Horticulture

To represent this healthy U.S. diet in GLOBIOM, we performed a series of additional conversions. First, we determined the allocation of GLOBIOM items across Calculator product groups based on how commodities are currently allocated across each product group. For example, the majority of barley is used for making alcohols and the remaining 9% is consumed as cereals, and about 21% of corn that is consumed by humans is consumed as a cereal, whereas the remaining 79% is used for making corn-based sugars . We then calculated healthy diet “shifters” for each Calculator product group by dividing the healthy diet kcal by the baseline diet kcal. A “shifter”, as we define it here, is a constant multiplier that allows conversion between scenario values. Food product group shifters allow for the creation of a healthy U.S. diet scenario using any baseline diet kcal values . We then combined the healthy diet shifters with the GLOBIOM-to-Calculator product group allocations to calculate diet shifters for GLOBIOM items . These shifters were used directly in GLOBIOM to create the Healthy US, Healthy World, and Sustainability scenarios. Unlike in the Calculator,dutch bucket for tomatoes shifters were applied to the demand curve in GLOBIOM, since final human consumption is determined endogenously .

This means that dietary changes between Calculator and GLOBIOM may not be identical, though they are highly similar .Using the US FABLE calculator, we developed two sets of yield shifters. The “BAU yields” shifters apply 2000–2015 yield growth trends in the U.S. to simulation years 2000–2050 . The Higher “U.S. Yields” shifters increase the growth rate by 200% between 2015 and 2050 if the annual positive rate is lower than 1%/year, 80% if the annual rate is higher than 1%/year, and turns negative historic growth rates into positive growth rates , but only applying these adjusted growth rates on the yields after 2020 . For example, if crop yields were declining at a rate of 0.5% per year historically, and the yield in 2020 was 4.5 tons/ha, we changed this in the higher “U.S. Yields” scenario to increase at a rate of 0.5%/ year starting in 2020 and thus the yield would be 4.6 tons/ ha in 2025. GLOBIOM endogenously adjusts yields based on cropping mix and management . Thus, GLOBIOM yields are a combination of our exogenously applied yield increases and GLOBIOM endogenous adjustments. As a result, the yields between GLOBIOM and the Calculator may not match exactly .We used the Calculator to explore the exogenous effects of changes in livestock productivity parameters. These livestock changes may represent technological innovations or management systems shifts .

The Calculator uses the historic USDA growth rate from 2010 to 2020 linearly extrapolated out to 2050 in the “BAU productivity” livestock productivity scenario, and we increased this growth rate by 20% in the “High productivity” livestock productivity scenario . For ruminant grazing density, the “Constant density” scenario uses the same ruminant density from 2010 to 2050, and reduces this by 6% by 2050 in the “Declining density” scenario. Though these changes were applied to all grazing livestock in the U.S., the vast majority of grazing livestock is cattle, thus, these changes effectively alter beef productivity and grazing density. We conducted these sensitivity analyses in the Calculator, because livestock productivity in GLOBIOM is a more complex combination of endogenous and exogenous factors than for crop yields. The amount of livestock product per unit of land area depends on the average feed conversion ratio and the grass yield. The grass yield is exogenous and can change over time under different climate scenarios , whereas the average feed conversion ratio is endogenous as the production system composition is endogenous.We constructed two main scenarios—Baseline and Sustainability—in both GLOBIOM and the US FABLE Calculator.

The values of all variables chosen for the Sustainability scenario are expected to favor sustainable outcomes . For the Baseline scenarios , we assume no change from the current average U.S. diet, SSP2-Middle-of-the-Road diets for ROW, and SSP2 baseline yields. GLOBIOM model runs generated scenario-specific values that were used as inputs in the Calculator for yields, livestock productivity, ruminant density, imports, and exports . In GLOBIOM, we ran five additional scenarios that isolate the roles of U.S. diets, ROW diets, and crop productivity assumptions to examine every combination of input assumptions. We could not replicate these scenarios in the Calculator because it cannot represent global demand and production. We simulated alternative crop productivity futures in GLOBIOM. As described above, yields are a function of both endogenous decisions and exogenous productivity growth rates that vary between business-as-usual and high yield scenarios . In this analysis we vary the latter to represent a range in expected technological change from business-as-usual to optimistic growth. The remaining five scenarios are as follows : 2: healthy U.S. diets and healthy ROW diets , 3: healthy U.S. diets and high U.S. yields ; 4: healthy U.S. diets only ; 5: healthy ROW diets only ; and 6: high U.S. yields only . For the Calculator sensitivity analysis , we used the A: sustainability scenario assumptions, but changed either the livestock productivity to be higher than in GLOBIOM or ruminant density to be lower than in GLOBIOM . Because of inherent differences in underlying data, model infrastructure, and system boundaries between the FABLE Calculator and GLOBIOM approaches, we report only the difference and percentage change from each model’s BAU scenario.

