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Soil disinfestation with steam was evaluated as an alternative to fumigation. Following soil disinfestation, plant health has traditionally been measured using plant size and yield. Plant health can be measured in a timely manner more efficiently, more easily and non-destructively using image analysis. We hypothesized that plant health could be quantified and treatments can be differentiated using an RGB (Red, Green, Blue) image analysis program, particularly by observing the greenness of plant leaves. However, plant size or the proportion of green area could be unreliable due to plant loss and camera’s position and angle. To this end, we decided to evaluate plant health by analyzing the RGB codes associated with the green color only, which detects the chlorophyll reflectance and nutrient status, noting that the degree of greenness within the green-leaf-area was not affected by the plant size. We identified five RGB codes that are commonly observed in the plant leaves and ordered them from dark green to light green. Among the five RGB codes, the relative percentage covered by the darkest green to the lightest green was significantly different between the steam and chloropicrin treatments and the control, and it was not significantly different between the steam and chloropicrin treatments. Furthermore, the result was correlated with the total yield, and the trend observed in the first year was replicated in the second year of this experiment. In this study, we demonstrate that the RGB image analysis can be used as an early marker of the treatment effect on the plant health and productivity.

Strawberry ( Fragaria × ananassa ) production practices followed by growers in the United States vary by region. Understanding the challenges, needs, and opportunities in each region is essential to guide research, policy, and marketing strategies for the strawberry industry across the country, and to enable the development of general and region-specific educational and production tools. This review divided the United States into eight distinct geographic regions and an indoor controlled or protected environment production system. Current production systems, markets, cultivars, trends, and future directions for each region are discussed. A common trend across all regions is the increasing use of protected culture strawberry production with both day-neutral and short-day cultivars for season extension to meet consumer demand for year-round availability. All regions experience challenges with pests and obtaining adequate harvest labor. Increasing consumer demand for berries, climate change-induced weather variability, high pesticide use, labor and immigration policies, and land availability impact regional production, thus facilitating the adoption of new technologies such as robotics and network communications to assist with strawberry harvesting in open-field production and production under controlled-environment agriculture and protected culture.

Weeds are the major limitation to efficient crop production, and effective weed management is necessary to prevent yield losses due to crop-weed competition. Assessments of the relative efficacy of weed control treatments by traditional counting methods is labor intensive and expensive. More efficient methods are needed for weed control assessments. There is extensive literature on advanced techniques of image analysis for weed recognition, identification, classification, and leaf area, but there is limited information on statistical methods for hypothesis testing when data are obtained by image analysis (RGB decimal code). A traditional multiple comparison test, such as the Dunnett-Tukey-Kramer (DTK) test, is not an optimal statistical strategy for the image analysis because it does not fully utilize information contained in RGB decimal code. In this article, a bootstrap method and a Poisson model are considered to incorporate RGB decimal codes and pixels for comparing multiple treatments on weed control. These statistical methods can also estimate interpretable parameters such as the relative proportion of weed coverage and weed densities. The simulation studies showed that the bootstrap method and the Poisson model are more powerful than the DTK test for a fixed significance level. Using these statistical methods, three soil disinfestation treatments, steam, allyl-isothiocyanate (AITC), and control, were compared. Steam was found to be significantly more effective than AITC, a difference which could not be detected by the DTK test. Our study demonstrates that an appropriate statistical method can leverage statistical power even with a simple RGB index.

Allyl isothiocyanate (AITC) is a glucosinolate produced in cruciferous plant species. AITC is known to act as a pesticide on microorganisms, insects, and weeds. Synthetic AITC is registered as a biopesticide for agricultural soil treatment use in the United States and elsewhere in the world. Although a potent pesticide, reports on the weed and pathogen control efficacy of synthetic AITC applied as soil disinfectant are highly variable. Due to the low vapor pressure of AITC, questions remain as to whether pest and weed control efficacy can be improved by combining it with other chemicals. The objective of this study was to assess the control efficacy of AITC stand-alone applications vs. applications, in which AITC was combined with the standard-fumigants chloropicrin, 1,3-dichloropicrin, and methyl isothiocyanate. Two shank-applied on-farm field trials were conducted in cut flower [delphinium ( Delphinium elatum ), ranunculus ( Ranunculus asiaticus )] fields, and two drip tape applied field trials in strawberry ( Fragaria × ananassa ) fields in California. Weed pressure, weed seed viability, nematode survival, and pathogen survival of Pythium ultimum , fusarium wilt ( Fusarium oxysporum ), and verticillium wilt ( Verticillium dahliae ) were assessed. Cumulative yearly yield of marketable fruit was assessed in the strawberry field trials. The results of this study show that the use of AITC as a stand-alone treatment provided no consistent weed or pathogen control efficacy. However, our results also indicate that shank and drip applied multitactic fumigation approaches with AITC can efficiently control soil-borne diseases and weeds. These findings have potential implications, especially in those areas where certain fumigants are restricted due to regulations and/or availability.

