per hectare land-use. Key parameters and variable required are: 1)
reference evapo-transpiration (ETO) calculated from temperature, wind speed, humidity and rainfall, 2)
crop specific coefficient derived from crop phenology (Kc); and 3) length of growing period (LGP) see
also Figure 2. Specifications and tools to process and compute effective rain fall and evapo-transpiration
are well established (e.g. Allen et al., 1998; FAO, 1998). Data used as input into these tools comprises
rainfall and reference evapo-transpiration (ET0) or detailed climatic parameters to compute ET0. Many
countries have a good density of climatic station network that, with relatively straightforward
processing, can provide the necessary input data.
There exist freely available global datasets containing a variety of climate variables. The WorldClim
(http://worldclim.org/), and CCAFS (http://www.ccafs-climate.org/data/) data portals and local climate
estimator [LocClim (FAO, 2005)], for example, contain information for long term averages. Local
relevance and spatial resolution remain important challenges. Cross-checking with the above-mentioned
climate station data is thus important. The evaporation power of the atmosphere is expressed by the
reference crop evapo-transpiration (ETo). The reference crop evapo-transpiration represents the evapotranspiration from a standardized vegetated surface. This needs crops specifications, like crop
coefficients, stress resistance factors, rooting depth, which are available also for major crops from Allen
et al. (1998) or already incorporated into these tools: selection of crop of interest suffices to capture
these crop specific values. Validation of these for the local circumstances will be important. Soil data are
also important inputs to these tools. CropWat, for example, requires a very simplified soil type in terms
of its structure (FAO 1998) to compute soil water. Information such as FAO’s global soil map or ISRIC’s
Harmonized World Soil Database can be also explored to capture such soil information. With increasing
information technology and worldwide data networking, the opportunities to use relevant and good
quality global or regional data sets are likely to increase.
8
Climatic and water data
Land use land
cover (ha) as
structured in
Table 4
— Min and MaxTemperature ( oC)
— Humidity (%)
— Rain fall
— Wind speed ( km day-1)
— Sunshine (hrs day-1)
— Radiation (Mj m-2 day-1)
— Volume of water per
irrigation and number of
irrigation
Crops and soil
parameters
— Soil type and structure
— Crop types and
management practices (
food and fodder crops)
— Length of growing period
for different stages of
development
— Soil types
Conversion factors,
HI, feed use factor (as
structured in Table 4)
Examples of tools and
procedures
— Budget (Raes et al.,
2006)
— CropWat (FAO 1998;
Allen et al., 1998)
Total evapo-transpired
water by feed sources
type (m3 ha-1)
Feed Dry Matter (kg m-3)
Feed resources by types
-1
(Kg ha )
Figure 2: A simplified framework to combine feed resources data base and water input requirement estimates (Blümmel et al., 2014)
9
This approach was initially applied to estimate water requirement for the three major classes of feeds
(NIANP, 2003) crop residues, concentrates and greens in four districts in the state of Karnataka in India,
Bijapur, Tumkur, Raichur and Chikmagalur. As shown in Table 2 crop residues were the most and greens
the least water-use efficient feeds with concentrates taking an intermediate position. The water
invested in crop production serves the grain and residues (Haileslassie et al., 2009). In order to
understand the water productivity of enterprises at household or system scale, partitioning the total ET
water between feed and grain is important (Haileslassie et al., 2009). This way of allocating water is
however only one approach others use economic criteria for the allocation of water for example the
cost ratio crop by-products to grain (e.g. Singh et al., 2004). Here we assumed that the water used to
produce a unit of grain and residue are equal and consequently used harvest indices to partition total ET
in crops. However, total ET was allocated to greens.
Table 2: Water use efficiency of different classes of feeds across different districts
District
Litres of water required to produce 1 kg of
Crop Residues
Concentrates
Greens
Bijapur
1303
2300
3427
Tumkur
1177
1589
3291
Raichur
1825
2108
3770
Chikmagalur
633
1140
3235
Generally the greens in the study areas came from forests, wastelands, permanent pasture, fallows etc.
The yield for these different land use units as estimated by NIANP (2003) ranges from 1-5 tons. The
highest yield is for permanent pasture and grazing lands and the lowest yield for fallow and wasteland.
Greater area under the latter land use and the aggregated biomass yield is therefore low. This factor will
contribute to the low water use efficiencies for greens in producing a unit of biomass.
