Version 1
: Received: 12 September 2024 / Approved: 12 September 2024 / Online: 12 September 2024 (15:41:35 CEST)
How to cite:
Ismail, N.; Khiari, L.; Daoud, A. R. Unveiling the Nutrient Signatures in Corn (Zea Mays L.) Grains: A Pivotal Indicator of Yield Potential. Preprints2024, 2024091030. https://doi.org/10.20944/preprints202409.1030.v1
Ismail, N.; Khiari, L.; Daoud, A. R. Unveiling the Nutrient Signatures in Corn (Zea Mays L.) Grains: A Pivotal Indicator of Yield Potential. Preprints 2024, 2024091030. https://doi.org/10.20944/preprints202409.1030.v1
Ismail, N.; Khiari, L.; Daoud, A. R. Unveiling the Nutrient Signatures in Corn (Zea Mays L.) Grains: A Pivotal Indicator of Yield Potential. Preprints2024, 2024091030. https://doi.org/10.20944/preprints202409.1030.v1
APA Style
Ismail, N., Khiari, L., & Daoud, A. R. (2024). Unveiling the Nutrient Signatures in Corn (Zea Mays L.) Grains: A Pivotal Indicator of Yield Potential. Preprints. https://doi.org/10.20944/preprints202409.1030.v1
Chicago/Turabian Style
Ismail, N., Lotfi Khiari and and Rachid Daoud. 2024 "Unveiling the Nutrient Signatures in Corn (Zea Mays L.) Grains: A Pivotal Indicator of Yield Potential" Preprints. https://doi.org/10.20944/preprints202409.1030.v1
Abstract
The composition simplex (N, P, K, Ca, and Mg) of the leaf is the main scores used by different approaches like the Diagnosis and Recommendation Integrated System and Compositional Nutrient Diagnosis to study the nutrient interactions and balance in plant leaves.
However, the application and validation of these concepts to grain composition remains unexplored. Contrary to foliar analysis's early intervention for nutrient deficiency detection and correction, applying this approach to seeds assesses diverse cultivars' potential, enabling anticipation of their adaptation to climate conditions and informed selection for future crops.
In the present study, a collected database of more than 924 scores including the grain yield (kg ha-1) and the nutrient composition (mg kg-1) of different corn varieties is used to develop a novel nutrient-based diagnostic approach to identify reliable markers of nutrient imbalance. A 'nutrient signature' model is proposed based on the impact of the environ-mental conditions on the nutrient indices and composition (N, P, K, Ca, and Mg) of the corn grains.
The yield threshold used to differentiate between low and high-yielding subpopulations is established at 12.000 kg ha-1and the global nutrient imbalance index (GNII) of 2.2 is determined using the chi-square distribution function and vali-dated by the Cate-Nelson partitioning method, which correlated yield data distribution with the GNII. Therefore, the nutrient compositions were classified into highly balanced (GNII ≤ 1.6), balanced (1.6 < GNII ≤ 2.2), and imbalanced (GNII > 2.2). In addition, we found that the Xgboost model's predictive accuracy for GNII is signif-icantly affected by soil pH, organic matter, and rainfall.
These results pave the way for adapted agricultural practices by providing insights into the nutrient dynamics of corn grains under varying environmental conditions.
Keywords
Nutrient Diagnosis Norms (NDN), Nutrient signature, Global nutrient imbalance index (GNII), High yielding subpopulation
Subject
Biology and Life Sciences, Agricultural Science and Agronomy
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
