growing raspberries in pots – Nature Ecrogrow

It is evident that there is less year-to-year variation in the national mean of temperature than precipitation

By using a panel of county level data and including county and state by year fixed effects, we rely on across county variation in county-specific deviations in weather within states. This means that our estimates are identified from comparisons of counties that had positive weather shocks with ones that had negative weather shocks, within the same state. Put in another way, this approach non-parametrically adjusts for all factors that are common across counties within a state by year, such as crop price levels. If production in individual counties affects the overall price level, which would be the case if a few counties determine crop prices, or there are segmented local markets for agricultural outputs, then this identification strategy will not be able to hold prices constant. The assumption that our approach fully adjusts for price differences seems reasonable for most agricultural products for at least two reasons. First, production of the most important crops is spread out across the country and not concentrated in a small number of counties. For example, McLean County, Illinois and Whitman County, Washington are the largest producers of corn and wheat, respectively, but they only account for 0.58% and 1.39% of total production of these crops in the US. Second, our results are robust to adjusting for price changes in a number of different ways. In particular, the qualitative findings are similar whether we control for shocks with year or state by year fixed effects.6 Returning to equation , consider the second term, which is the change in profits due to the weather-induced change in quantities. We would like to obtain an estimate of this term based on long run variation in climate, since this is the essence of climate change. Instead, our approach exploits short run variation in weather. Since farmers have a more circumscribed set of available responses to weather shocks than to changes in climate, it seems reasonable to assume that Short Run > Long Run. 7 For example, farmers may be able to change a limited set of inputs in response to weather shocks. But in response to climate change,plastic pots for planting they can change their crop mix and even convert their land to non-agricultural uses .

Consequently, our method to measure the impact of climate change is likely to be downward biased relative to the preferred long run effect. In summary, the use of weather shocks to estimate the costs of climate change may provide an appealing alternative to the traditional production function and hedonic approaches. Its appeal is that it provides a means to control for time invariant confounders, while also allowing for farmers’ short run behavioral responses to climate change. Its weakness is that it is likely to produce downward biased estimates of the long run effect of climate change.Agricultural Production. The data on agricultural production come from the 1978, 1982, 1987, 1992, and 1997 Censuses of Agriculture. The Census has been conducted roughly every 5 years since 1925. The operators of all farms and ranches from which $1,000 or more of agricultural products are produced and sold, or normally would have been sold, during the census year, are required to respond to the census forms. For confidentiality reasons, counties are the finest geographic unit of observation in these data. In much of the subsequent regression analysis, county-level agricultural profits are the dependent variable. This is calculated as the sum of the Censuses’ “Net Cash Returns from Agricultural Sales for the Farm Unit” across all farms in a county. This variable is the difference between the market value of agricultural products sold and total production expenses. This variable was not collected in 1978 or 1982, so the 1987, 1992, and 1997 data are the basis for our analysis. The revenues component measures the gross market value before taxes of all agricultural products sold or removed from the farm, regardless of who received the payment. Importantly, it does not include income from participation in federal farm programs, labor earnings off the farm , or income from non-farm sources. Thus, it is a measure of the revenue produced with the land. Total production expenses are the measure of costs. It includes expenditures by landowners, contractors, and partners in the operation of the farm business.

Importantly, it covers all variable costs . It also includes measures of interest paid on debts and the amount spent on repair and maintenance of buildings, motor vehicles, and farm equipment used for farm business. The primary limitation of this measure of expenditures is that it does not account for the rental rate of the portion of the capital stock that is not secured by a loan so it is only a partial measure of farms’ cost of capital. Just as with the revenue variable, the measure of expenses is limited to those that are incurred in the operation of the farm so, for example, any expenses associated with contract work for other farms is excluded.Data on production expenses were not collected before 1987. The Census data also contain some other variables that are used for the subsequent analysis. In particular, there are variables for most of the sub-categories of expenditures . These variables are used to measure the extent of adaptation to annual changes in temperature and precipitation. The data also separately report the number of acres devoted to crops, pasture, and grazing. Finally, we utilize the variable on the value of land and buildings to replicate the hedonic approach. This variable is available in all five Censuses. Soil Quality Data. No study of agricultural land values would be complete without data on soil quality and we rely on the National Resource Inventory for our measures of these variables. The NRI is a massive survey of soil samples and land characteristics from roughly 800,000 sites that is conducted in Census years. We follow the convention in the literature and use the measures of susceptibility to floods, soil erosion , slope length, sand content, clay content, irrigation, and permeability as determinants of land prices and agricultural profits. We create county-level measures by taking weighted averages from the sites that are used for agriculture, where the weight is the amount of land the sample represents in the county. Since the composition of the land devoted to agriculture varies within counties across Censuses, we use these variables as covariates. Although these data provide a rich portrait of soil quality, we suspect that they are not comprehensive. It is this possibility of omitted measures of soil quality and other determinants of profits that motivate our approach.

