plastic gardening pots – Nature Ecrogrow

Population growth rates vary tremendously geographically

The A2 vision focuses primarily on Low Density Residential development, while the B1 scenario is relatively evenly split between Low, Medium, and High Density types, and AB32-Plus favors Medium and High densities. In addition to modeling these three scenarios, we examined additional versions of A2 and AB32-Plus in which we held population constant at the B1 level, and in which we held population, energy efficiency for both homes and vehicles, and utility-portfolio assumptions constant. This step provides a more analogous comparison of the land use influence within the three scenarios.After modeling urban-growth footprints for the A2, B1, and AB32-Plus scenarios, we calculated two main categories of GHG emissions for the new urbanization produced by each scenario. These calculations help provide a ballpark sense of the magnitude of emissions variations that can result from different policy approaches. For the sake of simplicity, we focused on emissions from the operation of motor vehicles and residential structures, not their life cycle emissions from construction and materials, because operating emissions are likely to be a large majority of the total in both cases and thus estimate the emissions trade offs of different urbanization trajectories. Within the transportation category of GHG emissions, many factors potentially affect individual travel decisions within a given type of urban location, including: land use mix and densities in the surrounding area; the availability, attractiveness, and price of alternative travel modes; the nature of the travel route network, including available route choices and congestion; social pressures, influences, and incentives; and self-selection of residents living in that type of urban location.

An extensive field of travel modeling has attempted to take many of these variables into account ,plastic gardening pots usually projecting travel in the future based on changes to current conditions. But given that travel, like many other behavior choices, is highly multi-determined, the process is problematic. Even within time frames of 20 years or less, travel forecasts are often highly inaccurate , and have had particular difficulties in incorporating variables related to land development and urban design. For a longer time frame such as 2050, social factors, economic conditions, and behavioral changes are likely to play a larger role, changing travel demand in unpredictable ways and making modeling even more problematic. Accordingly, we have chosen here to keep our calculations to a very basic level, simply extrapolating travel based on the existing range of travel differences between residents in areas of different densities. Household travel surveys done by SACOG show that household vehicle miles travelled vary by a factor of 6 between households in low-density and high-density locations . Some of this difference may be due to household size, composition, and demographics, but much is probably due to accessibility factors , including proximity to jobs, shopping, and schools, and alternative transportation modes. All of these environmental variables can be assumed to vary in unison: the B1 and AB32-Plus storylines assume improved balance of jobs, housing, and shopping within communities; improved bicycle, pedestrian, and public-transit options; rising gas prices and/or carbon taxes; and other economic incentives such as higher parking and road-use charges. Likewise, we can assume that these multiple changes tend to influence resident behavior in synergistic ways; for example, individuals drive less in a dense urban environment because people discover alternatives and are influenced by their peers. Thus, the assumption of a strong difference in driving between low- and high-density environments in 2050 for purposes of back casting scenarios seems reasonable.

Transportation emissions also depend on the fuel efficiency of motor vehicles. The average fuel efficiency of American vehicles remained more or less unchanged from the mid-1980s through the early 2010s, and so for purposes of illustration we assumed only modest further improvements in the A2 scenario until 2050. In the B1 scenario, we assumed additional efficiency increases of 2% a year , which would plausibly be brought about through improvements in the US national CAFE standards. For the AB32-Plus scenario, we assumed improvements of 4% a year . These assumptions are reasonable given recent efficiency improvements such as the spread of hybrid vehicle technologies. Despite accelerating globalization, food security in most of the developing world depends upon local food production. Most rural citizens in developing nations are involved in agriculture . Also, most locally produced goods are consumed locally, so increasing local productivity and slowing population growth remains a central food security issue . Over the past few years, energy price increases, bio-fuels and food scarcity have led to higher global food prices and price volatility.Rapidly increasing consumer prices limit food access. Increased price volatility reduces the benefit that small scale farmers derive from higher producer prices. Bio-fuels create competition between poor people in the developing world and energy consumers in the developed world. While higher priced commodities can bring direct benefits to farmers, these benefits will not be attained without significant and sustained investments in agriculture to increase production and in programs that reduce price volatility. High and volatile food prices make local production even more important for food insecure regions. With high prices and continually increasing transportation costs, producing more locally will become an important source of vitality for programs focused on reducing poverty .

