The critical period for yield and quality determination in canola (Brassica napus L.)

Kirkegaard, John A.; Lilley, Julianne M.; Brill, Rohan; Ware, A. H.; Walela, Christine

doi:10.1016/j.fcr.2018.03.018

Cited by 96 publications

(85 citation statements)

References 28 publications

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“…By comparison HI for canola in trials from SE New South Wales are often substantially higher; for example, Hocking and Stapper reported HIs of 0.29-0.37 for crops sown in April and May at two sites [44] and they only fell to values comparable to those reported in the current experiments (0.25-0.28) when the crops were sown late and exposed to a higher level of stress due to the short growing season. Previously, Hocking et al measured HIs of 0.27-0.34 [54], while more recent work confirmed this difference with canola grown in South Australia having a HI of 0.27 compared to a HI of 0.34 in an experiment grown at Wagga Wagga in New South Wales [13]. This was associated with a 25% lower yield at the South Australian site.…”

Section: Discussionmentioning

confidence: 87%

“…The limit to the yield from high pod number in the rainfed treatments was due to a reduction in seeds pod −1 , which may reflect the increasing contribution from pods on the branches and the higher level of competition for assimilate at seed set. Seed set in pods on the branches show greater sensitivity to stress than the seed set on the main stem pods [13].…”

Section: Discussionmentioning

confidence: 92%

“…Moreover, water deficits at critical growth stages can limit N uptake and utilization in plants [11] and can reduce crop responses to N fertilizers [12]. The critical phase of growth for yield in canola is just after the start of flowering [13] when seed number is largely determined. Management practices that reduce the risk of N and water stress during this period will be important for achieving high yields.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Rate of Nitrogen Rather Than Timing of Application Influence Yield and NUE of Canola in South Australian Mediterranean Environments

2020

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Canola has a high nitrogen requirement and optimal nitrogen (N) management in environments with variable rainfall is a challenge. This study investigated the impact of timing of N as a single or split application at different growth stages on seed yield, N uptake and water-use efficiency in canola. Nitrogen rates of 100 and 200 kg ha−1 were applied after sowing when two leaves were unfolded or equally split between the rosette, green bud and first flower stages. The experiments were conducted at two sites with contrasting rainfall and a supplementary irrigation treatment at the low rainfall site, generating a third environment. Nitrogen application increased seed yield by up to 20% at a high rainfall site and by up to 77% at a medium rainfall site, but the timing of N did not significantly affect the yield response to N. Seed yield was closely associated with total dry matter production and seed m−2. N-use efficiency was influenced more by N recovery and uptake efficiency, rather than physiological efficiency, which highlights the importance of soil moisture availability and the ability of the crop to exploit soil water and N reserves. The results suggest that better use of subsoil moisture by overcoming some of the subsoil constraints may be an avenue for further improvements in yield and nitrogen-use efficiency (NUE) of canola in this environment.

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Section: Discussionmentioning

confidence: 87%

Section: Discussionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

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Rate of Nitrogen Rather Than Timing of Application Influence Yield and NUE of Canola in South Australian Mediterranean Environments

2020

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“…In the construction of forecasting models, not all available independent variables determining the scale of plant yielding should be taken into account. A big practical problem is combining the vegetation process of plants according to the widely used BBCH scale (Biologische Bundesanstalt, Bundessortenamt und CHemische Industrie) with the dynamically changing production conditions [24][25][26]. Operating on the BBCH scale allows for relatively reliable analyses, however precise collection of such data during vegetation is often simply impossible.…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Neural Networks for Yield Modeling of Winter Rapeseed Based on Combined Quantitative and Qualitative Data

et al. 2019

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Rapeseed is considered as one of the most important oilseed crops in the world. Vegetable oil obtained from rapeseed is a valuable raw material for the food and energy industry as well as for industrial applications. Compared to other vegetable oils, it has a lower concentration of saturated fatty acids (5%–10%), a higher content of monounsaturated fatty acids (44%–75%), and a moderate content of alpha-linolenic acid (9%–13%). Overall, rapeseed is grown in all continents on an industrial scale, so there is a growing need to predict yield before harvest. A combination of quantitative and qualitative data were used in this work in order to build three independent prediction models, on the basis of which yield simulations were carried out. Empirical data collected during field tests carried out in 2008–2015 were used to build three models, QQWR15_4, QQWR31_5, and QQWR30_6. Each model was composed of a different number of independent variables, ranging from 21 to 27. The lowest MAPE (mean absolute percentage error) yield prediction error corresponded to QQWR31_5, it was 6.88%, and the coefficient of determination R2 was 0.69. As a result of the sensitivity analysis of the neural network, the most important independent variable influencing the final rapeseed yield was indicated, and for all the analyzed models it was “The kind of sowing date in the previous year” (KSD_PY).

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“…Rapeseed, like no other culture, successfully combines the high potential seed yield (3.0-4.0 and more tons per hectare), with a high content of oil (45-48%) and protein in seeds (22-25%) and in green mass (3-4%) [8].…”

Section: Introductionmentioning

confidence: 99%

Culture of priority oil crops in the north of Kazakhstan

et al. 2020

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The research was carried out in order to increase the productivity of oilseed flax and spring rape in Northern Kazakhstan. Experimental studies were conducted from 2012 to 2014 in Kostanay research Institute of agriculture (Republic of Kazakhstan). The experiment studied the sowing of flax and spring rape – 2nd decade of May; 3rd decade of May; 1st decade of June and the seeding rate for flax is 6.5; 7.0 and 7.5 million viable seeds/ha for spring rape – 2, 0; 2.5 and 3.0 million viable seeds/ha. During the years of research, the best coefficient of water consumption of the studied crops showed the second sowing period the third decade of May. According to the seeding standards, the formation of an optimal stem density increased the efficiency of moisture use. It was noted that moisture was most rationally consumed when sowing oilseed flax with a norm of 7.0 million sunflower seeds/ha (16.5-17.7 mm/c), spring rapeseed with a seeding rate of 2.5 million sunflower seeds/ha (9.9-11.2 mm/c). Oilseed flax sowing in the 3rd decade of May with a seeding rate of 7.0 million seeds/ha allowed to obtain the highest indicators of plant productivity in the experiments: yield is 13.1 c/ha, oil yield is 5.4 c/ha. In spring rape, the optimal sowing period was also the third decade of May; according to the seeding rate, the variant of 2.5 million suns excelled. seeds/ha sown in a given period: yield is 22.9 c/ha, oil collection is from 1 ha 9.4 c.

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The critical period for yield and quality determination in canola (Brassica napus L.)

Cited by 96 publications

References 28 publications

Rate of Nitrogen Rather Than Timing of Application Influence Yield and NUE of Canola in South Australian Mediterranean Environments

Rate of Nitrogen Rather Than Timing of Application Influence Yield and NUE of Canola in South Australian Mediterranean Environments

Application of Artificial Neural Networks for Yield Modeling of Winter Rapeseed Based on Combined Quantitative and Qualitative Data

Culture of priority oil crops in the north of Kazakhstan

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