Purpose <p>In Australia, pulse crops are underutilised relative to cereals, with a consensus that this is largely attributed to pulses exhibiting greater yield variability than cereals. However, the variability indicators used have typically not accounted for the spatial structure of within-field variation. A total of 762 yield maps across Australia were used to 1) compare the within-field variability of pulses (chickpea, lentil, and lupin) to wheat, using the Yield Opportunity Index (<i>Y</i><sub><i>i</i></sub>), and 2) investigate the spatial and temporal behaviour of <i>Y</i><sub><i>i</i></sub> to improve its interpretability at the farm level.</p> Methods <p>To account for differences in terms of environmental conditions across Australia and in time, a principal components analysis (PCA) was performed on soil, elevation and weather variables covering all yield maps. Only wheat yield maps within the convex hull of each pulse species were used for comparisons. A case study farm was used to compare chickpea and wheat with two contrasting seasons, and three individual field time-series to observe fluctuations in <i>Y</i><sub><i>i</i></sub> over time.</p> Results <p>The <i>Y</i><sub><i>i</i></sub> results across all species showed that pulses have a greater within-field variability compared to wheat, and therefore a greater opportunity for site-specific crop management (SSCM). The farm case study indicated that chickpea and wheat exhibited different SSCM opportunities depending on seasonal conditions. Greater SSCM opportunities were correlated with more coherent, manageable zones, as highlighted by the <i>Y</i><sub><i>i</i></sub>. The PCA on environmental factors ensured that direct comparisons between species could be made.</p> Conclusion <p>The main finding is that there is a greater opportunity for SSCM in pulse crops than in wheat. The validation of <i>Y</i><sub><i>i</i></sub> as a more complete measure of within-field variability than traditional approaches such as the coefficient of variation (CV), and is a valuable tool in the adoption of SSCM.</p>

错误:搜索内容不能为空,请输入英文关键词
错误:关键词超出字数限制,请精简
高级检索

Are pulses really more variable than cereals? A comprehensive analysis of within-field yield variability across Australia

  • T. McPherson,
  • D. Al-Shammari,
  • P. Filippi,
  • T. F. A. Bishop

摘要

Purpose

In Australia, pulse crops are underutilised relative to cereals, with a consensus that this is largely attributed to pulses exhibiting greater yield variability than cereals. However, the variability indicators used have typically not accounted for the spatial structure of within-field variation. A total of 762 yield maps across Australia were used to 1) compare the within-field variability of pulses (chickpea, lentil, and lupin) to wheat, using the Yield Opportunity Index (Yi), and 2) investigate the spatial and temporal behaviour of Yi to improve its interpretability at the farm level.

Methods

To account for differences in terms of environmental conditions across Australia and in time, a principal components analysis (PCA) was performed on soil, elevation and weather variables covering all yield maps. Only wheat yield maps within the convex hull of each pulse species were used for comparisons. A case study farm was used to compare chickpea and wheat with two contrasting seasons, and three individual field time-series to observe fluctuations in Yi over time.

Results

The Yi results across all species showed that pulses have a greater within-field variability compared to wheat, and therefore a greater opportunity for site-specific crop management (SSCM). The farm case study indicated that chickpea and wheat exhibited different SSCM opportunities depending on seasonal conditions. Greater SSCM opportunities were correlated with more coherent, manageable zones, as highlighted by the Yi. The PCA on environmental factors ensured that direct comparisons between species could be made.

Conclusion

The main finding is that there is a greater opportunity for SSCM in pulse crops than in wheat. The validation of Yi as a more complete measure of within-field variability than traditional approaches such as the coefficient of variation (CV), and is a valuable tool in the adoption of SSCM.