Simulated diets and crop yields reflect scenario adjustments. Scenario adjustments to commodity demand curves and crop yields in GLOBIOM resulted in both production and consumption changes . That is, instead of perfect alignment with assumed diet and productivity assumptions in a domestic-only LCA or mass balance approach, simulated diets and yields in GLOBIOM reflect endogenous prices and supply-side adjustments that cause variation in crop yields . GLOBIOM uses commodity-specific demand curves for representing human demand. Thus, applying the healthy U.S. diet shifters essentially shifts the entire demand curve, as opposed to the final demand for each commodity, which is determined by both the demand and supply curves and market dynamics. As a result, applying the same set of shifters to both the Calculator and GLOBIOM does not necessarily ensure the same percentage change in final per capita consumption across all items. However, demand curve adjustments to reflect healthy diets in particular, led to expected changes in consumption across all food product groups . Results indicate that the final per capita consumption in GLOBIOM very closely resembles that of the Calculator . Similarly, simulated yields from GLOBIOM vary across scenarios due to market adjustments in the U.S. and the rest of the world, illustrating the sensitivity of the U.S. production system to global market forces .Pastureland declines significantly while cropland contracts slightly in response to healthier U.S. diets . Healthier diets in the rest of the world and increases in U.S. crop yields only modestly reduce cropland in the U.S.,blueberry grow pot but significantly reduce cropland in the rest of the world. In the Sustainability scenarios, domestic land used for livestock forage and grazing decline by 37 mil ha in the US FABLE Calculator and 28 mil ha in GLOBIOM scenarios if the average American diet resembled the Healthy-style DGA diet by 2050 . These declines in pastureland are far more dramatic than for cropland, which declines by only 3.9 mil ha in the Calculator scenarios and 2–3.3 mil ha in GLOBIOM scenarios due to healthier U.S. diets . Percentage reductions in both cropland and pastureland are similar across the two models, with the reductions in the Calculator about 1% and 10% points greater, respectively. Both models assume that reductions of pastureland and cropland result in a commensurate increase in natural lands. Across GLOBIOM scenarios, healthy U.S. diets have the greatest single impact on pastureland changes . Pastureland use is more sensitive to dietary changes than cropland due to the low relative land use efficiency of beef production . Increasing crop productivity has no discernible impact on pastureland use. Simultaneously shifting to healthy diets in the rest of the world and the U.S. only negligibly changes pastureland requirements in the U.S. by 1–1.8 mil ha relative to only shifting to healthy diets in the U.S. , because most beef produced in the U.S. is domestically, as opposed to being exported.

Thus, U.S. pastureland should be most responsive to changes in domestic beef consumption. Correspondingly, shifting just the ROW to healthier diets actually slightly increases pastureland over the baseline by 1.8 mil ha despite a slight reduction in U.S. beef production, likely a result of decreased beef land use efficiency . As the ROW demand for U.S. beef declines, these small decreases in U.S. production result in beef land use efficiency reduction. For cropland, there is a similar spread of about 1 mil ha across the GLOBIOM scenarios that adopt healthier diets in the U.S., the ROW, or both. The greatest declines in cropland are observed with healthier diets. Increased crop productivity in the U.S. only reduces domestic cropland by less than 200,000 ha , due to increased production and exports from the greater global comparative advantage of U.S. crop commodities . As a result, higher U.S. yields alone cause a 7.5 mil ha decrease in croplands globally by 2050 relative to the baseline, which is partially offset by a 1.9 mil ha increase in grassland globally . Annual domestic GHG emissions decrease due to shifts to healthier diets in the U.S. and declines are primarily driven by livestock methane emissions reductions and land sequestration. As a result of a healthier U.S. diet, annual CO2e emissions from the agriculture, forestry, and other land use sectors reduces by 176–197 MT for GLOBIOM scenarios and 187 MT for the Calculator Sustainability scenario compared to the baseline by 2050. Livestock methane emissions drive the majority of total reductions for GLOBIOM scenarios, whereas land use change emissions drive the majority of reductions for the Calculator. Most of the land use change emissions reductions are due to increases in forest sequestration from natural regeneration on former cropland and pastureland . As with land use changes, domestic emissions reductions by 2050 show minor differences in U.S. emissions between GLOBIOM scenarios that vary yields and diets in the ROW. These differences are only apparent for cropland-related emissions—crop and soils non-CO2. Increasing yields alone has little to no effect on total emissions, since any additional sequestration from land use change is negated by increased crop and soil non-CO2 emissions due to more intensive farming practices. In particular, N2O emissions from fertilizer use increases as fertilizer intensity expands with higher yields; higher fertilizer application and associated input costs are exogenously required to increase to achieve higher exogenous yield growth rates. Healthy ROW results in near-term total emissions reductions of 50–60 MT CO2e/year, but these diminish to less than 10 MT CO2e/year by 2050. We do not find evidence of international leakage when the U.S. shifts to healthier diets . In fact, we find slight declines in global emissions in the Healthy U.S. and U.S. Yields scenarios . We do find that Healthy ROW alone reduces global AFOLU emissions by 26% despite having minor impacts on U.S. emissions . The future trajectory of beef productivity and ruminant density provide bounds for the range of possible land use and emissions impacts from healthy U.S. diets . To explore the role of technology improvements in the cattle industry and changes in production system or intensification in response to changes in demand, we use the Calculator to run a range of sensitivity scenarios that exogenously alter beef productivity and ruminant density of cattle. We find that beef productivity decreases significantly in response to lower domestic beef consumption and production after 2020 in the healthier U.S. diet GLOBIOM scenarios . Productivity increases and then decreases after 2030 or 2040 for the scenarios that maintain the current average U.S. diet . The business-as-usual beef productivity trajectory in the U.S., based on USDA data from the last 20 years is comparable with GLOBIOM baseline until 2040, when the productivity growth in GLOBIOM starts to level of due to market conditions. The sensitivity that increases this BAU livestock productivity growth rate causes beef production to exceed that of all GLOBIOM scenarios . These productivity increases result in the greatest pastureland reduction by 2050 across all scenarios. Emissions follow similar trends with increases in beef productivity resulting in significantly greater total emissions reductions compared to baseline or 90–127 MT/year greater reductions than the Calculator Sustainability scenario that uses GLOBIOM productivity assumptions .