Pre-plant methods for managing soil-borne pests and diseases are an important priority for many agricultural production systems. This study investigates whether the application of steam is an economically sustainable pre-plant soil disinfestation technique for organic and conventional strawberry (Fragaria ananassa) production in California’s Central Coast region. We analyze net returns from field trials using steam and steam + mustard seed meal (MSM) as pre-plant soil disinfestation treatments. ANOVA tests identify statistically significant differences in net revenues by treatment and trial. Multivariate regressions estimate the magnitude of these effects. Predictive polynomial models identify relationships between net returns and two treatment characteristics: maximum temperature (°C) and time at ≥60 °C (minutes). For organic production, net returns are statistically similar for the steam and steam + MSM treatments. For conventional production, the steam + MSM treatment has significantly higher net returns than the steam treatment. Cross-validated polynomial models outperform the sample mean for prediction of net returns, except for the steam + MSM treatment in conventional production. The optimal degree of the polynomial ranges from 1–4 degrees, depending on the production system and treatment. Results from two of three organic models suggest that maximum soil temperatures of 62–63 °C achieved for 41–44 min maximizes net returns and may be a basis for further experiments.

The discipline of weed science is at a critical juncture. Decades of efficient chemical weed control have led to a rise in the number of herbicide-resistant weed populations, with few new herbicides with unique modes of action to counter this trend and often no economical alternatives to herbicides in large-acreage crops. At the same time, the world population is swelling, necessitating increased food production to feed an anticipated 9 billion people by the year 2050. Here, we consider these challenges along with emerging trends in technology and innovation that offer hope of providing sustainable weed management into the future. The emergence of natural product leads in discovery of new herbicides and biopesticides suggests that new modes of action can be discovered, while genetic engineering provides additional options for manipulating herbicide selectivity and creating entirely novel approaches to weed management. Advances in understanding plant pathogen interactions will contribute to developing new biological control agents, and insights into plant–plant interactions suggest that crops can be improved by manipulating their response to competition. Revolutions in computing power and automation have led to a nascent industry built on using machine vision and global positioning system information to distinguish weeds from crops and deliver precision weed control. These technologies open multiple possibilities for efficient weed management, whether through chemical or mechanical mechanisms. Information is also needed by growers to make good decisions, and will be delivered with unprecedented efficiency and specificity, potentially revolutionizing aspects of extension work. We consider that meeting the weed management needs of agriculture by 2050 and beyond is a challenge that requires commitment by funding agencies, researchers, and students to translate new technologies into durable weed management solutions. Integrating old and new weed management technologies into more diverse weed management systems based on a better understanding of weed biology and ecology can provide integrated weed management and resistance management strategies that will be more sustainable than the technologies that are now failing.

Specialty crops, like flowers, herbs, and vegetables, generally do not have an adequate spectrum of herbicide chemistries to control weeds and have been dependent on hand weeding to achieve commercially acceptable weed control. However, labor shortages have led to higher costs for hand weeding. There is a need to develop labor-saving technologies for weed control in specialty crops if production costs are to be contained. Machine vision technology, together with data processors, have been developed to enable commercial machines to recognize crop row patterns and control automated devices that perform tasks such as removal of intrarow weeds, as well as to thin crops to desired stands. The commercial machine vision systems depend upon a size difference between the crops and weeds and/or the regular crop row pattern to enable the system to recognize crop plants and control surrounding weeds. However, where weeds are large or the weed population is very dense, then current machine vision systems cannot effectively differentiate weeds from crops. Commercially available automated weeders and thinners today depend upon cultivators or directed sprayers to control weeds. Weed control actuators on future models may use abrasion with sand blown in an air stream or heating with flaming devices to kill weeds. Future weed control strategies will likely require adaptation of the crops to automated weed removal equipment. One example would be changes in crop row patterns and spacing to facilitate cultivation in two directions. Chemical company consolidation continues to reduce the number of companies searching for new herbicides; increasing costs to develop new herbicides and price competition from existing products suggest that the downward trend in new herbicide development will continue. In contrast, automated weed removal equipment continues to improve and become more effective.