More work needs to be done to understand the factors that contribute to variations in water use
efficiency within a specific feed across different sites (Table 3). While the overall ranking in water use
efficiency i.e. sorghum stover > groundnut haulm > pearl millet forage is similar to that found for classes
of feeds in Table 2. However water use efficiency for sorghum stover and groundnut haulm could vary –
dependent on the district, by about 100% (Table 3). Key variable for the calculation of water use
efficiency was reference evapo-transpiration (ETO) calculated from temperature, wind speed, humidity
10
and rainfall, the crop specific coefficient derived from crop phenology (Kc), length of growing period
and of course biomass yield (see also Figure 2). Future work should investigate and model the
proportional importance and relevance of these different variables. While biomass yield will likely be a
key factor determining water use efficiency, better understanding of the co-factors will be important for
designing water use efficient feeding strategies.
Table 3: Water use efficiency of different representatives of classes of feeds across different districts
District
Litres of water required to produce 1 kg of
Sorghum stover1
Groundnut cake2
Pearl millet forage
Bijapur
765
1088
2928
Tumkur
932
1713
3001
Raichur
1924
2414
3220
Chikmagalur
694
714
2196
In any event by linking information contained in Table 1 with process outlined in Figure 2 it will be
possible to establish a causal relationship that associated feed production with water requirements and
ultimately feed mediated water requirements for the production of ASF.
Using feed data bases to prioritize interventions to close yield gaps in dairy production and
productivity in India
Livestock play an important role in the livelihoods of smallholder farmers but production and
productivity of ASF such as milk and meat is generally low, and often below the genetic potential of the
animals. However, there are huge farmer to farmer variations as evident in Figure 3. Reason for the
higher milk yields realized by top farmers and strategies to increase milk and meat production and
productivity include: improving nutrition of individual animals to increase growth rates, daily milk yields
and length of lactation; altering the local herd structure by culling unproductive animals; and replacing
indigenous livestock breeds with higher-producing exotic breeds and crossbred animals. (Interestingly
11
even with a breed milk yields between the 10% best farms and the rest varied by almost threefold
(Figure 3) suggesting that in this instance genetics of the animals was not the primary constraints.
Figure 3: Milk production levels of the 10% top farmers (left bars) versus milk levels of the remaining
90% farmers (right bars) in different states of India (VDSA Survey 2013).
Aforementioned strategies may or may not require an increase in the amount and/or quality of feed
resources. The key question here will be if replacement of lower producing animals by higher producing
ones will result in an overall reduction of livestock numbers. If so feed requirement will decrease
because of a shift of feed use from maintenance to production. For example Blümmel et al. (2013)
calculated that, relative to the production of 81.8 million tons of milk in India in 2005, feed requirement
in terms of ME could be reduced by 27 and 41% if average across herd daily milk production would
increase from 3.6 to 6 and 9 kg, respectively, when accompanied by a correspond decrease in number of
dairy animals. That is the same amount of milk - 81.8 million tons – would be produced from fewer
animals. This could be achieved with reallocation of existing feed resources i.e. without increasing feed
quality (Tarawali et al., 2011). In any event, due to competing land uses (e.g. cropping, industry and
urbanization) there are limited opportunities to increase feed and fodder production.
We present here feed supply – demand approach currently used in a project funded by the Bill and
Melinda Gates Foundation (BMGF) to identify opportunities to increase dairy and meat production in
12
India within the constraints of current feed resources. This case study that looks at dairy production in
the irrigated agricultural zone of the state of Bihar is used as an example in this paper. Bihar is a high
priority state for many donors and other development actors. The described feed data base of NIANP
(2003) contained unfortunately no updated Bihar data so more recent data about Bihar were distilled
from Ministry of Agriculture (MoA 2014a, MoA 2014b) to come up with the kind of information
contained in Table 1.
Based on animal numbers and average daily milk yields taken from the 2012 livestock census (Table 4;
MoA 2014a, MoA 2014b), average daily milk production in Bihar was estimated to be 19.7 thousand
tons. This equated to approximately 190 g milk per person per day. The daily and annual energy
requirements for cattle and buffalo to maintain this level of production were estimated using equations
from AFRC (1993). These equations account for animal live weight, sex, activity, amount of milk
produced and milk fat content. The amount of energy required in livestock feed was greater than that
produced locally from crop residues, greens and concentrates, so it was assumed that additional feed
resources were sourced from outside of Bihar. This highlights one of the limitations of a localized feed
supply – demand scenarios namely that while livestock population and crop production data are at
district and state level, feed resources often move across boundaries and may even be imported from
overseas. Values for the amount of feed resources imported from other areas (or exported from Bihar)
were difficult to obtain. We assumed that enough feed was available to support the current level of
dairy production, and used a spreadsheet-based calculator to investigate how milk production could be
increased without increasing the total demand for feed. This necessitated a decrease in the total
number of dairy animals.
