Tag: pots with drainage holes

The extract was analyzed by high-performance liquid chromatography

Following the short-term testing, the building managers identified several potential sources of indoor contaminants including composite wood paneling, painted gypsum board walls and high density plastic barrels used as hydroponic containers for the bio-filtration-based air cleaning technology. Both new and aged plywood wall paneling was present in the building and the paneling was coated with a clear polish. The goal of this study was to measure material specific emission factors for VOCs and carbonyls and characterize the potential influence of the polish coating on the wood panel material. All building materials that were tested for emissions were harvested from the PBC building, double wrapped in foil and shipped directly to Lawrence Berkeley National Laboratory for testing. A description of each of the material samples is provided in Table 1. All samples except for the hydroponic drum were cut to 0.023 m 2 . In the laboratory, the sides and backs of the material were sealed with aluminum tape and stainless steel backing plates, respectively, to leave only the front face of the material exposed for testing. Each sample was placed individually in 6-liter stainless steel conditioning chamber as illustrated in Figure 1. The conditioning chambers were closed with Teflon lined lids and held at approximately 22 ˚C and 50% relative humidity to precondition the materials prior to sampling. For new materials, samples are typically preconditioned to allow the emissions to drop to a more relevant value for estimating long-term emission rates. For materials that are allowed to age in the environment, the conditioning period is important to allow chemicals that have partitioned into the material from the environment, e.g.,drainage planter pot chemicals that are not indigenous to the material, to off gas so that the measured emission rates are relevant to the material being tested.

The emission testing generally followed California Specification 01350 and ASTM Standard Guide D-6007-02 using small emission chambers. The approach has been used for a wide range of materials measuring both VOCs and carbonyls as described previously and as summarized below. Four emission chambers installed in a controlled environment oven provide an isolated environment with constant temperature and humidity. The constant humidity was maintained by splitting the flow of dry carbon/HEPA filter air with a portion of the air bubbling through a water bath then re-mixed to achieve the desired humidity for air flowing through each chamber. The chambers, shown in Figure 2, are made of stainless steel and all interior surfaces are coated with Sulfinert® coating to minimize chemical interaction with chamber walls. The chambers are 10.75 L and are operated with an approximate ventilation rate of 1 liter per minute equivalent to 5.6 air changes per hour , or 2.6 m3 [air]/m2 [exposed surface area]/hour. The standard tests are operated at 25 °C and 50% RH. After being pre-conditioned, each test material was transferred to the test chamber and placed on Sulfinert® treated screens resting slightly below the midpoint of the chamber. Each material was allowed to equilibrate in the test chamber for at least 30 minutes after being transferred from the conditioning chamber before testing. Once equilibrated, the air samples were collected directly from the test chamber and analyzed for VOC and Aldehydes as described below. The materials were first tested after 24 hours of conditioning, and then again after at least seven days of conditioning. The first sampling period was used to get information on upper bound emission rates and allow for the identification of the mix of chemicals in the emission stream.

The second sampling period, after seven days conditioning, provides the emission factors that are more relevant to the long-term emission pattern. Additional measurements were collected for the new wood with new polish to further understand how the polish affects the emissions from the material. VOC samples were collected and analyzed following the U.S. Environmental Protection Agency Method TO-17. VOC air samples were collected directly from the chambers by drawing chamber air through multi-sorbent tubes with a primary bed of TenaxTA® sorbent backed with a section of Carbosieve®. A peristaltic pump was used to pull the air through the sample tubes at a rate of approximately 100 mL/min for 1 hour. The flow was measured using a DryCal gas flow meter and was recorded at the beginning and the end of the sampling period. Before subjected to chemical analysis, each sample was spiked with 120ng of gas-phase 1-Bromo-3 Fluoro-Benzene , which was used as the internal standard in the quantification method. Analytes were thermally desorbed from the sampling tubes using a thermodesorption auto-sampler , a thermo-desorption oven , and a cooled injection system . Desorption was performed in splitless mode where the desorbed analytes were refocused on the cooled injection system prior to injection. Desorption temperature for the TDS started at 30 ˚C with a 0.5 minute delay followed by a 60 ˚C ramp to 250 ˚C and a 4 minute hold time. The cooled injection system was fitted with a Tenaxpacked glass liner that was held at -10 ˚C throughout desorption and then heated within 0.2 minutes to 270 ˚C followed by a 3-minute hold time. Compounds were resolved on a GC equipped with a 30 meter HP- 1701 14% Cyanopropyl Phenyl Methyl capillary column with helium flow of 1.2 mL/min.