Climate Data. The climate and weather data are derived from the Parameter-elevation Regressions on Independent Slopes Model .This model generates estimates of precipitation and temperature at 4 x 4 kilometers grid cells for the entire US. The data that are used to derive these estimates are from the more than 20,000 weather stations in the National Climatic Data Center’s Summary of the Month Cooperative Files. The PRISM model is used by NASA,drainage for plants in pots the Weather Channel, and almost all other professional weather services. It is regarded as one of the most reliable interpolation procedures for climatic data on a small scale. This model and data are used to develop month by year measures of precipitation and temperature for the agricultural land in each county for the 1970 – 1997 period. This was accomplished by overlaying a map of land uses on the PRISM predictions for each grid cell and then by taking the simple average across all agricultural land grid cells.To replicate the previous literature’s application of the hedonic approach, we calculated the climate normals as the simple average of each county’s annual monthly temperature and precipitation estimates between 1970 and two years before the relevant Census year. Furthermore, we follow the convention in the literature and include the January, April, July, and October estimates in our specifications so there is a single measure of weather from each season. Table 1 reports county-level summary statistics from the three data sources for 1978, 1982, 1987, 1992, and 1997. The sample is limited to the 2,860 counties in our primary sample.Over the period, the number of farms per county declined from approximately 765 to 625. The total number of acres devoted to farming declined by roughly 8%. At the same time, the acreage devoted to cropland was roughly constant implying that the decline was due to reduced land for livestock, dairy, and poultry farming. The mean average value of land and buildings per acre in the Census years ranged between $1,258 and $1,814 in this period, with the highest average occurring in 1978. The second panel details annual financial information about farms. We focus on 1987-97, since complete data is only available for these years. During this period the mean county-level sale of agricultural products increased from $60 to $67 million. The share of revenue from crop products increased from 43.5% to 50.2% in this period. Farm production expenses grew from $48 million to $51 million. Based on the “net cash returns from agricultural sales” variable, which is our measure of profits, the mean county profit from farming operations was $11.8 million, $11.5 million, and $14.6 million or $38, $38, and $50 per acre in 1987, 1992, and 1997, respectively. The third panel lists the means of the available measures of soil quality, which are key determinants of lands’ productivity in agriculture. These variables are essentially unchanged across years since soil and land types at a given site are generally time-invariant. The small time-series variation in these variables is due to changes in the composition of land that is used for farming.

Notably, the only measure of salinity is from 1982, so we use this measure for all years. The final panels report the mean of the 8 primary weather variables for each year across counties. The precipitation variables are measured in inches and the temperature variables are reported in Fahrenheit degrees. On average, July is the wettest month and October is the driest. The average precipitation in these months in the five census years is 3.9 inches and 2.0 inches, respectively.Table 2 explores the magnitude of the deviations between counties’ yearly weather realizations and their long run averages. We calculate the long run average variables as the simple average of all yearly county-level measurements from 1970 through two years before the examined year. Each row reports information on the deviation between the relevant year by month’s realization of temperature or precipitation and the corresponding long run average. The first column presents the yearly average deviation for the temperature and precipitation variables across the 2,860 counties in our balanced panel. The remaining columns report the proportion of counties with deviations at least as large as the one reported in the column heading. For example, consider the January 1987 row. The entries indicate that 73% of counties had a mean January 1987 temperature that was at least 1 degree above or below their long run average January temperature . Analogously in October 1997, precipitation was 10% above or below the long run average in 95% of all counties. Our baseline estimates of the effect of climate change follow the convention in the literature and assume a uniform five degree Fahrenheit increase in temperature and eight percent increase in precipitation associated with a doubling of atmospheric concentrations of greenhouse gases .It would be ideal if a meaningful fraction of the observations have deviations from long run averages as large as 5 degrees and 8% of mean precipitation. If this is the case, our predicted economic impacts will be identified from the data, rather than by extrapolation due to functional form assumptions. In both the temperature and precipitation panels, it is clear that deviations of the magnitudes predicted by the climate change models occur in the data. It is evident that for all four months there will be little difficulty identifying the 8% change in precipitation. However in the cases of temperature, deviations as large as +/- 5 degree occur less frequently, especially in July. Consequently, the effects of the predicted temperature changes in these months will be identified from a small number of observations and functional form assumptions will play a larger role than is ideal.

