A crude protein and fiber model of alfalfa incorporating growth age under water and salt stress

2021 
Abstract Crude protein (CP) and fiber are the two most important quality traits of forage crops, and accurately estimating them under water and salt stress is of great significance to the production of high-quality alfalfa. However, there is seldom an applicable quality model. Here, we conducted pot and plot experiments under three irrigation amounts (full irrigation W1, moderate water deficit W2, and severe water deficit W3) and six soil salinity levels (S0, S1, S2, S3, S4, and S5, indicating 0, 1, 2, 3, 4, and 5‰ mass ratio of salt per unit soil mass, respectively), to examine the responses of CP and relative feed value (RFV) and develop CP and RFV models in alfalfa at different growth ages under combined water and salt stress. We found that there was a negative parabolic relationship between relative alfalfa CP (CPr) and relative soil electrical conductivity (ECr) with a ECr threshold (ECrth) for maximum CPr. Moderate water stress improved the CPr, while cutting number related to growth age had the opposite effect. The ECrth tended to decrease with increasing water stress and growth age, i.e., they increased the sensitivity of CPr to salt stress. Relative RFV (RFVr) of alfalfa was improved by water and salt stress, however, at the expense of biomass reduction, and the rate of increase declined with increasing growth age. Based on the above, crude protein and fiber models of alfalfa incorporating growth age under water and salt stress were developed, and performed well under pot (CP model: r2 = 0.94; RFV model: r2 = 0.98) and plot conditions (CP model: r2 = 0.95; RFV model: r2 = 0.88). Overall, the developed CP and RFV models would provide an essential framework for the production of high-quality forage in saline soil, conservation of water resources, and suppression of salinization.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    48
    References
    2
    Citations
    NaN
    KQI
    []