Search Results (6)

Interest is increasing in grazing winter canola (Brassica napus) as an alternative crop in winter wheat (Triticum aestivum) rotations in the Southern High Plains (SHP) of the USA and similar environments. In this stidy, winter cereal rye (Secale cereale) and winter canola pastures (forage) were compared for two winter growing seasons at New Mexico State University’s Rex E. Kirksey Agricultural Science Center at Tucumcari, NM, USA, to determine the relative effect of pasture type on late-gestation beef cows and growing yearling cattle, along with the effect of grazing on canola grain production. Canola grain yields were reduced by 25% when canola was grazed until removal approximately one month after grazing was initiated, but before the onset of rapid regrowth after winter (641 vs. 486 kg grain ha⁻¹ for never grazed or grazed canola, respectively, p < 0.0256). No differences existed for forage mass, nutritive value, or animal performance, although forage mineral composition of canola could be a concern. Grazing winter canola as a dual-purpose crop in the SHP and similar environments is feasible when proper grazing management is applied; producers should anticipate a 20–25% reduction in grain yield, but expect animal gains to offset that loss. Full article

Due to the expanding population and the constantly changing climate, food production is now considered a crucial concern. Although passive satellite remote sensing has already demonstrated its capabilities in accurate crop development monitoring, its limitations related to sunlight and cloud cover significantly restrict real-time temporal monitoring resolution. Considering synthetic aperture radar (SAR) technology, which is independent of the Sun and clouds, SAR remote sensing can be a perfect alternative to passive remote sensing methods. However, a variety of SAR sensors and delivered SAR indices present different performances in such context for different vegetation species. Therefore, this work focuses on comparing various SAR-derived indices from C-band and (Sentinel-1) and X-band (TerraSAR-X) data with the in situ information (phenp; pgy development, vegetation height and soil moisture) in the context of tracking the phenological development of corn, winter wheat, rye, canola, and potato. For this purpose, backscattering coefficients in VV and VH polarizations $({\sigma }_{VV}^0$, ${\sigma }_{VH}^0$), interferometric coherence, and the dual pol radar vegetation index (DpRVI) were calculated. To reduce noise in time series data and evaluate which filtering method presents a higher usability in SAR phenology tracking, signal filtering, such as Savitzky–Golay and moving average, with different parameters, were employed. The achieved results present that, for various plant species, different sensors (Sentinel-1 or TerraSAR-X) represent different performances. For instance, ${\sigma }_{VH}^0$ of TerraSAR-X offered higher consistency with corn development (r = 0.81), while for canola ${\sigma }_{VH}^0$ of Sentinel-1 offered higher performance (r = 0.88). Generally, ${\sigma }_{VV}^0$, ${\sigma }_{VH}^0$ performed better than DpRVI or interferometric coherence. Time series filtering makes it possible to increase an agreement between phenology development and SAR-delivered indices; however, the Savitzky–Golay filtering method is more recommended. Besides phenological development, high correspondences can be found between vegetation height and some of SAR indices. Moreover, in some cases, moderate correlation was found between SAR indices and soil moisture. Full article

Biophysical parameter retrieval using remote sensing has long been utilized for crop yield forecasting and economic practices. Remote sensing can provide information across a large spatial extent and in a timely manner within a season. Plant Area Index (PAI), Vegetation Water Content (VWC), and Wet-Biomass (WB) play a vital role in estimating crop growth and helping farmers make market decisions. Many parametric and non-parametric machine learning techniques have been utilized to estimate these parameters. A general non-parametric approach that follows a Bayesian framework is the Gaussian Process (GP). The parameters of this process-based technique are assumed to be random variables with a joint Gaussian distribution. The purpose of this work is to investigate Gaussian Process Regression (GPR) models to retrieve biophysical parameters of three annual crops utilizing combinations of multiple polarizations from C-band SAR data. RADARSAT-2 full-polarimetric images and in situ measurements of wheat, canola, and soybeans obtained from the SMAPVEX16 campaign over Manitoba, Canada, are used to evaluate the performance of these GPR models. The results from this research demonstrate that both the full-pol (HH+HV+VV) combination and the dual-pol (HV+VV) configuration can be used to estimate PAI, VWC, and WB for these three crops. Full article