Small-acreage brassica vegetables need additional herbicide options. Among the vegetables grown in California are a number of niche crops, such as bok choi and brussels sprouts, that have a limited number of registered herbicides, such as DCPA. Sulfentrazone and S-metolachlor have food use tolerances for use on brassica head and stem Group 5-16, which includes crops like bok choi and brussels sprouts, as well as brassica leafy greens Subgroup 4-16B, which includes crops like kale. However, there is a lack of data for S-metolachlor and sulfentrazone on a wide variety of seeded and transplanted brassica vegetables. S-metolachlor applied preemergence (PRE) was evaluated on six direct-seeded brassica vegetables during 2019 and 2020, including bok choi, broccoli rabe, collard, mizuna, radish, and mustard greens. S-metolachlor and sulfentrazone were both evaluated PRE in transplanted brussels sprouts and kale. The results indicate that most of the seeded brassica vegetables were tolerant of S-metolachlor and that transplanted brassica vegetables were tolerant of both S-metolachlor and sulfentrazone. Broccoli rabe was moderately injured in 2020, but yields did not vary among treatments either year. Nomenclature: DCPA; sulfentrazone; S-metolachlor; bok choi, Brassica rapa L. subsp. Chinensis (Rupr.) Olsson; broccoli rabe, Brassica rapa L. var. rapa brussels sprouts, Brassica oleracea L. var. gemmifera DC.; collard, Brassica oleracea L. var. acephala DC.; mizuna, Brassica rapa L. subsp. japonica; kale, Brassica oleracea L. var. sabellica L.; mustard greens, Brassica juncea (L.) Czern.; radish, Raphanus sativus L.

Sulfentrazone was recently granted food-use tolerance approval for use on Brassica head and stem, as well as Brassica leafy vegetables. To date, one sulfentrazone registrant has listed those crops on its use label. In coastal California multiple crops per year including Brassica vegetables are grown in rapid succession; therefore, to avoid injury to rotational crops, herbicides used in those fields must be carefully selected. Given concerns about the relatively long soil persistence of sulfentrazone, studies were conducted to measure the response of direct-seeded carrot, lettuce, onion, spinach, and seeded tomato planted 3, 6, 9, and 12 mo after sulfentrazone application at 0, 112, 224, and 336 g ai ha–1. Eight plant-back studies were conducted during 2010–11 and 2012–13. Data collected were injury estimates, and stand and dry weights. Results indicate that it is safe to plant carrot and tomato 3 mo after sulfentrazone application at rates up to 336 g ai ha–1. Lettuce and green onion should not be planted within 9 mo of sulfentrazone application. Spinach should not be planted within 12 mo of sulfentrazone application. Nomenclature: Sulfentrazone; carrot; Daucus carota subsp. Sativus (Hoffm.) Schübl. & G. Martens; lettuce; Lactuca sativa L.; onion; Allium cepa L.; spinach; Spinacia oleracea L.; tomato; Lycopersicon lycopersicum (L.) Karsten L. esculentum (L.) Mill.

Increasing weed control costs and limited herbicide options threaten vegetable crop profitability. Traditional interrow mechanical cultivation is very effective at removing weeds between crop rows. However, weed control within the crop rows is necessary to establish the crop and prevent yield loss. Currently, many vegetable crops require hand weeding to remove weeds within the row that remain after traditional cultivation and herbicide use. Intelligent cultivators have come into commercial use to remove intrarow weeds and reduce cost of hand weeding. Intelligent cultivators currently on the market such as the Robovator, use pattern recognition to detect the crop row. These cultivators do not differentiate crops and weeds and do not work well among high weed populations. One approach to differentiate weeds is to place a machine-detectable mark or signal on the crop (i.e., the crop has the mark and the weed does not), thereby facilitating weed/crop differentiation. Lettuce and tomato plants were marked with labels and topical markers, then cultivated with an intelligent cultivator programmed to identify the markers. Results from field trials in marked tomato and lettuce found that the intelligent cultivator removed 90% more weeds from tomato and 66% more weeds from lettuce than standard cultivators without reducing yields. Accurate crop and weed differentiation described here resulted in a 45% to 48% reduction in hand-weeding time per hectare. Nomenclature: lettuce, Lactuca sativa L.; tomato, Solanum lycopersicum L