Evaluation of panicle removal methods and crop topping applications as supplemental tools for wild oat (Avena fatua) management

Increased frequency and occurrence of herbicide-resistant biotypes heightens the need for alternative wild oat management strategies. There is an opportunity to exploit the height differential between wild oat and crops by targeting wild oat between panicle emergence and seed shed timing. Two field studies were conducted either in Lacombe, AB, or Lacombe, AB and Saskatoon, SK from 2015-2017. In the first study, we compared panicle removal methods: hand clipping, use of a hedge trimmer and a selective herbicide crop topping application to a weedy check and an industry standard in-crop herbicide application in wheat. These treatments were tested early (at panicle emergence), late (at initiation of seed shed) or in combination at one location over three years. In the second study, we investigated optimal timing of panicle removal via a hedge trimmer with weekly removals in comparison to a weedy check in wheat and lentil. This study was conducted at two locations, Lacombe, AB and Saskatoon, SK over three years. Among all the tested methods, the early crop topping treatment consistently had the largest impact on wild oat density, dockage, seedbank and subsequent year crop yield. The early (at panicle emergence) or combination of the early and late (at initiation of seed shed) treatments tended to reduce wild oat populations the following season the most compared to the late treatments. Subsequent wild oat populations were not influenced by panicle removal timing, but only by crop and location interactions. Panicle removal timing did significantly affect wild oat dockage in the year of treatment but no consistent optimal timing could be identified. However, the two studies together highlight a number of additional questions to be investigated, as well as the opportunity to manage wild oat seedbank inputs at the panicle emergence stage of the wild oat lifecycle.

Potential of Hybrid CNN-RF Model for Early Crop Mapping with Limited Input Data

When sufficient time-series images and training data are unavailable for crop classification, features extracted from convolutional neural network (CNN)-based representative learning may not provide useful information to discriminate crops with similar spectral characteristics, leading to poor classification accuracy. In particular, limited input data are the main obstacles to obtain reliable classification results for early crop mapping. This study investigates the potential of a hybrid classification approach, i.e., CNN-random forest (CNN-RF), in the context of early crop mapping, that combines the automatic feature extraction capability of CNN with the superior discrimination capability of an RF classifier. Two experiments on incremental crop classification with unmanned aerial vehicle images were conducted to compare the performance of CNN-RF with that of CNN and RF with respect to the length of the time-series and training data sizes. When sufficient time-series images and training data were used for the classification, the accuracy of CNN-RF was slightly higher or comparable with that of CNN. In contrast, when fewer images and the smallest training data were used at the early crop growth stage, CNN-RF was substantially beneficial and the overall accuracy increased by maximum 6.7%p and 4.6%p in the two study areas, respectively, compared to CNN. This is attributed to its ability to discriminate crops from features with insufficient information using a more sophisticated classifier. The experimental results demonstrate that CNN-RF is an effective classifier for early crop mapping when only limited input images and training samples are available.

The Virtues of the Naïve View

This chapter approaches the relationship between knowledge and intervention in synthetic biology by considering the uses of ignorance among practitioners. It introduces the notion of an “epistemic wager” in order to articulate the element of conjecture at the heart of synthetic biologists' attempts to refigure relations between making and not knowing. It also argues that practitioners adjust relations between knowledge and intervention, as both means and ends on the go. The chapter takes the destabilization of knowledge in the postgenomic era to mean that the question concerning how not knowing will shape the century of biology is as pressing as the one that takes relations between knowledge and intervention as its starting point. It mentions James Collins, a prominent synthetic biologist and originator of the ground-breaking genetic switch, who mythologizes “naïveté” among an early crop of parts-based practitioners.