Connecting new innovations in crop research with improved outcomes in the developing world will likely require public sector investments . The recent entry into small-scale agricultural investment by the Bill and Melinda Gates Foundation has added new vibrancy to efforts focused on understanding barriers to increasing yields and sustaining production in the face of climate change. This paper explores the convergence of three different trends: changes in agricultural production, changing climate and increasing population. Our assumption in the analyses presented is that that some countries will continue to have substantially less purchasing power than others over the next few decades. Although global trade is important, we assume that regions with small agriculturally based economies today are unlikely to transform themselves into industrial nations without first improving their agricultural productivity . The collision between increasing global food demand, competition for food with developed world energy consumers and increasingly difficult production conditions means that the food security situation is likely to worsen. Climate change will potentially bring reduced rainfall over some regions, and increased rainfall over others. The impact of these changes on agriculture must be anticipated and planned for . To this end, this paper examines potential trends in per capita cereal production and rainfall. While other foods make substantial contributions to diets in many areas,blueberry pot size per capita cereal production is indicative of general food availability in most developing countries. If the current rates of yield and population increases persist, many regions will see substantial declines in cereal availability. Greenhouse gas-induced reductions in monsoonal rainfall could exacerbate these grain shortages. On the other hand, relatively modest yield improvements in the least developed nations could lead to improved levels of per capita cereal production by 2030. If done sustainably, raising yields in these poorest nations may be the most technologically feasible way of addressing global cereal demands while reducing poverty and food insecurity.This study uses five sets of data : agricultural statistics, population, observed rainfall and simulated rainfall from 10 different global climate models . Based on simple equations describing the conservation of mass, momentum, radiation and other key dynamic factors, the global climate models simulate the full atmosphere at sub-daily time steps. These models may be constrained by observed sea surface temperatures , or run combined with ocean models in full simulation mode. When constrained by observed SSTs, the models produce circulations resembling the observed atmosphere. This analysis uses 24 of these 20th century simulations from 10 models to examine how well the models recreate observed seasonal rainfall variations between 1980 and 2000 rainfall observations. Our study also examines climate change simulations for the 21st century which predict a doubling of atmospheric CO2 concentrations by 2100 . When combined with ocean models and forced by projected trends in greenhouse gasses and aerosols, climate simulations can be used to examine the impact due to anthropogenic emissions into the atmosphere. This study examines 28 simulations from a standard emission scenario—the Special Report on Emissions Scenarios, SRESA1B. Individual simulations were weighted in such a way as to give each model an equal influence.Between 1961 and 1986, global total cereal yield increased by 89%, harvested area expanded by 11% and global per capita cereal production increased enormously, with a maximum in about 1986 .

