Journal
PLANT AND SOIL
Volume -, Issue -, Pages -Publisher
SPRINGER
DOI: 10.1007/s11104-023-06099-z
Keywords
Drip-irrigated spring maize; Critical nitrogen dilution curve; Nitrogen nutrition index; Nitrogen requirement; Straw incorporation; Biochar
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This study investigated the effects of different straw incorporation methods and nitrogen rates on maize yield and nitrogen utilization efficiency. The results showed that reducing nitrogen input during the reproductive stage can increase maize yield under straw incorporation. In addition, mathematical models were developed to describe the relationships between nitrogen nutrition index, nitrogen requirement, and yield.
Background and aimsN nutrition index (NNI) diagnosis and N requirement (Nfr) estimation are essential for accurate N management in the fertirrigation system. However, the NNI after straw retention and the relationships between NNI, Nfr, and relative yield (RY) are all not fully understood.MethodsA three years field trial with three straw incorporation methods (without straw (NI), with straw (SI), and straw-derived biochar incorporation (BI)) and five N rates (0, 150, 225, 300, and 375 kg ha(-1)) was conducted on drip-irrigated maize in northwest China. Soil NO3--N content (C-N), N recovery efficiency (REN), NNI, Nfr, and grain yield under NI, SI, and BI were compared. Later, the mathematical relationships between NNI, Nfr, and RY were defined.ResultsResults showed that compared with NI, the N input during the reproductive stage could be reduced under SI and BI to achieve higher grain yield with NNI increased due to an increase in C-N. In addition, Nfr was calculated by dividing N accumulation deficit (NAD) by REN, where a significant linear relationship existed between NAD and NNI (NAD = S(1-NNI)), and the slope of the NAD-NNI regression (S) varied quadratically with days after sowing (DAS). NNI and RY were also related in linear plus plateau functions, and the maximum RY was achieved when NNI was 0.88.ConclusionsBased on patterns of variance, three easily accessible independent variables (DAS, REN, and NNI) were used to create and verify Nfr estimating techniques for generating higher yields in maize under NI, SI, and BI.
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