Response of sunflower hybrids to crop density in the steppe of Ukraine

Field studies conducted in 2018–2019 in the northern Steppe of Ukraine with sunflower hybrids of different maturity groups (LG 50300, LG 5580, LG 5478, LG 5638, LG 5662) showed that the crop density of early-crop hybrid LG 50300 from 55,000 plants/hectare to 70,000 plants/hectare led to a decrease in productivity by 0.11 t ha−1 and a decrease in oil content by 0.9%. The density of middle-early hybrid LG 5580 resulted in a decrease in sowing productivity of 0.21 t ha−1, while oil content remained nearly the same. Another middle-early hybrid LG 5478 showed slight variations in productivity and oil content with an increase of crop density. The study of the mid-season hybrid LG 5038 showed a decrease in sowing productivity by 0.2 t ha−1 with the density up to 70,000 plants/hectare. Mid-season hybrid LG 5662 with density of 70,000 plants/hectare showed productivity increase by 0.14 t ha−1.

POLARIMETRIC SAR DATA FROM SENTINEL-1A APPLIED TO EARLY CROP CLASSIFICATION

This paper aims to map crops in two Brazilian municipalities, Luís Eduardo Magalhães (LEM) and Campo Verde, using dual-polarimetric Sentinel-1A images. The specific objectives were: (1) to evaluate the accuracy gain in the crop classification using Sentinel-1A multitemporal data backscatter coefficients and ratio (σ0VH, σ0VV and, σ0VH/σ0VV, denominate BS group) in comparison to the addition of polarimetric attributes (σ0VH, σ0VV, σ0VH/σ0VV, H, and α, denominate BP group) and; (2) to assess the accuracy gain in the earliest crop classification, creating new scenarios with the addition of the new SAR data together with the previous images for each date and group (BS and BP) during the crop development. For BS and BP groups, 13 e 10 scenarios were analyzed in LEM and Campo Verde, respectively. For the classification process, we used the Random Forest (RF) algorithm. In the LEM site, the best results for BS and BP groups were equivalent (overall accuracy: ∼82%), while for the Campo Verde site, the classification accuracy for the BP group (overall accuracy: ∼80%) was 2% higher than the BS group. The addition of new images during the crop development period increased the earliest crop classification overall accuracy, stabilizing from mid-February in LEM and mid-December in Campo Verde, after 10 and 8 images, respectively. After these periods, the gain in classification accuracy was small with the addition of new images. In general, our results suggest the backscattering coefficients and polarimetric attributes extracted from the Sentinel-1A imagery exhibited a great performance to discriminate croplands.

Effect of Plant Biostimulants on Macronutrient Content in Early Crop Potato Tubers

This paper analyzes the effects of the foliar application of the seaweed extracts Bio-algeen S90 (Ascophyllum nodosum) and Kelpak SL (Ecklonia maxima), as well as the humic and fulvic acids in HumiPlant (leonardite extract) on the macronutrient content in tubers of very early potato cultivars (‘Denar’, ‘Lord’, ‘Miłek’) and their ionic ratios. The field experiment was carried out in central-eastern Poland over three growing seasons, using Haplic Luvisol. The biostimulants were applied according to the manufacturers’ recommendations. Potatoes were harvested 75 days after planting. The use of biostimulants increased potassium (K) content in tubers, on average, by 1.26 g∙kg−1 of dry matter compared with the untreated control tubers. Bio-algeen S90 did not affect the phosphorus (P) content in tubers, whereas Kelpak SL and HumiPlant reduced the phosphorus content, on average, by 0.063 g∙kg−1 of dry matter. The biostimulants did not affect calcium (Ca), magnesium (Mg), or sodium (Na) content in tubers. The use of biostimulants resulted in an increase in the mass ratios of K+:Ca2+, K+:Mg2+, and (K+ + Na+):(Ca2+ + Mg2+) in early crop potato tubers, compared with the untreated control tubers, but did not affect the mass ratios of Na+:Ca2+ and Na+:Mg2+ or the mass ratio of Ca:P. The macronutrient content in early crop potato tubers and their ionic ratios depended on the cultivar and environment conditions.