When plant tissues cannot grow due to nitrogen limitation, they cannot incorporate or store additional carbohydrates. This lower physiological limit of tissue nitrogen content, at which no more cell division or incorporation of carbon is possible, is called the critical nitrogen content of the tissue . The CNC is expressed on a carbon basis .The CNC can be determined for whole plants , and for the different functional tissues of the plant. Different plant parts perform different functions, and therefore have different minimum nitrogen requirements for metabolism maintenance. In previous research with the dicotyledonous storage root perennial Ipomoea batatas , it was determined that the most photosynthetically active tissues, the leaves, and the fibrous roots, which are involved in nutrient uptake, have the highest CNC of all vegetative plant tissues . The storage roots of I. batatas had a significantly lower CNC than any of the other Ipomoea tissues. The difference between the actual tissue nitrogen content and the CNC determines the capacity of these different plant tissues to incorporate or store carbohydrates. Tissues with nitrogen contents that are above the CNC can still incorporate or store carbohydrates. These tissues have a positive carbon sink strength .

Photosynthetically active tissues that have reached their CNC will not incorporate the produced carbohydrates, because that would dilute the nitrogen content of these tissues below the CNC,hydroponic nft channel and metabolism would be impaired. Instead, the photosynthetically active tissues deposit the produced carbohydrates in the phloem, which transports them to those tissues that still have the ability to incorporate or store carbohydrates . This is how leaves, that because of their high CNC loose the ability to incorporate the photosynthates in their own tissues relatively early during the development of the plant, can still produce photosynthates and translocate them down to the reserve storage organs, such as I. batatas storage roots, which maintain their positive carbon sink strength, and tissue growth , the longest of all plant tissues, due to their low CNC.The critical nitrogen content of Arundo leaf tissue was determined in a hydroponics experiment. One hundred Arundo stem fragments were collected in June 1998 from the Santa Ana River near River Road in Riverside county. In the greenhouse, the stem fragments were placed in water for 2 weeks to allow for root and shoot growth. After two weeks, 48 young plants that sprouted from the meristems on the stem fragments were randomly selected for use in the experiment.

Four stems were placed in each of eleven 120-liter plastic containers, that were filled with 100 L aerated, half strength Hoagland nutrient solution . The sprouted stem fragments rested on a floating plastic mesh supported by a ring of plastic pipe, on the surface of each trash can’s nutrient solution. A sheet of opaque white plastic was wrapped around and over each trash can to block out sunlight preventing algae growth and high temperatures in the nutrient solution. The nutrient solutions in the containers were monitored two times per week during a 48-week growth period. Each check consisted of the following: addition of enough deionized water to bring the can’s nutrient solution level up to a 100-L mark, determination of the can’s electrical conductivity in full volume. A concentrated Hoagland solution was added to re-establish the conductivity of the nutrient solution to its original value. The pH was adjusted to 5.7. The harvest dates were partially determined by the growth of the plants as the experiment progressed. Harvested plants were separated into apical meristems, green leaf blades, brown leaf blades , green leaf sheaths, brown leaf sheaths , stems, rhizomes, and roots. Plant parts were dried to a constant weight at 60 C. Biomass of the tissue was determined and sub-samples were ground in a Wiley mill to pass a 0.5 mm mesh screen . The nitrogen and carbon contents of the tissues were determined using an organic elemental analyzer . Stem fragments with meristems can root and regenerate new Arundo plants , as has been reported earlier by .