The initial temperature of the oven was -10 ˚C held for 0.5 minutes then ramped at 5 ˚C/min to 40 ˚C then 3 ˚C/min to 140 ˚C and finally at 10 ˚C/min to 250 ˚C and held for 10 minutes. The resolved analytes were quantified using electron impact mass spectrometry, , with mass to charge ratio limits of 44.0 m/z and 450.0 m/z. The MS was operated in full scan mode with a solvent delay of 3.00 minutes. Compounds were initially identified using NIST mass spectral search program for the NIST/EPA/NIH mass spectral library with identity confirmed and quantified using pure standards. When pure standards were not available, the analyte was reported in terms of toluene equivalence by comparing the instrument response for the total ion chromatogram of the chemical to a multi-point calibration of TIC response for toluene.The volatile carbonyls including formaldehyde, acetaldehyde and acetone are quantified using USEPA Method TO-15. As with the VOC samples, the air was drawn directly from the chambers during sampling. The sampling rate was maintained at less than 80% of the total air flow through the chamber to prevent back flow of unfiltered air into the chamber during testing. The sample air passed through silica gel cartridges coated with 2,4-dinitrophenyl-hydrazine, which quantitatively reacts with the carbonyl functional group effectively trapping the aldehydes and other low molecular weight carbonyl compounds. A peristaltic pump was used to pull the air through the cartridge at a rate of approximately at 800 mL/min for 1 hour. The flow was measured using a DryCal gas flow meter and recorded at the beginning and the end of the sampling period. Prior to analysis, sample cartridges were eluted with 2ml of high purity acetonitrile and the effluent was brought to a final volume of 2 ml.The HPLC was fitted with a C18 reverse phase column and run with 65:35 H2O: Acetonitrile mobile phase at 0.35 mL/minute and UV detection at 360 nm. Multi-point calibrations were prepared for the target analytes using commercially available hydrazone derivatives of formaldehyde,plant pot with drainage acetaldehyde and acetone.All materials were initially tested after only 24-hours of conditioning time. Prior to conditioning, the materials had been tightly wrapped in foil and packaged individually in resealable plastic bags during shipping so the initial emissions were expected to be elevated. The purpose of this in initial testing was to identify the chemicals in the emission stream. A total of forty chemicals were identified in the emission stream from the six materials tested. All chemicals are listed in Table 2 along with steady state concentrations measured after 24-hours of conditioning. All values are listed in Table 2 for comparision but values above the typical method limit of quantification of 0.5 µg/m3 are listed in bold text. The plastic material from the hydroponic drum was tested as received without sealing the back and sides so the exposed area was approximately double that of the other materials. The initial measurements found that except for hexadecane and tetradecane, most of the VOCs from the plastic material, including the aldehdyes, were near or below the minimum detection limit. Therfore, the plastic is not likely a source of indoor contaminants in the indoor environment of the PBC.