Table 4: Total number of cattle and buffalo in Bihar state, number of lactating animals and average milk
yields. Data is from 2012 cattle census (MoA 2014a).
Animals
Lactating Animals Average milk yield (kg/head/day)
(millions)
(millions)
All zones
Irrigated zones
Indigenous cattle
8.76
1.74
2.9
4.3
Crossbred cattle
3.48
1.09
6.1
8.1
13
Buffalo
7.57
2.05
4.0
6.1
Results suggest that reasonable increases in milk production at a state level can be made by reducing
the total number of local cattle and better feeding the remaining animals (Table 5). While this may be
considered a risky or undesirable strategy at an individual farmer level, movement of people out of the
agricultural sector means that it is not implausible. If this strategy was accompanied by culling of mature
male cattle, production could be increased even further. Male cattle have little value to farmers except
as draught animals, and nationally, there was a 19% decrease in the number of male cattle and buffalo
between 2007 and 2012 (MoA 2014a). This may indicate a shift away from the use of draught animals
towards mechanized agriculture and an increased focus on dairy production. Improving production of
crossbred cattle gave smaller increases because they comprise only a small portion of the current
livestock population. Larger gains could be made from replacing indigenous cattle with crossbred cattle
or buffalo, especially if they were fed better to achieve higher daily milk yields. Replacing 50% of local
cattle with buffalo or crossbred cattle and increasing individual animal milk yields through better feeding
would increase milk availability to around 250 g per person/day.
Table 5: Examples of interventions to increase milk production in Bihar, assuming a constant level of
feed energy available. Baseline milk production estimates are based on animal numbers from
the 2012 livestock census and reported average milk yields (MoA 2014a; MoA 2014b). Animal
equivalents are based on a 350 kg cow.
Scenario
Animal equivalents Milk/day
Milk per person
(millions)
(thousand t)
(g/day)
Baseline
13.1
19.7
189
Replace 50% local cattle with buffalo
13.0
20.7
199
Increase average milk production from 13.0
21.5
207
crossbred cattle
14
Increase average milk production from 13.0
22.0
211
22.5
216
23.1
222
23.2
223
25.0
240
25.8
248
25.8
248
26.5
255
indigenous cattle
Change population structure of local cattle 13.0
herd (less mature males)
Replace 50% local cattle with crossbred 13.1
cattle
Increase average milk production from 12.7
buffalo
Increase average milk production from 12.6
crossbred cattle and buffalo
Replace 50% local cattle with buffalo + 12.5
increase buffalo milk production
Change population structure of local cattle 12.8
herd + increase milk production
Replace 50% local with crossbred cattle + 12.8
increase crossbred cattle milk production
In this example, use of a feed supply – demand tool provided clear guidelines on where the BMGF and
other organizations could target programs to increase dairy production. Our analysis indicated that the
biggest advantages could be achieved by targeting farmers that currently keep local cattle breeds –
reducing animal numbers and increasing individual levels of nutrition, followed by a transition from local
cattle to buffalo and crossbred cattle. However, it should be noted that the accuracy of results from the
feed balance exercise are limited by the accuracy of data on livestock populations and feed resources.
While India conducts a regular livestock census, similarly detailed livestock data is not available for all
countries, especially in the developing world. In regards to feed resources, in addition to accurate
reporting of grain yields, there is an assumption that factors used to estimate availability of stovers,
chunnis and other livestock feeds based on grain production are sound, but this will be highly
15
dependent on local crop management, harvest and storage practices. Estimating the amount of green
feed available from common lands, wasteland, fallows, forests and cultivated forages is also challenging.
However many of above criticized assumptions and constants can be regarded as placeholder to be
replaced once better input data are available. In other words, a conceptually sound frame work is more
important than specific and currently perhaps inadequate input data. In this context it is important to
understand that the scenarios presented in Table 4 and 5 are essentially based on the kind of
information presented in Table 1. These information combined with process outlined in Figure 2 will
ultimately yield feed mediated water requirements for different scenarios of production of ASF as
compared in Table 4 and 5.
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