There were significant patterns in rooting success of meristems on Arundo stems throughout the growing season. In the winter months of November through January, rooting is low, and success percentage lies below 20%, with the exception of 28 ± 12% rooting for meristems from hanging stems in January . Nearly all meristems rooted from March through September. The speed with which meristems rooted showed a related pattern through the growing season . In the period with the lowest rooting success, t50 had the highest values, indicating the slowest rooting. Rooting was most rapid in the months of May through July, a time window that was more narrow that the period in which rooting is most successful.There are no significant differences between the results in plain water and half-Hoagland nutrient solution . The rooting success and speed pattern is similar in soil, but the single replicate meristems do not allow for inclusion in the two-way ANOVA. When compared within each sampling, the rooting success of meristems from hanging stems was significantly higher than that of meristems from upright stems . When split for rooting medium, there was no significant different in rooting success between hanging and upright meristems over time in plain water , but the differences remain significant in the nutrient solution and in soil . Like with rooting success, rooting speed of hanging meristems was significantly faster when compared to that of the upright meristems of the same sampling date . When separated among rooting media, the difference in the speed with which rooting occurs was most pronounced in plain water , but still exists in the nutrient solution and soil . Though these differences in rooting success and the speed of rooting may be statistically significant, they generally are too small to be ecologically significant. Stem diameter at the node where the meristem is placed is an indicator of relative height on the stem,nft growing system and the age of the meristem. Within stems, there was no relation of rooting success or speed with the diameter of the stem at the point of the meristem, so the older meristems on an Arundo stem do not root better or faster than the younger meristems on Arundo stems.When the temperature of the rooting environment was controlled at 28/15 °C for 14/10 h during the entire growing season the seasonal patterns of rooting success and speed remained, and differences between the seasonal rooting patterns of the fragments from hanging and upright stems emerged . The overall patterns differed slightly from those in the greenhouse experiment, with the lowest rooting by both stem type fragments in February through March. The rooting percentages increased in April, and the highest rooting rates occurred from July through September at 80 – 92% for both stem types. In October the rooting of the upright stem fragments decreased more than that of those from hanging stems. The lowest rooting rates of the stem fragments from upright stems were 0 – 10% in February and March, while the rooting rates of the hanging stem fragments only decreased to 30% . The positive influence of the seasonal effect in the months of July – September on the rooting rates of both stem types masked the difference that emerged in the Winter and Spring months. The rooting by meristems from upright stems benefits more from this seasonal effect that of meristems from hanging stems. The seasonal effect on the rooting rates of the stem types could be related to a number of environmental factors that change during the growing season, such as temperature, light intensity, and daylength. determined that stem fragments sampled at the same time, but stored at different temperatures, displayed different sprouting percentages when potted and regenerated at the same temperature in a single greenhouse.

We hypothesize that the different ambient temperatures prior to sampling in our experiment was an ecophysiological equivalent of the experimental factor “storage temperature” in the Boose and Holt study, and one of the factors involved in the seasonal pattern of A. donax stem fragment rooting.The seasonal differences in the rooting percentages and speed between meristems from upright and hanging stems that was striking under controlled temperature conditions had been much less pronounced in the greenhouse rooting experiment. The results of the greenhouse rooting experiment show that the temperature at the time of rooting influence the effects of the seasonal factor. Environmental effects, such as temperature and inundation, are known to affect the success of invasive plants with either negatively or positively In the greenhouse experiment, the temperatures of the rooting medium varied with the ambient temperatures and solar irradiation, while the temperature of the rooting environment in the growth chamber experiment was the same throughout the growing season. The greenhouse, the temperature of the rooting media in the winter ranged from 0.5 – 2 °C at night to 19 –21 °C during the day. In the spring and summer, solar irradiation increased these temperatures to 16 -18 and 28 – 34 °C, respectively. To test the effect of the temperature at rooting, we tested the rooting of fragments of hanging A. donax stems at different temperatures in April and May, a period that in the year-round temperature controlled experiment the success rates were 45 ± 10% in 1998, and 45 ± 21% in 1999. In this test using constant temperatures, no rooting occurred at 10 °C during the 40 days of the experiment . At 15 °C, rooting was better than at 10 °C , but significantly less than at 17.5, 20, and 22.5 °C . In the greenhouse experiment, the seasonal pattern of rooting success was present, but the inherent advantage of fragments of the hanging stems in the winter months was masked by the negative effect of the lower temperatures of the rooting media. The temperatures chosen for the year-round temperature controlled experiment were selected to reflect the temperature conditions in the habitats invaded by A. donax in Southern California in the months of April and May. From the results of this April constant temperature experiment, it appears that the lower night temperature in the 28/15 °C for 14/10 h experiment led to a reduction in rooting success from the maximum possible in that month. This reemphasizes the effect of in situ temperature on the success of stem fragment meristem rooting, and the ecological danger of the floating stem fragments in shallow waters. The inherent seasonal pattern observed in the year round temperature controlled experiment may have resulted from cycles in the concentrations of the plant growth regulators that play a role in the growth of the side shoots, and the apical dominance of the top of the main stems. One of the growth regulators that plays a major role in the regulation of apical dominance is indole-3-acetic acid. The effect of IAA on the rooting of axillary bud on A. donax stem fragments throughout the growing season was tested through the use of exogenous IAA in the rooting medium, and the determination of endogenous IAA levels in the shoots that grew from the axillary buds. When the stem fragments and their axillary buds were exposed to 5 and 10 µM IAA in the rooting medium, the difference between the hanging and the upright stems disappeared. The main effect of the exogenous IAA was a significant improvement of the rooting percentage and speed of the upright stem fragments in the winter and spring periods, so that the difference between the two stem types was minimized. The exogenous IAA had little effect on the rooting success and speed of the hanging stem fragments . At 20 µM exogenous IAA, the highest concentration applied, the success rate and the speed of upright stem fragment rooting decreased from the optimum observed at 5 and 10 µM, almost down to the percentages and t50 observed in the absence of the hormone . The IAA in the rooting medium may have reached the axillary bud through the vascular bundles of the main stem piece, directly through the cuticle of the bud itself, which was positioned immediately below the rooting medium surface, or both. In early studies into the effect of IAA on plant growth, the growth regulator was sometimes applied to the leaf tissues, and the position of the axillary bud in the rooting medium could have resulted in a similar situation.