The drywall material produced a number of elevated VOCs with two that exceeded the linear range of the analytical method. Drywall is typically a low VOC material although fresh coatings such as paint or plaster can emitt VOCs during curing. The drywall samples tested in this study appear to be freshly painted because the edges were sealed with paint. This might explain the elevated propylene glycol and benzyl alcohol. The new wood paneling had very high levels of formaldehyde both with and without polish although the polished panel had the high test levels of formaldehyde overall. However, the unpolished new wood panel produced a wider variety and higher levels of VOC in the emissions. The old wood paneling produced much lower levels of formaldehyde and the levels of VOCs in general were similar both with new and old polish. After the initial tests were completed to identify the target chemicals in the emission stream, the materials were returned to the conditioning chambers for approximately six more days before measuring the emission factors. Concentrations for the plastic material from the hydroponic drum remained low in the second test so emission factors are not reported for the plastic hydroponic drum material. The standard emission factors determined for the wood paneling and drywall materials are reported in Table 3. The formaldehyde emissions from new wood with new polish were still significantly elevated after 7 days but we suspected that the combination of polish and storage may have increased the time needed for the emission factor to drop to a relativily constant level. To address this, we continued to condition the new wood with new polish for an additional week and re-tested. The additional time needed to condition the new wood with new polish may have been due to a higher capacity of the polish coating for accumulating formaldehyde during storage. This possibility was tested and is discussed further below. The standard emission factors for the materials from the PBC are summarized in Table 3. Several of the chemicals that were initially detected in the materials were no longer detectable in the emission stream after a week of conditioning and are therfore not listed in Table 3. The painted drywall continued to have extremely high levels of benzyl alcohol and propylene glycol as well as quantifiable levels of several other aldehydes , alcohols and esters that may be related to the coating material and/or sorbed into the drywall matrix from the environment. The wood paneling material presented a mix of VOCs depending on if the polish and/or wood were new or old as illustrated in Figure 3. Figure 3 lists the sum of all emission factors for VOC presented as stacked colums with the largest overall emission factors listed in decreasing order from bottom to top on the figure legend. Emission factors listed in Table 3 that are below the approximate limit of quantification of 1.65 µg/m2 /h are not included in Figure 3. Overall the drywall material had the highest sum of individual emission factors with the paneling material emitting 134, 129, 33 and 7 for the new wood no applied polish, old wood new polish, new wood new polish and old wood old polish, respectivily. Formaldehyde emissions for the old wood paneling with new and old polish, and the drywall were all similar ranging from 10 µg/m2 /h to 22 µg/m2 /h . For the new wood, the formaldehyde emissions were approximately an order of magnitude higher than the other materials for both the polished and unfinished surfaces. The emission results for formaldehyde are illustrated in Figure 4 showing that the polish coating does not seem to significantly change the measured emission factors when the age of the wood paneling is taken into consideration.

Animal intrusion into fresh produce fields causes significant agricultural losses each year

We present a novel approach that uses multiple linear regression to combine the CPU temperature from nearby SBCs and remote weather stations, to estimate the temperature at outdoor locations that do not have temperature sensors. We use sensor data to train and test multiple regres-sion models. We investigate the efficacy of using different smoothing techniques and we account for the computational load of SBCs at the time of measurement and data collection. We find that our approach enables a prediction error that is less than 1.5 degrees Fahrenheit, while past work results in errors of 1–14 degrees Fahrenheit for similar datasets. We integrate sensor synthesis into Hypatia and use it to facilitate automatic and scalable model selection, as well as visualization for different data sets and recommendations. Finally, we developed a new approach to distributed scheduling for analytics applications in IoT settings: sensor-edge-cloud deployments. Our scheduler takes advantage of remote resources when available, while fully utilizing local edge systems, as it optimizes for time to completion for applications and workloads. The scheduler uses remote resources only if doing so reduces the latency of providing actionable insights locally. The scheduler uses histories of both computation and communication time the applications, which it uses to construct a job placement schedule that minimizes application response latency . Hypatia then uses this schedule to automatically deploy workloads across edge systems and cloud computing systems. We empirically evaluate Hypatia using both clustering and regression services and show that it is able to achieve better end-to-end performance than using the edge or cloud alone. The result is the first end-to-end system that fully utilizes edge computing resources as it serves the needs of precision agriculture.

