El Fire Weather Index (FWI)

In all derived models, the driving forces were similar whatever the species (sheep vs. cattle), nutritional or physiological (non-productive, growing, lactating, gestating) state of the animal. Models could then be applied to all ruminants, only the numerical value of a few parameters sometimes differed.

Net splanchnic release of ketogenic nutrients

Splanchnic release of ketogenic nutrients could be predicted (Loncke et al., 2008a, Loncke, 2009; Table 2) from two major dietary predictors: the rumen fermentable organic matter (RFOM) intake and its content in rumen digestible neutral detergent fiber (RdNDF) plus an animal predictor: the energy balance of the animal, for BHBA.

Net splanchnic release of acetate was predicted from its net portal appearance (NPA). As for the two other major VFA, propionate and butyrate,the NPA of acetate was predicted from a combination of two prediction equations: first, the prediction of total VFA-NPA from RFOM intake, and second the prediction of the pattern of portal VFA from the RdNDF concentration in RFOM. Across the liver, the release of acetate responded mainly to mass action law, as no simple relationship could predict endogenous acetate release which accounted for 0 (non productive and gestating adults), 10 (growing) and 24 (lactating) % of NPA. Net splanchnic release of acetate could also be predicted from RFOM intake only but with a lower adjustment and dietary NDF as an interfering factors. The low net splanchnic release of butyrate was predicted from RFOM intake and the RdNDF concentration in RFOM. The equation combined the prediction of butyrate-NPA and of its net hepatic uptake averaging 75% of incremental butyrate-NPA.

The net splanchnic release of BHBA was based on RFOM intake, the RdNDF concentration in RFOM as well as the energy balance of the animals. Its prediction combined that of BHBA-NPA which depends on acetate and butyrate-NPA, and of its net hepatic release (Loncke et al., 2008a). Basically, the equation combined two distinct responses. The first one applies to animals in positive

Table 2. Dietary and animal predictors of the net splanchnic release of energetic nutrients in ruminants (Loncke, 2009).

Nutrient Predictors Dietary interfering

factors Mean difference predicted-observed % of observed Ketogenic nutrients

Acetate RFOMI, RdNDF/FOM

Or RFOMI1 none_{dietary NDF} 2.7_3.4

Butyrate RFOMI, RdNDF/FOM1 _{none 2.7}

ΒHBA RFOMI, RdNDF/FOM, EB1 _{none 6.3}

Glucogenic nutrients

Propionate RFOMI, RdNDF/RFOM1 _{none 8.0}

L-lactate Rd Starch, RFOMI,

RdNDF/FOM1 _{none 20}

Or Rd Starch, EB1 _none

α-amino-N PDIE1 _{dietary N content 10}

Glucose Rd and Id Starch, RFOMI,

RdNDF/FOM, PDIE1 _{none 14}

RFOM(I): rumen fermentable organic matter (intake). RdNDF: rumen digestible neutral detergent fibre. Id Starch: intestinal digestible starch.

Rd Starch: rumen digestible starch.

PDIE: protein digestible in the intestine, as limited by the energy supply. EB: energy balance.

energy balance where net BHBA release is tightly driven by the dietary supply of precursors. The second one applies to animals in negative energy balance where mobilised body fat becomes the major source of precursors of BHBA. The physiological state of the animal modified the parameters associated to the energy balance term, indicating that fatty acids are not used to the same extent in fat-mobilising animals.

Net splanchnic release of glucogenic nutrients

Splanchnic release of glucogenic nutrients could be predicted (Loncke, 2009; Loncke et al., 2010a; Table 2) from a complementary and wide range of dietary predictors: the RFOM intake and its content in RdNDF, ruminally vs. intestinally digested starch and protein digestible in the small intestine as limited by energy (PDIE), and in some cases energy balance.

The low net splanchnic release of propionate was predicted from RFOM intake and the RdNDF concentration in RFOM. The equation combined the prediction of propionate-NPA and of its net hepatic uptake averaging 91% of incremental propionate-NPA.

The net splanchnic release of L-lactate could be predicted either from RFOM intake and the RdNDF concentration in RFOM, themselves predictors of propionate and L-lactate-NPA, or from rumen digestible starch (to predict L-lactate-NPA) and energy balance (able to predict both net hepatic uptake and release of L-lactate). These predictions will still require improvement to solve the remaining high discrepancy between predicted-observed values.

The, net splanchnic release of α-amino-N was directly derived from PDIE intake, expressing the fact that the mass action law is the first driving force of the net hepatic uptake of α-amino-N. It should be made clear that this equation was strictly established to estimate average gluconeogenic amino acid use in the liver, and will definitely need revision to address fate of individual amino acids. The net splanchnic release of glucose was then predicted from the glucose-NPA and hepatic supply in gluconeogenic precursors of either dietary or endogenous origin as detailed in the present symposium (Loncke et al., 2010a). This work clearly showed that in growing, finishing or non productive ruminants, net glucose release was directly related to the sum of all potential precursors. Precursor carbons for glucose synthesis were missing only for dairy cows.

Push versus pull type criteria

Two types of prediction criteria of net splanchnic fluxes of nutrients were thus identified. Predictors of nutrient-NPA were under the only control of dietary supply, thus driven by push-type regulations with no interfering factors. However for the liver, both types of predictors were significant. When hepatic metabolism of nutrients responds directly to a mass action law, then the NPA of the nutrient of interest or of its precursors were the best predictors. However, when hepatic metabolism is regulated by an intricate balance between nutrient supply and demand, the energy balance of the animal was an additional appropriate predictor of net hepatic flux. In fact, calculated energy balance was a relevant indicator of the supply of endogenous precursors to the liver, considering the scarcity of quantitative information especially on long chain fatty acid or glycerol. In this respect, NPA and energy balance can both be considered as predictors of nutrient fluxes driven by mass action law. However, it is in these later situations that prediction equations depended most on physiological status of the animals, reflecting additional intricate metabolic regulations and coordination between tissues for nutrient use.

Evaluation of prediction equations

Present models have first been extensively evaluated by comparing the derived predictions with other predictions obtained on related variables (e.g. amount and profile of VFA produced in the rumen) from other databases, characterised according to the same INRA dietary criteria. These evaluation steps have already been published (Nozière et al., 2010a,b).