The root biomass among these translocation lines ranked as follows: Pavon 1RS.1AL > Pavon 1RS.1DL > Pavon 1RS.1BL > Pavon 76. On the other hand, in Colorado, the ‘Amigo’ 1RS.1AL translocation from a different rye source in wheat cv. ‘Karl 92’, showed 23% yield increase under field conditions over its winter wheat check, Karl 92 . In 1RS.1BL translocation wheats grown in acid soils, roots were thinner and there was a higher root length density, and this likely enhanced the root surface area . The yield advantage of 1RS translocation lines may be partly attributed to the increase in root biomass that increases uptake of water and nutrients from the soil . To elucidate the mechanisms responsible for increase in root biomass in 1RS wheats, it is necessary to genetically map and identify loci responsible for enhanced root traits. The objective of this study was to develop a consensus genetic map of the 1RS- 1BS chromosome arms using a population of induced homoeologous recombinants, and subsequently use the genetic map to tag the 1RS chromosomal region responsible for increased root traits. Molecular linkage maps of cereals are being improved rapidly by adding new types of markers, merging different species-specific maps and comparative mapping of markers between related genomes. Efficient use of resulting dense maps requires detailed insights into the relationship between genetic and physical distances.PCR-based markers were developed for 1RS.1AL and 1RS.1BL wheat-rye translocations in wheat .

However,stackable planters these translocations have not been used extensively to generate consensus map of wheat and rye chromosomes which would be a useful tool to study different agronomic characters influenced by the presence of rye chromatin. Recombination mapping has an advantage over deletion-bin mapping in generating higher resolution maps. In deletion mapping, the number of available breakpoints, hence the number of bins, limits the resolution, and after the set developed by Endo there have been no further efforts to generate new breakpoints. In genetic mapping, resolution is limited primarily by the number of available markers, and these increase steadily. Another advantage of recombination mapping is the ability to study the genes on rye chromosomes, where, because of diploidy, it is difficult, if not impossible, to practice deletion bin mapping. In this study, using a set of recombinants, we generated a consensus map of 1RS-1BS that integrates physical and molecular markers and attempted to narrow the regions containing major QTL for root characteristics. Experimental material was provided by Dr. A.J. Lukaszewski, University of California, Riverside. It consisted of a set of rye-wheat recombinant lines in a nearisogenic background of bread wheat cv. Pavon 76. Pavon 76 is a spring hard wheat from the breeding program at Centro Internacional de Mejoramiento de Maíz y Trigo , Mexico. The set includes centric translocation 1RS.1BL in Pavon 76, where the 1RS arm is from cv. Kavkaz and 1BL arm is from Pavon 76 and a set of 68 recombinants of the 1RS arm with 1BS in Pavon 76 . 1RS-1BS recombination was induced by the absence of the Ph1 locus. All recombinants are single breakpoints; therefore, the short arm of each recombinant chromosome contains one segment of 1RS and a complementary segment of 1BS. Each recombinant has a normal 1BL long arm. Since they were produced by crossing over, they can be used to generate a genetic map.

However, since recombination in wheat is predominantly in the distal portion of each arm , physically, these recombination breakpoints cover only the terminal 40% of the arm’s length. A phenotyping experiment to study root characters was set up in the glasshouse in sand-tube cultures in PVC tubes, 80 cm long and 10 cm in diameter during 2006, 2007 and 2008. The study involved five lines: Pavon 76 and Pavon 1RS.1BL as the parents, and recombinants T-14, 1B+38, and 1B+2 . The three recombinant lines were chosen from the set of 68 lines to subdivide the recombining portion of the arms into three segments of roughly equal lengths. Seed of these five lines were surface sterilized with 5% commercial bleach for 5 min, washed for 10 min in distilled water, soaked in water for 24 hrs and then germinated on wet filter paper in Petri dishes. Five day old seedlings were transplanted to 80 cm PVC tubes containing 1 m polythene tubing, closed at one end, with 8.5 kg of silica sand #30. Two small holes were made at the bottom of polythene tube to allow drainage of excess water. The PVC tubes were supported in metal frames and arranged in a randomized complete block design with four replicates. Plants were harvested 45 days after germination when the differences for root characters could be efficiently measured among different recombinant lines. Data for different shoot characters were recorded and the tubes containing roots were stored at 4oC until processing. Roots were washed and recovered without damage using a floatation technique . The shoot characters measured were longest leaf length , maximum width of the longest leaf , leaf area , plant height , number of tillers , and dry shoot biomass .