The use of competitive crops in successional rotations has been shown to reduce the growth and establishment of annual weeds by the depletion of the weed seedbank in broadacre cropping systems. However, the impact of specific crop rotational sequences contributing to weed seedbank density has not been quantified in the Riverina region of southern Australia. Trials were established in two locations in 2014–2018 to quantify the impact of selected annual rotations featuring grain, pulse, and pasture crops on weed infestation and seedbank dynamics with a focus on winter and summer annual weeds. The weed seedbank dynamics were evaluated by a twice-annual soil sampling regime (at planting and harvest), followed by soil sample screening for weed propagule germination and seedling establishment in a subsequent controlled-environment screening performed from 2014–2020. The weed seedling density decreased in the cereal rotations in years experiencing average to above-average rainfall, as crops established a dense canopy leading to reduced weed establishment and fecundity. Several rotational treatments were effective in suppressing the weed propagule numbers over time, including those using dual-purpose cereals only or a canola break-crop along with the cereals. Rotational selection can be an important and cost-effective tool in integrated weed management systems when applied over multiple growing seasons. Full article

Canola crop has the potential for both seeds and grazing. Optimal planting density, time of nitrogen (N) fertilizer application and rates are the major aspects for successful qualitative traits and canola yield formation. In this content, optimization of planting density, N levels and its time of application in dual purpose canola are needed. This study was carried out in RCB design with split pot arrangement having three repeats during winter 2012–2013 and 2013–2014. The study evaluated N levels (120 and 80 kg N ha⁻¹), cutting treatment, N application timings and planting density (20 and 40 plants m⁻²) effects on qualitative traits and yield of canola. No-cut treatment had 7.02%, 2.46%, and 4.26% higher, glucosinolates, oil, and protein content with 31.3% and 30.5% higher biological and grain yield respectively, compared with grazed canola. Compared with no-cut canola, grazed canola resulted in 7.74% of higher erucic acid. Further, application of N at 120 kg N ha⁻¹ had 8.81%, 5.52%, and 6.06% higher glucosinolates, percent protein, and seed yield, respectively than 80 kg N ha⁻¹. In-addition, the application of N into two splits was most beneficial than the rest application timings. Cutting had 15% reduction in grain yield of canola and fetched additional income of 143.6 USD compared with no-cut. Grazing resulted in a 23% reduction in grain yield while had additional income of 117.7 USD from fodder yield. Conclusively, the application of N in two splits at 120 kg N ha⁻¹ combined with 20 plants m⁻² is a promising strategy to achieve good qualitative attributes and canola yield under dual purpose system. Full article

Global interest in stable energy resources coupled with growing demand for bio-oils in various conventional and arising industries has renewed the importance of vegetable oil production. To address this global interest, oilseed production has been increased in recent decades by different approaches, such as extending the cultivation area of oil crops, or breeding and growing genetically modified plants. In this study, pea (Pisum sativum L.) accessions were screened for lipid content using a rapid extraction method. This method quantifies lipid concentration in pea seeds and was developed by assessing and comparing the results of existing extraction methods used for canola and soybean, the top two Canadian oilseeds. Seeds of 151 field pea accessions were grown to maturity in 2009 and 2010 at McGill University (Quebec, Canada). Overall, lipid concentration in pea seeds ranged from 0.9 to 5.0%. Among several seed characteristics, only seed shape (wrinkled verses round) had a significant effect on the total lipid production in the seeds. Peas are a valuable source of protein and starch, but the lipid concentration in their seeds has been undervalued. This research supports the idea of developing a novel dual-purpose oilseed pea that emulates the protein and oil production in soybean seeds while being conveniently adapted to a colder climate. Full article