Population, meanwhile, grew by 60% over those 25 years, resulting in an increase in per capita cereal production from 284 to 372 kg of grain per person per year. The last 21 years have seen slower population growth accompanied by slower yield improvements and a 2% reduction in the total area harvested. Surprisingly, over this period according to FAO and UN data, per capita harvested area, fertilizer and seed use have all declined by 20–30% . Presumably, higher yielding seeds, double cropping of rice, irrigation and more effective farming techniques have made up for lower per capita inputs. Per capita production is now about 350 kg of cereal per person per year, 6% less than the 1986 maximum. The relative stabilization of global per capita production hides the increased use of cereals for biofuels, alcohol and meat production, as well as significant variations between regions.Four regions have more than doubled their population since 1980: Eastern Africa, Western Africa, Middle Africa and Western Asia. Central America, Southern America, Northern Africa, Southern Africa, Southern Asia, and Southeastern Asia have seen their populations increase by more than 50%. Since 1980, two out of every three people born now live in Asia. Another one out of four lives in Africa. Across most of Africa, harvested area has increased substantially more than yields . In Eastern Africa, for example, yields have only increased by 25% since 1980, while harvested area increased by 55%. In Western Africa yields increased by 42% while harvested area increased by 127%. Yields, per capita harvested area and production remain very low. Across much of the rest of the world, harvested areas have remained fairly steady and yields have been the primary driver of agricultural growth. Our evaluation of FAO statistics shows that Southern America, Northern America, Eastern, Southern and Southeastern Asia have experienced greater than 70% increases in yields since 1980. Today, huge regional disparities in yields remain the norm. In 2007, cereal yields varied by almost 600%, from ~1,000 kg ha−1 in Middle Africa, to more than 5,000 kg ha−1 in Northern America, Eastern Asia, and Europe.4 Given that global and regional tendencies in per capita harvested area and yields appear fairly predictable on 10 year scales , we may plausibly use the observed trends to project to 2030. Table 4 presents the anticipated 2007 to 2030 changes, expressed as kg of cereals per person per year, and as percent changes compared to 2007. Figure 3 shows the historical and projected per capita cereal production for selected regions. Globally, a return to per capita production of the late 1960s, when per capita production was near 327 kg per person,appears likely. Eastern Asia may return to 1980s production levels and Southern Asia to 1960s levels . Declines in heavily populated Asia could re-expose millions of people to chronic undernourishment.5 Central and Southern America may experience 18–20% declines in per capita cereal production levels. Eastern and Middle Africa, however, may be affected most, with more than 30% reductions in already low per capita cereal production levels, with Eastern Africa changing from 131 kg per year in 2007 to 84 kg per person per year in 2030. United Nations projections suggest that the population of Eastern Africa will increase by 191 million people by 2030—a net increase second only to Southern Asia. While imprecise, it is instructive to evaluate simple food balance equations, examining the number of people who could be reasonably supported by the anticipated levels of grain production . We do this by calculating the theoretical population without food . Globally, the 2030 food balance estimates suggest that as now, we will still have enough grain to maintain our human population,albeit at a low baseline value of 1,900 calories a day. This estimate, however, does not take into account grain consumption associated with livestock production, biofuels or industrial applications.

Spurs that bore fruit in a given year rarely flowered or bore fruit in a subsequent year

In spite of these efforts, relative fruit set is still variable and little improved since the early data reported in 1959 by Kester and Griggs. But, is fruit set the main limiting process for almond productivity? Another approach could be to increase the number of flowers per acre — but that approach demands more information on the eco-physiological basis that regulates flowering of almond spurs . Individual spurs tend to alternate bear with only a small percentage of spurs flowering the year after bearing . The authors have observed tagged spurs in outer canopy– exposed positions to live at least 15 years. To investigate this, an almond spur dynamics research project was initiated by Lampinen and colleagues in 2001. This study was designed to quantify the dynamics of spur renewal, fruitfulness and longevity and to determine how these dynamics are impacted by orchard management practices. Results from the study indicated that the number of flowers borne by individual spurs is a function of spur leaf area in the previous year and whether or not the spur bore a fruit in the previous year.Furthermore, spur mortality was much higher in spurs that had low previous year spur leaf area because fruit bearing competes with leaf growth and decreases the amount of source organ available on bearing spurs . Although there was a strong tendency for individual spurs to not bear fruit in successive years,pot raspberries whole trees or orchards are not strongly alternate bearing because fewer than 20% of the spurs on a tree bear fruit in a given year . In addition, the spur dynamics study documented that the key to ensure the largest flowering over an orchard’s life is to have the largest number of spurs possible with the optimal leaf area for flowering.