The root characters measured were number of roots greater than 30 cm , longest root length , total length of roots greater than 30 cm , shallow root weight , deep root weight , dry root biomass , and root biomass to shoot biomass ratio . The shoot and root data were subjected to the analysis of variance for each year . The combined ANOVA across years was performed for each measured and calculated trait. The overall rooting ability of each genotype was calculated by ranking each genotype for individual root traits in each replication in each of the three years. Genotypes with the highest values were ranked 5 and those with the lowest values were ranked 1. Subsequently, all the ranks of root characters for each genotype were summed providing a measure of the rooting ability index for each genotype at different replications . The genotypic rank sums averaged across the years were subjected to the non-parametric Quade analysis developed for randomized complete block designs to differentiate genotypes for overall rooting ability. A calculative approach was used to generate a genetic map of the 1RS-1BS arms. The 1RS-1BS map from the previous study , based on twelve markers and 103 recombinants, was enriched by eight additional molecular markers and recalculated using 68 lines. The population of recombinants was divided into two configuration groups, 1B+ lines with distal 1RS, and T- lines with distal 1BS, comprising 34 lines each. Both groups were scored seperately for the presence/absence of each of the 20 markers, with the presence of a marker denoted as a score of 1 and the absence as 0. Each line was then ranked according to its total score for all markers and each group was further divided into 11 subgroups on the basis of its ranking. Thus,stacking pots the entire mapping population was divided into 22 subgroups . In this fashion, the 20 markers subdivided the genetic maps of the arms into 15 intervals . Phenotyping of recombinants was necessary to test the general applicability of the consensus 1RS-1BS map in locating a 1RS region showing better rooting ability. Various shoot and root traits were studied using two parents and three recombinants covering the whole 1RS-1BS map. There were significant differences among years for all shoot characters measured, except for the maximum width of the longest leaf . Significant differences were found among the genotypes for shoot characters, except for maximum width of the longest leaf and leaf area. Genotype × year interaction was significant only for the number of tillers per plant . This interaction was due to changes in the magnitude of the genotypic means across different years rather than changes in ranking of the means. Therefore, shoot characters in Table 2 are represented by means averaged across years. Pavon 1RS.1BL was taller, had longer leaf length and a greater root to shoot ratio than Pavon 76. Since Pavon 1RS.1BL and Pavon 76 had similar shoot biomass , greater root to shoot biomass ratio in the former genotype indicated greater root biomass in 1RS.1BL than Pavon 76 . Leaf area in 1B+2 was the highest followed by Pavon 1RS.1BL .

Despite significant differences observed among the genotypes for shoot characters, the differences were relatively small, except for the shoot biomass in 1B+2 in the third year which was due to greater number of tillers per plant and plant height . Otherwise, the rest of the genotypes did not show large differences for combined as well as for individual years for most of the shoot traits. There were significant differences among years for all root characters measured . Significant differences were found among the genotypes for all the root characters measured, except for longest root length. The genotype × year interaction was significant only for the number of roots greater than 30 cm. Therefore, means for root characters were averaged across three years . The number of roots greater than 30 cm and root biomass in Pavon 1RS.1BL were greater than those in Pavon 76 which confirmed the results reported earlier . Number of roots greater than 30 cm in Pavon 1RS.1BL, 1B+2 and 1B+38 were similar, but greater than those in Pavon 76 and T-14 . A similar trend was observed for the total length of roots greater than 30 cm. Shallow root weight was highest in Pavon 1RS.1BL followed by 1B+2 , and 1B+38 . The lowest shallow root weight belonged to T-14 . Deep root weight in Pavon 1RS.1BL, 1B+2, and 1B+38 were similar, but greater than those in Pavon 76 and T-14. Greater dry root biomass observed in Pavon 1RS.1BL compared to Pavon 76 was due to a combination of greater shallow and deep root weight in the former than the later genotype . Quade analysis was used to compare the rooting ability index of the examined genotypes based on the mean rank sums of root characters. The Quade statistic ranged from -11.0 for T-14 to 11.5 for Pavon 1RS.1BL . Differences among Sj were statistically significant at P< 0.10 . Pavon 1RS.1BL had the highest RAI followed by 1B+2 and 1B+38 . Pavon 76 and T-14 had the lowest RAI . Genetic mapping in wide hybrids has been performed for many plant species, particularly in diploids including barley , chickpea , lentils , onion , Nicotiana species , and tomato . In the early days of genetic mapping with molecular markers, wide hybrids were the approach of choice, for it guaranteed much higher levels of polymorphism than in intra-specific hybrids. Despite notable instances of non-Mendelian segregation and skewed distribution of recombination, wide hybrids produced useful genetic maps with higher marker saturation at considerably less cost and effort . Use of wide hybrids in allopolyploids is more complicated than in diploids as allopolyploids tend to have some kind of chromosome pairing control system in place that limits crossing over to homologues. Hence, homoeologous pairing may be low or even non-existent. In this study, 68 recombinants produced by crossing over were used. These recombinants were selected at random from a population of 103 such recombinants developed by Lukaszewski . The entire recombinant population was selected from a population of ca. 17,000 progeny with the Ph1 system disabled. If the assumption is made that crossing over in the Ph1+ and Ph1- wheats is the same, and they appear to be, except for the absence of multiple crossovers per arm then the sample analyzed here would be equivalent to a population of 136 back cross progeny, a sensible number giving the maximum resolution level of 0.7 cM. Physical distribution of 68 recombinant breakpoints used in the present study is shown in Figure 3. With a total of 20 physical and molecular markers, we constructed the combined genetic map of 1RS-1BS recombinant breakpoints in Pavon 76 background. The genetic map produced here has an average density of 2.5 cM. The maps shown in Figures 2 and 4 represent only the physical 40% of the distal ends of the 1S arms as no recombination took place in the proximal 60% of the arm. Any loci in this region would show complete linkage with the centromere.