We see a similar effect for the number of hierarchical levels within a community

We believe that the positive association between population density and war found in previous studies is a result of omitted variable bias. That is, failure to include all relevant control variables will lead to biased coefficient estimates. Obviously, any study relying on bivariate methods fails to include the relevant controls. We show in the appendix how omitted variable bias could, in our particular case, lead to a wide range of values for the association between population density and war. Leaving out important variables that are both highly correlated with population density and positively associated with war—variables such as the use of metal or the existence of writing and record-keeping—causes our measure for population density to capture variation that belongs to these other variables, and gives the false result that population density is positively associated with war. The exact source of the negative relationship between population density and war is unclear. But we speculate that it may be due to the reluctance of others to attack high population density communities or to the reluctance of high population density communities to attack others. The former is plausible since high population density communities would have improved land and structures worth defending, and would have the potential to field large, well-equipped forces for effective defense. The latter because relatively complex high population density communities may find war a costly disruption, both to their economy and to the ambitions of their elites, who are likely to have non-war strategies for obtaining and keeping status. We had speculated that communities with little political autonomy would be less likely to go to war,plastic garden pots since any antagonism toward an external community would require the approval of the larger polity before actual war could develop. The estimated coefficients reveal a more complex pattern, as shown in Figure 4: both totally dependent and fully autonomous communities have a higher propensity to go to war than do communities that are semi-autonomous.

It seems likely that the semi-autonomous are constrained by the larger polity from freely engaging in war, but are also too loosely controlled for the larger polity to draw them into irrelevant wars. On the other hand, totally dependent communities can be forced into wars by the larger polity, while the fully autonomous can enter any war that they choose. Most warlike of all are communities which have equal status to other communities in a pluralistic society. Such equal status may be the consequence of an often-exercised willingness to fight other communities. The theoretical minimum for the number of levels is two ; v236 considers up to a maximum of four levels. As Figure 5 shows, communities with this maximum number of levels are much less likely to engage in external war than are other communities. Decisions to engage in external war may be more difficult when larger numbers of political actors must agree, as would be the case in larger, more complex, communities. As an example, one might consider the Mae Enga, of the New Guinea highlands, who deliberate the war decision in large meetings, where all fighters have the chance to speak . From the above, one can see that features of communities with a low propensity to engage in war include: high population densities ; relatively complex community-level political structures ; and ties to other communities that constrain the free exercise of war . These features reflect a more complex social and material order. Chronic war is the enemy of order, since its object is to destroy the crops, structures, institutions, and lives of a people. One would expect a community with a long history of peace to have evolved a complex social and material life, able to sustain high population densities. Thus, the features identified by our model may be a cause of low levels of war, as we hypothesize, but can also be a consequence. Our results provide some insight into causality, in the form of the endogeneity tests. Since these show that no independent variables are endogenous, our estimated model in Table 5 can therefore be interpreted as representing solely the causes of external war, not consequences.

Three of the 12 major habitat dummies survived to the final model . Relative to all other habitat types, societies found within temperate coniferous forests or boreal forests/taigas have lower incidences of external warfare. Conversely, societies who make their homes in temperate broad leaf and mixed forests experience higher incidences of warfare. These results confirm Nolan’s suggestion that there are features of biophysical environments that affect the frequency of war. Though the exact paths of that effect are not clear, they are independent of the confounding effects of subsistence and cultural transmission, which are controlled for in our model. The general thesis of the ecological-evolutionary theorists is that ecology, subsistence type, and population density are the dominant determinants of the frequency with which a society goes to war. Table 7 shows that ecology, subsistence type, and population density together account for only about 17 percent of the variation in the frequency of external war. If one broadens the set of variables to include technology facilitating war , the broader set accounts for about 27 percent of the variation in the dependent variable. Thus, only when quite broadly defined does the general thesis of the ecological-evolutionary theorists find strong support in our results. Otterbein has argued that sociopolitical variables have much more influence than economic or ecological variables in determining the frequency and nature of war. Variables reflecting political organization account for about 11 percent of the variation in the frequency of external war; variables reflecting the strategies by which elites gain status account for another 10 percent. Add to this the five percent accounted for by the degree to which the supernatural supports morality, and the resulting 26 percent of variation in frequency of external war accounted for by sociopolitical factors is about the same as the 27 percent accounted for by the broadly defined ecological evolutionary variables. Some believe that the frequency of war may simply be a function of who a society happens to have as neighbors: Keely suggests that hostile neighbors may be the most important determinant of whether a society is warlike, and Younger finds that more isolated societies are more peaceful.