Proper irrigation during the previous year vegetative season and even after harvest can help to minimize spur death and has been reported to have a critical impact on subsequent bloom and fruit set . The almond spur dynamics study also provided information regarding the importance of PYSLA in determining subsequent spur flowering, fruit bearing and survival as well as the fact that spur fruit bearing in turn, reduces spur leaf in the same year . Thus, spur flowering and fruiting in two sequential years is relatively rare . However, the total number of flowering spurs on a tree may be of limited significance if greater relative fruit set of the flowers can compensate for decreased flower numbers in the orchard. Thus, understanding the relative impact of flower number and relative fruit set on almond tree yield in commercial orchards is essential for guiding efforts to improve orchard productivity and help growers determine the most profitable practices for almond crop management. To address this question we analyzed flowering and fruit set data recorded during the almond spur dynamics project. The study was conducted in a 145-acre orchard, planted in 1996, at 24 feet between and 21 feet within rows. The orchard planting consisted of rows of ‘Nonpareil’ alternating with pollinizer rows of ‘Monterey’ , and ‘Wood Colony’ . The orchard was located in Kern County on a sandy-loamy soil. Irrigation was carried out by micro-sprinklers and irrigation schedule was based on weekly measurement of midday stem water potential that was maintained between −0.7 and −1.2 MPa. Nitrogen was applied at 110 to 220 pounds per acre and leaf N content was between 1.95% and 2.45% over the period of the experiment. Bee hives were placed at a density of two to three hives per acre prior to bloom.

During the experiment, weather conditions during the pollination period were not limiting for bee activity. The orchard was divided into six equal-sized replicate blocks and 50 spurs were tagged in eight ‘Nonpareil’ trees within each of the six blocks. A total of 2,400 spurs were marked with aluminum tags in late March and early April 2001. Twelve spurs were selected on each of the northeast and northwest quadrants of individual trees and 13 spurs were selected on each of the southeast and southwest quadrants of the same trees. Tagged spurs were located at positions ranging from shaded to exposed portions of the canopy at a height of 3 to 12 feet. During the first 4 years of the study, lost tags or dead spurs were replaced with spurs in close proximity with similar light exposure to the original tagged spurs. The dynamics of annual growth, flowering, fruitfulness and spur mortality were quantified annually. For more detail see Lampinen et al. . The number of flowers produced on each tagged spur was counted in the spring of each year from 2002 through 2007. Multiple year records of PYSLA , previous year bearing, number of flowers in the current year and number of fruit in the current year were used to assess spur behavior in relation to PYSLA in spurs that bore no fruits in the previous year. These analyses involved data from 6,980 spurs spread over the 6 years. Kernel yield of the individual trees with tagged spurs and the kernel yield of the orchard containing those trees were also recorded for 6 years . Statistical analyses were carried out using ANOVA to test the significance at P < 0.01 of relationships between PYSLA and current year spur flower density , current year spur fruit density and current year spur relative fruit set. The same test was also used to test the significance of the relationship between tree yield and tree spur population relative fruit set and spur flower density . The number of flowers differentiated during the previous year is the first component of yield in fruit trees .

In almond spurs, flower formation was closely related to spur leaf area in the previous year . Thus, if the leaf area of each spur on a tree were known, the number of flowers that a tree would bear in the following year could be estimated, and, if spur relative fruit set were constant, spur fruit bearing and yield of that tree could be predicted. However, although the relationship between spur fruit density and PYSLA was significant, it was weaker than the relationship between spur flowering and PYSLA . This was because fruit set was highly variable in almond across years. Relative fruit set varied from 19% to 36% . These data apparently support the large effect of season, and particularly weather conditions, on the fruit set process. In almond, rainfall during the bloom period has been reported to affect pollinator activity and to wash pollen off stigmas . Anther dehiscence also can be affected by rain and high relative humidity . Temperature affects pollen germination, pollen tube growth , ovule degeneration and pollinator activity in the field . Wind can also affect pollinator activity. On the basis of this information, some have hypothesized that yield fluctuations can be explained mainly by variations in climatic factors . Actually, large relative fruit set variability also occurred among individual trees . This fluctuation could be a result of “on-trees” and “off-trees” occurring in the same orchard and season . On the other hand, fluctuations of relative fruit set of spur populations in different trees exposed to the same climatic conditions suggest that climatic conditions are not the major factor influencing tree spur population fruit set. In this experiment, at the spur level, there was no correlation between the PYSLA and relative fruit set in the current year . Thus, whereas previous year conditions are fundamental for flower formation on spurs , previous year leaf area did not appear to influence current year spur relative fruit set. Furthermore, spur fruiting was associated with reduced spur leaf area in the current season, suggesting that current year spur leaf area does not exert any influence on spur relative fruit set . In this experiment, the number of nuts borne by individual trees was significantly correlated with the number of nuts borne by the tagged spur populations in those trees . This suggests that our spur sample was relatively representative of the spur population of the trees. On a whole tree basis,plastic garden pots tree yield was not correlated with mean relative fruit set measured on tree spur populations. Instead, tree yields appeared to be more closely correlated with flower density on the tagged spur population. Thus, while relative fruit set is obviously important, it was not the primary yield limiting factor in this orchard situation, and increased relative fruit set when floral densities were low did not compensate for lower numbers of flowers . There were significant correlations between spur flower density and tree yield over years ; for individual years, the relationship was significant in 4 of the 6 years of our experiment . On the other hand, the relationship between tree relative fruit set and tree yield was not significant in any of the 6 years of the experiment.