Several applications have been discovered, such as for wastewater treatment, fish farming, shrimp breeding, and hydroponics. These are further substantiated by Agarwal and coworkers , for such specific issues as the disinfection of infected surfaces, the degradation of organic compounds, and the disinfection of the water itself. The effects of increased yield of fish due to higher dissolved oxygen content are summarised by Endo et al. . The usage of hydrogen nanobubbles in gasoline to improve the calorific yield is also reported by Oha et al. . Other projected uses include the use of nanobubbles as contrast agents for the ultrasound imaging of tumours, as reported by Cai and co-workers, as well as reduction and removal of deposits of calcium oxalate, which is similar to the composition of kidney stones in rat kidneys, as presented by Hirose et al. Another application of the nanobubble’s ability to permit salts to crystallize is the design of self-cleaning membranes for desalination of water, which use nanobubbles as electrically conductive spacers and pass current through them to force the salts to crystallize on the nanobubble surface,nft channel which will permit easy removal of the accumulated salts. This was demonstrated and presented by Abida et al.

The pressure balance of the nanobubble is considered to be given by the Young-Laplace equation, which, as explained above, equates the internal pressure, external pressure and the surface tension. The first of the four forces that we consider in the Young-Laplace equation is internal pressure. It is proportional to the surface area of the nanobubble, and is assigned a positive sign since it acts to increase surface area. The second is the external pressure, given by the hydrostatic pressure acting on the surface of the bubble, which also decreases the surface area and is negative. The third is the surface tension, which acts along the surface area at the molecular level. The surface tension acts to decrease the surface area, hence the radius and size, and can also be assigned the negative sign. However, a fourth force which is thought to be integral to the stability is the electrostatic repulsion between hydroxide ions adsorbed to the surface of the nanobubble, or, possibly in the cloud surrounding the surface. This repulsion seeks to reduce the contact between the ions on the surface of the bubbles, which also acts to increase the distance between the ions, thus increasing the surface area, and therefore results in a positive pressure. The nature of the interaction between ions can be characterized by the expression for Coulombic repulsion. Since one hydroxide ion is of the order of 1 nanometre in diameter, and most nanobubbles are two orders of magnitude greater in size, we can ignore the curvature of the distance between them and take it to be linear.

The repulsion should, in theory, affect all neighbouring hydroxide ions, but is assumed to be insignificant beyond the nearest neighbours. We also assume the spatial arrangement of these ions over the surface to be close-packed in nature, since the repulsion is equal in all directions, and they would ideally assume a close-packed formation. This arrangement of ions is shown schematically below, in Fig. 1a, and as shown in Fig 1b it is assumed, due to close-packing, that they assume the formation of a rhomboidal unit cell, of side and diagonal length denoted by x, which will be referred to subsequently as the inter-ionic distance. That the nanobubble shrinks due to outward diffusion of the gas contained within is, of course, undisputed, but the precise methods and the rate of diffusion are highly debated. Previous theoretical studies have always assumed a model with a higher mass transfer coefficient, or longer time scales for the process to account for the reduced rate and the high lifetime of the nanobubble. However, it is reasonable to suggest that the change in the rate of diffusion can be attributed to two things: the velocity due to the Brownian motion of the nanobubble, and the inhibition of the diffusion due to the adsorbed hydroxide ions on the surface. In this chapter, the possible effects of Brownian motion are examined for the effect on the rate of diffusion that they may possess. Earlier studies have shown that nanobubbles can be formed by supersaturation, where the solubility limit of the gas, when surpassed will permit the gas to precipitate and form bulk nanobubbles as reported by Matsuki and co-workers .