It does so by accounting for resource constraints at the edge, the lack of or intermittent connectivity to the public cloud,pots with drainage holes and the expense of transmitting the data to/from remote cloud systems. Moreover, the system is open-source and integrates a wide range of analytics, scoring methods, and visualization tools, which can be easily extended with new and emerging techniques. By doing so, we enable others to easily build upon, extend, reproduce, and compare it to our work in the future. Moving forward, we hope to encourage adoption of Hypatia by growers, farm consultants, and data analysts interested in taking advantage of the locality of edge systems to provide low latency analytics. Given the existing infrastructure, we plan to add new sensors, develop more synthesized, sensors, and to integrate additional analytics and scoring methods. Specifically, we plan to extend Hypatia with support for image classification and to use analytics accelerators at both the edge and in the cloud when available. Other future work includes investigating new data sources and machine learning algorithms that inform a more refined scheduling algorithm that can take advantage of even more granular resources. In addition, Hypatia error analysis can benefit from additional abstractions that account error propagation, which has the potential for making the results and recommendation more informative and trustworthy.Fragmentation of natural habitats during conversion of wild lands to agriculture and the subsequent increase in agrochemicals has resulted in a loss of biodiversity and a deterioration of ecosystem function, including natural pest control. Non-crop habitats harbor natural enemies to crop pests . Such habitats also harbor beneficial songbirds that consume insect pests , and provide perching sites for raptors that deter avian and rodent pests . Balancing the role of agricultural lands in providing habitat for biodiversity while simultaneously avoiding bird damage and reducing food safety risks is the primary goal behind the concept of co-management, which is recommended by the Food Safety Modernization Act .

Wild and domestic animals destroy crops by eating and trampling them, and can pose food safety risks due to the deposition of potentially contaminated feces on or near the crops . Birds are one of the most challenging animals to keep out of agricultural fields, and they may harbor food borne pathogens. For example, European starlings are a source of Salmonella enterica at concentrated animal feeding operations , posing a greater risk of pathogen transfer than other variables like cattle density, facility management operations, and environmental variables . They also may be a significant source of other Salmonella spp, Escherichia coli O157, and other shiga toxin–producing E. coli . During a study at a CAFO in southern Arizona, 103 birds were tested for food borne pathogens. Two tested positive for Salmonella, and five tested positive for non-O157 Shiga toxinproducing E. coli. Other studies have shown similar results as documented in a review by Langholz and Jay-Russell where they listed 23 studies on food borne pathogen prevalence in birds, including positive results for ducks, gulls, starlings, and pigeons. A more recent review listed food borne pathogens specifically transmitted by wild birds . All reviews discuss a 2008 outbreak of Campylobacter related to pea consumption because it was one of few outbreaks directly linking the pathogen to a wildlife source, in this case, sandhill cranes . This highlights the potential risks to food safety associated with migratory birds. Damage and food safety risks from wildlife activities remain significant economic problems despite the use of a variety of methods to control bird and rodent pests . Yield loss and economic impacts vary by crop and region, but can be a substantial burden on growers . Growers of fresh produce try countless methods to deter birds. These deterrents fall into nine general categories . This paper is not intended to be an exhaustive review of bird deterrents, but instead we present an overview of the ones most used in the field, as well as methods that utilize multiple techniques in an effort to develop integrated pest management for nuisance bird control.