We include two variables that provide some measure of the effect of neighbors, and together they account for about 12 percent of the variation in the frequency of external war. The first of these confirms Younger’s view that more isolated societies are more peaceful. The second— our network lag term—shows the effect of cultural transmission. The network lag term’s optimal composite weight matrix indicates that societies will tend to engage in war at much the same frequencies as their geographical neighbors and their co-religionists. Table 5 contains two pieces of evidence suggesting that a society will not be much influenced by the frequency with which their ancestors went to war: the near-zero value of the composite weight for linguistic phylogeny, and the high p-value for the LM test for spatial lag based on linguistic phylogeny. In other words, there is evidence here that vertical transmission does not account for the frequency of external war. We re-examine Patrick Nolan’s empirical work on the causes of war. We criticize his methods, which consist of bivariate or tri-variate tabular analyses, for sacrificing variation, for ignoring confounding variables, for failing to show the relative importance of the analyzed effects, for ignoring Galton’s problem, and for ignoring the problem of missing data. Our approach is to build a multivariate model,square pots which uses multiple imputation to handle the problem of missing data, and uses a network lag term to handle Galton’s problem. Our results reinforce Nolan’s conclusions on a few points, notably the positive association between metal technology and war. And while this relationship is important, it is hardly decisive—accounting for about 6.5 percent of the variation in the frequency of external war. When we evaluate the total importance of all factors related to ecology, subsistence, population density, and technology, we find that together they explain about 27 percent of the variation in the frequency of external war. This is comparable to the 26 percent explained by a broad set of sociopolitical factors. Thus our results suggest that those who argue for ecological-evolutionary theory, such as Nolan, are about as correct as those who argue that sociopolitical factors are the main determinants of war, such as Otterbein . This serves as an example of the superiority of multivariate methods: by including all of the most likely determinants of war, one can gain a sense of their relative importance. For the first of the two specific hypotheses advanced by Nolan—more productive subsistence leads to more frequent war—we find only qualified support. Taking the proportion of subsistence derived from agriculture as a measure of productivity, we find the relationship to be quadratic. As Nolan would predict, increases in agriculture’s importance leads to increases in the frequency of external war, but for non-agricultural societies only. For societies primarily relying on agriculture, we find a result opposite to that predicted by Nolan: increases in agriculture’s importance lower the frequency of external war.

Our results explicitly contradict Nolan’s second hypothesis: that higher population densities lead to higher frequency of war. We find a strictly negative relationship, in which high population densities discourage war. In the appendix we show that omitted variable bias is the probable reason that other studies failed to find a negative relationship. This highlights again the necessity of multivariate models in cross-cultural research—only by considering all important confounding factors can a model be free of omitted variable bias. Finally, we feel encouraged that our results support an optimistic view of peace among human societies. The propensity to engage in war is not vertically transmitted, is not a behavior that a society is locked into by the practices of its ancestors, but rather appears to be a product of current conditions. And many of the features of contemporary societies appear to be those which favor peace: high population densities; moderately restricted political autonomy; more complex political structures; widespread belief in moralizing gods; and the prevalence of capitalism as a means for elites to gain status. If peace is our goal, perhaps we are heading in the right direction.In alternate bearing theory, there are three competing hypothesis that attempt to explain how the fruit negatively influence vegetative growth. The competition hypothesis suggests that the demand between two sink tissues determines the flow of nutrients to each organ, the inhibitor hypothesis suggests that either the leaves or the fruits suppress flower development even when nutrient supplies are adequate, while the dominance hypothesis suggests that the fruits reverse the apical dominance mechanism, suppressing the apical meristem and subsequent vegetative growth. Although the competition hypothesis is favored by the growth trade-off observed in many alternate bearing species, there been few attempts to determine which of the mechanisms predominates in actual growing tissues. One way of doing this would be to observe the expression profile of actively growing meristems subjected to a heavy fruit load, as each of the hypothesis could be expected to produce a unique signature of up and down regulated genes. For example, a recent genetic study of alternate bearing apple trees was able to demonstrate several genes related to auxin and gibberellin hormone pathways were located in alternate bearing quantitative trait loci. The presence of auxin genes could be used to support the fruit dominance hypothesis, while gibberellin and floral induction genes might indicate the presence of an inhibitor pathway. An impressive set of microarray data from alternate bearing mandarin scions found that several glucan and trehalose sugar-related genes were activated during the ON year. The authors further argued that the FT paralogs CiFT1 and CiFT3 were involved in suppressing vegetative growth, but recommended more work to validate this idea. One aspect not captured by either study is the degree of fruit load, which varies continuously over the annual production cycle and may even produce a concentration gradient in the case of the inhibitor theory. However, even if reported in terms of the alternate bearing index or fruit biomass, the use of averaging and biological replicates would obscure the signal before dosage sensitive responses could be extracted from the data.