The shrinkage of nanobubbles has so far been thought to be governed by Fick’s Laws, since it is a case of how fast the gas can dissolve into the surrounding fluid. Thus, according to the first law, it must be directly proportional to the outward gas flux, but the constant is still the diffusion constant D0 for the diffusion of the gas into water. However, this only holds true where the surface area of the nanobubble remains constant. It is, however, possible, that the outward diffusion is a case of Fick’s second law, since the surface area that is available to the gas to diffuse outward also changes according to size, and that this surface area determines the rate of shrinkage and thus the lifetime of the bulk nanobubble. It is then reasonable to suppose that the cause of the change of surface area available for diffusion is the change in the surface area occupied by hydroxide ions combined with the decreasing radius of the bulk nanobubble. The rationale for the assumption that the hydroxide ions adhere to and are released the nanobubble surface is based on two observations, as mentioned before. Firstly, the observation that all interfaces formed by water are negatively charged, and we consider nanobubbles to be a special case of a gas-water interface which may be charged in the same way. Secondly, the zeta potentials measured for nanobubbles are all negative, indicating that a negative ion present in pure water is responsible for the negative charge, which by elimination is the hydroxide ion. Further observations also indicate higher negativepotentials for more electronegative gases, such as oxygen and nitrogen, than for other reported gases such as argon and xenon as reported by Ushikubo et. al.. That nano- and micro-bubbles release hydroxide ions as they shrink is a well-known phenomenon. The stabilization and the shrinkage can be considered to be related to the same phenomenon; thus, the ideal case can be taken to be a nanobubble that is newly formed with no hydroxide ions at the surface at the instant of its formation of an interface. Here, the hydroxide ions present in the water immediately surrounding the bubble, in the hydrodynamic layer, adhere almost instantaneously, the time taken for the adsorption to occur being too small in comparison to the overall timescale to be important. As a concentration gradient is then formed between the water layers at the nanobubble surface and the bulk fluid,hydroponic nft more hydroxide ions begin diffusing from the bubble through this diffusion layer to the surface of the bubble. The thickness of this layer can be found by Prandtl’s equation, where the fluid velocity is the velocity of the Brownian motion of the bubble as predicted by the Langevin equation, using the Ornstein-Uhlenbeck process. The same layer also acts a diffusion region for protons, which diffuse in from the bulk layer once they are depleted or their concentration changes, and must also be affected by the distance they must diffuse through to reach the nanobubble surface.At the same time, protons from the diffusion layer also reach the hydroxide ion-rich surface, but much more slowly, at a rate about five times slower than the hydroxide ions. Upon reaching the surface, they start eliminating the hydroxide ions into water molecules, which further increases the dilution of both ions, and encourages diffusion from the bulk layer to the interface, which is probably a monolayer.

When the three processes, of hydroxide diffusion, proton diffusion, and hydroxide elimination by protons, are in steady state or in dynamic equilibrium, we have a fixed amount of area which is not covered by hydroxide ions, however temporarily and will allow the diffusion of gas into the water. Taking an average, we can define a percentage of surface area of the nanobubble, which will remain available for diffusion, which will be in proportion to the radius of the bubble. This can be done by taking the size of one hydroxide ion, then finding the capacity of a nanobubble’s surface to adsorb hydroxide ions, correlating it with the number of ions being eliminated, and taking a ratio with the capacity which is a function of area, which is a function of radius. The rate at which the adsorption of the hydroxide ion takes place would then depend on two separate phenomena: firstly, the repulsion by the hydroxide ions already physisorbed onto the surface, which would force the ion to move along the surface until it finds a location that is unoccupied, and secondly, the velocity of the hydroxide ion as it travels through the hydrodynamic layer of the nanobubble. The velocity can be found by calculating the surface charge on the nanobubble, and using it and the initial distance between a particular ion to find the potential that drives it to move. The potential for the hydroxide ion to move to the nanobubble surface decreases as the surface charge increases, and thus the rate will eventually dwindle down to zero as the bubble achieves stability, and the potential will reach a constant value. The rate for the elimination of the physisorbed hydroxide ions, on the other hand, will only increase the surface charge density increases, since the elimination is accomplished by positively charged protons attracted to a negatively charged surface. The same equations for ionic mobility can be used to calculate the velocity of travel for the protons, but there is no equation needed for the rate of adsorption, as they simply react with the adsorbed hydroxide ion to give two molecules of water. The balance between these two rates thus depends on the time at which the reactions are taking place, which will ultimately determine the area needed for the diffusion of the gas into the water. To find the ion mobility, we first consider an ideal case where a newly-formed and shrinking bubble has no hydroxide ions physisorbed onto its surface, and is formed in pure water with a pH of 7. This gives us, assuming a perfectly uniform distribution of ions in the water, a concentration of 10-7 moles of hydroxide and protons each in the surrounding hydrodynamic layer. Thus, the amount of both available to be physisorbed can be found by simply taking a section of the hydrodynamic layer up to the distance from the surface where we wish to find the concentration and time needed to reach the surface for the ions present at that distance from the surface. We take the volume of this section and multiply by molarity and Avogadro’s number to get the actual number of ions present, as shown below. In the derivation of the force balance presented in section 3.2, the formula takes into account the contribution of the repulsion between hydroxide ions adsorbed to the surface of the nanobubble. This section estimates the number of the ions adsorbed to the surface of the nanobubble, and uses the terms associated wit their arrangement to calculate this contribution and to examine the possibility that they can, indeed help to balance the inward and outward pressures exerted on the nanobubble surface and thus provide an explanation for their stability. Both possibilities of stationary and nanobubbles in motion are assumed and calculated to provide estimates for the repulsive force, and are substituted along with representative values in the derived equation for the force balance, and the result is presented. However, the stationary nanobubble is an ideal case, and in actual situations the bulk nanobubble is usually in motion due to Brownian motion, which also prevents it from rising to the surface. Thus, it can be established that the bulk solvent for a bulk nanobubble in motion, only consists of the boundary layer that moves with the nanobubble as it moves through the solvent. It also must supply the ions needed to stabilise it, and must contain the ions that are adsorbed.