Soil surface texture is an important environmental factor that influences crop productivity because of its direct effect on soil water and complex interactions with other environmental factors. Using 30-year data, an agricultural system model (DSSAT-CERES-Wheat) was calibrated and validated. After validation, the modelled yield and water use (WU) of spring wheat (Triticum aestivum L.) from two soil textures (silt loam and clay) under rain-fed condition were analyzed. Regression analysis showed that wheat grown in silt loam soil is more sensitive to WU than wheat grown in clay soil, indicating that the wheat grown in clay soil has higher drought tolerance than that grown in silt loam. Yield variation can be explained by WU other than by precipitation use (PU). These results demonstrated that the DSSAT-CERES-Wheat model can be used to evaluate the WU of different soil textures and assess the feasibility of wheat production under various conditions. These outcomes can improve our understanding of the long-term effect of soil texture on spring wheat productivity in rain-fed condition.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4115211PMC
http://dx.doi.org/10.1038/srep05736DOI Listing

Publication Analysis

Top Keywords

spring wheat
12
rain-fed condition
12
silt loam
12
wheat grown
12
soil
9
long-term soil
8
soil texture
8
texture spring
8
wheat productivity
8
productivity rain-fed
8

Similar Publications

Reconciliation of wheat 660 K and 90 K SNP arrays and their utilization in dough rheological properties of bread wheat.

J Adv Res

January 2025

Agronomy College / National Key Laboratory of Wheat and Maize Crop Science, Henan Agricultural University, Zhengzhou 450046 China. Electronic address:

Introduction: High-density Wheat 660 K and 90 K SNP arrays are powerful tools for understanding the genetic basis of wheat traits. However, their inconsistantly physical positions that were caused by different versions of Chinese Spring genome during developing arrays are confused and inconvenient for further application.

Objective: With the repid development of wheat geonome sequencing, we aim to reconciliate Wheat 660 K and 90 K SNP arrays in modern cultivar and reveal the genetic basis of dough rheological properties in bread wheat.

View Article and Find Full Text PDF

Single-Cell RNA Sequencing Reveals the Developmental Landscape of Wheat Roots.

Plant Cell Environ

January 2025

State Key Laboratory for Biology of Plant Disease and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, People's Republic of China.

Allohexaploid wheat (Triticum aestivum L.) is one of the major crops worldwide, however there is very limited research on the transcriptional programmes of underlying cell type specification. Single-cell RNA sequencing (scRNA-seq) was used to unravel the transcriptome heterogeneity of cells and the composition of cell types in broad-spectrum organisms.

View Article and Find Full Text PDF

KPRR: a novel machine learning approach for effectively capturing nonadditive effects in genomic prediction.

Brief Bioinform

November 2024

State Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Yuanmingyuan West Road, Beijing, 100193, China.

Nonadditive genetic effects pose significant challenges to traditional genomic selection methods for quantitative traits. Machine learning approaches, particularly kernel-based methods, offer promising solutions to overcome these limitations. In this study, we developed a novel machine learning method, KPRR, which integrated a polynomial kernel into ridge regression to effectively capture nonadditive genetic effects.

View Article and Find Full Text PDF

Drought stress significantly impacts wheat productivity, but plant growth regulators may help mitigate these effects. This study examined the influence of gibberellic acid (GA3) and abscisic acid (ABA) on wheat (Triticum aestivum L., CV: Giza 171) growth and yield under different water regimes.

View Article and Find Full Text PDF

Evaluation of resistance and molecular detection of resistance genes to wheat stripe rust of 82 wheat cultivars in Xinjiang, China.

Sci Rep

December 2024

Key Laboratory of the Pest Monitoring and Safety Control of Crops and Forests of the Xinjiang Uygur Autonomous Region, College of Agronomy, Xinjiang Agricultural University, Urumqi, 830052, China.

Wheat stripe rust is a fungal disease caused by Puccinia striiformis f. sp. tritici.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!