However, it should be noted that the coefficients of determination were low due to the large number of points and the limited size of the spur sample compared with the total number of spurs borne by each tree; only 5.3% of the variability in tree yield can be explained by spur flower density. These results support the validity of flower density as an important parameter in the evaluation of almond cultivars . These data support the importance of total flower production for obtaining large crops. As a result of these spur dynamics studies, it is clear that the key to optimizing yields in commercial almond orchards is to focus on maximizing healthy populations of productive spurs. Some spur mortality is unavoidable and linked to insufficient spur leaf area associated with spur bearing and spur shading . Thus, continued productivity is dependent on spur renewal that is achieved by ensuring that there is annual growth of as many existing spurs as possible and new shoots that provide sites for new spurs . Health of spurs is also a function of total canopy light interception and good light distribution with the tree canopy . It is clearly important to select cultivars with the ability to produce large numbers of flowers and have crop management practices aimed at limiting abiotic stresses during the vegetative season . In an experiment not potentially biased by experimental manipulation , these results support the assertion of Kester and Griggs that reductions in total number of flowers due to adverse orchard conditions are not likely to be compensated for by increased relative fruit set when adequate pollinizers and pollinators are present and can result in some measure of crop reduction. Such was the case in this study since it was conducted in an orchard in which the ‘Nonpareil’ trees were flanked by two pollinizer cultivars selected for bloom overlap with ‘Nonpareil’ and relatively high populations of bee pollinators were placed in the orchard each year to facilitate pollination. Had such factors not been present in the orchard during bloom, it is likely that relative fruit set would have varied even more among years and measured tree yields would have been more dependent on variations in relative fruit set.Refrigeration can lead to post harvest loss and waste , although it is the most effective strategy to maintain the quality and prolong the shelf-life of horticultural products. The rates of metabolic reactions increase 2–3-fold for every 10°C rise in temperature, and low-storage temperature delays deterioration by slowing down respiration and ethylene production, and by reducing pathogen growth and water loss. Commodities such as apples, blackberries, blueberries, cherries, and grapes benefit from refrigeration, however, in produce originating from tropical and subtropical regions, such as tomato, banana, pineapple, potato, and basil, refrigeration may lead to injury. Postharvest chilling injury is initiated when the tissues of cold-sensitive species are stored between 0 and 15°C, but becomes apparent after transfer to warmer conditions. Because the affected species are taxonomically diverse and the organs affected vary, for example, fruit, tuber, root, leaf, and stem, PCI symptoms can be variable . However, some common phenotypes include tissue browning or blackening, pitted surfaces, shriveling, negative changes in texture, carbohydrates and aroma volatiles, and fungal infection. PCI severity is determined by many factors with temperature and storage time being the most important. If low temperatures are mild and exposure istransient, many metabolic functions will resume after rewarming, and visible symptoms may not develop.