Spirulina (Spirulina platensis) algae supplementation increases microbial protein production and feed intake and decreases retention time of digesta in the rumen of cattle

Cattle consuming pastures low in protein have low liveweight gain due to low rumen degradable protein (RDP) supply and thus low microbial crude protein (MCP) production and efficiency of MCP production [EMCP, g MCP/kg digestible organic matter (DOM)]. Nitrogen supplements can increase MCP production and EMCP of cattle grazing low protein pastures. The objective of this study was to compare the effects of supplementation with a non-protein-N source (NPN), in this case urea and ammonium sulfate (US), with a single-cell algal protein source (Spirulina platensis), on intake, microbial protein supply and digestibility in cattle. Nine cannulated Bos indicus steers [initial liveweight 250.1 ± 10.86 (s.d.) kg] were fed Mitchell grass hay (Astrebla spp; 6.1 g N, 746 g NDF/kg DM) ad libitum and were supplied with increasing amounts of US (0, 6, 13, 19 and 33 g US DM/kg hay DM) or Spirulina 0, 0.5, 1.4, 2.5 and 6.1 g Spirulina DM/kg W.day in an incomplete Latin square design. The response of MCP production and EMCP to increasing amounts of the two supplements was different, with a greater response to Spirulina evident. The MCP production was predicted to peak at 140 and 568 g MCP/day (0.64 and 2.02 g MCP/kg W.day) for the US and Spirulina supplements, respectively. The highest measured EMCP were 92 and 166 g MCP/kg DOM for the US and Spirulina treatments at 170 and 290 g RDP/kg DOM, respectively, or a Spirulina intake of 5.7 g DM/kg W.day. Increasing RDP intake from US and Spirulina resulted in an increase in Mitchell grass hay intake and rumen NH3-N concentration and reduced the retention time of liquid and particulate markers and digesta DM, NDF and lignin in the rumen with greater changes due to Spirulina. Total DM intake peaked at a Spirulina supplement level of 4.6 g Spirulina DM/kg W.day with a 2.3-fold higher DOM intake than Control steers. Rumen NH3-N concentrations reached 128 and 264 mg NH3-N/L for the US and Spirulina treatments with a significant increase in the concentration of branched-chain fatty acids for the Spirulina treatment. The minimum retention time of liquid (Cr-EDTA; 23 and 13 h) and particulate (Yb; 34 and 22 h) markers in the rumen were significantly lower for Spirulina compared with US and lower than unsupplemented animals at 24 and 34 h for Cr-EDTA and Yb, respectively. Spirulina could be provided safely at much higher N intakes than NPN supplements. The results suggest that, at an equivalent RDP supply, Spirulina provided greater increases than US in MCP production, EMCP and feed intake of Bos indicus cattle consuming low protein forage and could also be fed safely at higher levels of N intake.

An improved simulation model to predict pre-harvest aflatoxin risk in maize

Aflatoxin is a potent carcinogen produced by Aspergillus flavus, which frequently contaminates maize (Zea mays L.) in the field between 40° north and 40° south latitudes. A mechanistic model to predict risk of pre-harvest contamination could assist in management of this very harmful mycotoxin. In this study we describe an aflatoxin risk prediction model which is integrated with the Agricultural Production Systems Simulator (APSIM) modelling framework. The model computes a temperature function for A. flavus growth and aflatoxin production using a set of three cardinal temperatures determined in the laboratory using culture medium and intact grains. These cardinal temperatures were 11.5 °C as base, 32.5 °C as optimum and 42.5 °C as maximum. The model used a low (≤0.2) crop water supply to demand ratio—an index of drought during the grain filling stage to simulate maize crop's susceptibility to A. flavus growth and aflatoxin production. When this low threshold of the index was reached the model converted the temperature function into an aflatoxin risk index (ARI) to represent the risk of aflatoxin contamination. The model was applied to simulate ARI for two commercial maize hybrids, H513 and H614D, grown in five multi-location field trials in Kenya using site specific agronomy, weather and soil parameters. The observed mean aflatoxin contamination in these trials varied from <1 to 7143 ppb. ARI simulated by the model explained 99% of the variation (p ≤ 0.001) in a linear relationship with the mean observed aflatoxin contamination. The strong relationship between ARI and aflatoxin contamination suggests that the model could be applied to map risk prone areas and to monitor in-season risk for genotypes and soils parameterized for APSIM.

An improved simulation model to predict pre-harvest aflatoxin risk in maize

Aflatoxin is a potent carcinogen produced by Aspergillus flavus, which frequently contaminates maize (Zea mays L.) in the field between 40° north and 40° south latitudes. A mechanistic model to predict risk of pre-harvest contamination could assist in management of this very harmful mycotoxin. In this study we describe an aflatoxin risk prediction model which is integrated with the Agricultural Production Systems Simulator (APSIM) modelling framework. The model computes a temperature function for A. flavus growth and aflatoxin production using a set of three cardinal temperatures determined in the laboratory using culture medium and intact grains. These cardinal temperatures were 11.5 °C as base, 32.5 °C as optimum and 42.5 °C as maximum. The model used a low (≤0.2) crop water supply to demand ratio—an index of drought during the grain filling stage to simulate maize crop's susceptibility to A. flavus growth and aflatoxin production. When this low threshold of the index was reached the model converted the temperature function into an aflatoxin risk index (ARI) to represent the risk of aflatoxin contamination. The model was applied to simulate ARI for two commercial maize hybrids, H513 and H614D, grown in five multi-location field trials in Kenya using site specific agronomy, weather and soil parameters. The observed mean aflatoxin contamination in these trials varied from <1 to 7143 ppb. ARI simulated by the model explained 99% of the variation (p ≤ 0.001) in a linear relationship with the mean observed aflatoxin contamination. The strong relationship between ARI and aflatoxin contamination suggests that the model could be applied to map risk prone areas and to monitor in-season risk for genotypes and soils parameterized for APSIM.