By Larissa Holland, Michael Bange and the CSD Extension Team

At a glance:

• The CSD Extension Team collected comprehensive agronomic data from grower’s crops to help understand how cotton developed and whether varieties (Sicot 714B3F, 748B3F, 746B3F) and regions caused differences.
• Most variability in node, height, boll development and changes in nodes above white flower (NAWF) were explained by the response to day degrees (temperature).
• While there were differences in crops across the industry varieties and regions did not contribute to these differences once day degrees were included for the above variables.
• Differences were however found amongst varieties for NAWF at flowering, the day degrees at flowering and the length of the flowering period.
• The results from these studies have been used to update the CSD’s CottonTracka® online crop monitoring tools.

Monitoring the progress of a cotton crop is vital for ideal crop performance. To extract the most value out of this monitoring, it is important to know how the growth habits of varieties differ across the regions.

The CSD Extension Team collects detailed data for varieties across the regions to help us better understand these differences. This data is also what underpins CSD’s online agronomic tool CottonTracka®. Recent data and analysis of the Bollgard 3® varieties (grown under full irrigation) presented the following:

• Differences between height, node development, boll development, and nodes above white flower (NAWF) was not significantly impacted by region or variety choice.
• Most variances in development could be attributed to differences in day degrees.
• Differences were found for the onset of first flower, the length of the flowering period, and the time when crops reached cutout (NAWF=4).

The team are currently collecting similar data for the XtendFlex® varieties to ensure recommendations reflect these new varieties.

Analysing the Data

Data has been collected over 7 seasons from 312 crops across all cotton growing regions. Data was collected from the Sicot 714B3F, Sicot 748B3F, and Sicot 746B3F varieties and covered:

• Height and node development
• Boll progression
• Changes in NAWF
• Timing of first flower flowering
• NAWF count at first flower
• Length of the flowering period

As expected, the was some variation in data due to growing crops in different fields. Following this, we focused on assessing whether the variation in height, nodes, NAWF, and boll numbers data could be attributed to differences in day degrees, and then to differences in variety and region.

Averages were generated for timing of flowering, NAWF, and flowering period for varieties and regions. Statistical differences for these averages were at a 95% confidence level. This means that when differences were found we are 95% confident that they are real and not random in nature.

What We Found

Most differences across height, node, NAWF, and boll development could be accounted for by response to day degrees, followed by field differences where crops were individually managed. The analysis also showed that variety did not account for the variability in the data. Region did account for some of the variability but only for the starting time for boll accumulation.

Figure 1: Summary of the impacts of the day degrees, differences in crops, varieties, and regions on accounting for the variability across height, node, NAWF, and boll development.

The undetermined percentage in Figure 1 may represent impacts on growth and development that can’t be accounted for with the measurements taken in these studies or could be related to management practices.

Based on the analysis we can be confident that there is a similar rate of development regardless of variety or region when assessing crop performance. Figure 2 shows that variety and region did not impact boll accumulation between planting and cut-out. The main variation in data during this period was due to field differences.

Figure 2: All boll progression data for crops collected in this study.

The line shown in Figure 2 is the final fitted response to the data that is used in CottonTracka.

There were major differences across varieties during the following stages:

• NAWF at the start of flowering, i.e., how many nodes above white flower at the onset of flowering.
• Day Degrees accumulated at the onset of flowering.
• Length of flowering.
• Day Degrees accumulated when the crop was at cut-out.

Sicot 714B3F had the lowest average NAWF while Sicot 748B3F had the highest average. The average timing of cutout was similar for Sicot 714B3F and Sicot 746B3F, but Sicot 748B3F was on average later by 35-day degrees.

As a result, the average period of flowering was different for all varieties. Compared to Sicot 748B3F, Sicot 714B3F was 51-day degrees shorter while Sicot 746B3F was 35-day degrees shorter. The longer flowering period could see an increase in boll numbers and thus improve yield. On the other hand, varieties with shorter flowering periods may be better suited when a delayed cut-out would lead to immature fruit or wasting resources.

Variety	NAWF at start of flowering	Day Degrees at onset of flowering	Day Degrees at NAWF = 4 cutout	Length of flowering (DD1532)
Sicot 714B3F	8.3(a)	584(a)	974(a)	389(a)
Sicot 746B3F	8.5(b)	597(b)	990(b)	394(a)
Sicot 748B3F	8.8(c)	600(b)	1025(c)	426(b)

Table 1: Average differences in NAWF at the start of flowering, day degrees accumulated at the onset of flowering, the length of flowering and the day degrees that had accumulated when the crop cut-out. These are the averages across regions and values that are statistically different are marked with different letters for each category.

It is important to note that his data represents the average characteristic of each variety. As previously mentioned, management can influence how varieties perform in different regions. The results provided in this analysis are general indication on how each variety may perform.

Figure 3:  An example of one of the CottonTracka® tools updated to include outcomes resulting from this study (csd.net.au).

Looking Forward

These models provide a basis to compare future varieties and provide the opportunity to better understand emerging technologies.

Early results from data collected for the new XtendFlex varieties are promising and showing little differences to current varieties, however more data will be needed to confirm this.

These early results confirm that CottonTracka is well poised to continue to help growers and consultants with their cotton management into the future.