The array of visual bird deterrents is expansive, and includes lights that are flashing or rotating, searchlights, mirrors and reflectors, reflective tape, flags, rags, streamers, lasers, dogs, humans, scarecrows, raptor models, corpses, balloons with eye spots, kites, kite hawks, mobile predator models, and water dyes or colorants . All of these methods work to some degree for a short period of time until habituation. Lasers that were used to disperse crows, for example, resulted in an initial dispersion, but crows reoccupied their roosts the same night that the lasers were used, and none of the roosts were abandoned . Kite balloons were shown to be effective in the short term, but birds quickly become habituated, reducing the effectiveness over time . Similarly, balloons with eye spots have been used in an attempt to reduce damage to vineyard grapes in New Zealand, but growers reported no economically significant effect . Generally, balloons, scarecrows, hawk kites, and reflective tape work best with sound cannons or netting, described below . Noise deterrents are generally effective, but much like visual deterrents, birds easily become habituated to them,drainage planter pot decreasing their efficacy over time. They have the added issue that growers who use them are subject to complaints of nuisance noise from neighbors . Propane sound cannons are the most commonly used noise deterrent, but they need to be repositioned weekly and set to go off randomly every 7-20 minutes during daylight hours for the greatest effect. Since sound cannons usually make a hissing noise before sounding off, they give birds a warning to leave the area, and then they return after the explosive noise. Some of the other common noise deterrents include bangers, screamers, squawkers, whistlers, scare cartridges, and noise bombs . Even human presence can be used as a noise deterrent if they rattle cans, crack whips, yell, honk horns, or shoot guns . Human activity can be very effective at keeping nuisance birds out of fields when fields are small enough to drive or walk around, but it can be expensive to maintain a human guard on duty. Instead, some growers use synthetic sounds that offer unambiguous messages that elicit inter specific responses, like distress calls . They prevent habituation by varying the rhythm and number of signals emitted . In a study of alarm calls from crimson rosellas in orchards,researchers found that these birds were effectively deterred in the short- to medium-term . However, distress calls offer another challenge since they may be an invitation to nearby predators indicating that their next meal is ready. Broadcast units are a less expensive, more technologically advanced noise deterrent that reproduce accurate and effective birds calls that significantly reduce damage in vineyards .

Another moderately effective noise deterrent is the sonic net, which overlaps with the frequency range of bird vocalizations, making communication among a flock ineffective. When used at an airfield, researchers demonstrated an 82% reduction in birds in the sonic net area, and it remained effective after four weeks of exposure . Fencing is an effective non-lethal, long-term method used as a standard technique to minimize wildlife intrusion into agricultural lands . While fencing cannot be used to deter birds, netting can be. While noise deterrents used against juvenile starlings in a cherry orchard were shown to be ineffective, research suggests that the netting-in of an orchard would be more effective . However, while netting is the most effective method, it is also has some drawbacks. It is one of the most expensive methods for deterring birds due to the massive areas of crops that need to be covered . It can also be easily damaged, and it can be a hazard to wildlife. Other exclusions that are used with birds are electric fencing, overhead wires, and anti- perching devices, such as spikes, some of which are also considered tactile deterrents and forms of habitat modification described below. The concept of habitat modification to deter nuisance birds includes a wide array of activities, from providing better quality forage or shelter in alternate locations through lure crops or sacrificial crops to simply removing roost structures, food, and shelter, forcing birds to go elsewhere. In many cases, deterring nuisance birds from one field causes them to negatively impact neighboring farms. For that reason, Ainsley and Kosoy propose collective action on the part of neighboring farmers in which communal feeding plots are constructed to protect the fields of all farmers in a single area, thereby evenly distributing crop losses and maintaining stable bird populations in the ecosystem . The USDA’s Wildlife Services attempted this method when they began to cost share eight hectare Wildlife Conservation Sunflower Plots with sunflower growers to lure migrating blackbirds away from commercial sunflower fields. The targeted blackbirds ended up removing 10 times more sunflower seeds from the WCSP than from commercial fields, making this strategy an important part of an integrated pest management plan for commercial sunflower growers . Monk parakeets tend to damage corn and sunflower fields that are closest to man-made structures and adjacent trees, areas with tree patches around the crop fields, and areas with high availability of pasture and weedy and fallow fields . The removal of these landscape features that attract birds, like areas with structures for perching, breeding, and shelter, can cause birds to move out of an area . A recent study indicated that hedgerows harbor higher biodiversity of rodents, but that biodiversity does not spill over into wildlife intrusion into fields . While rodents differ from birds, the concept of wildlife utilizing adjacent habitat without affecting agricultural crops or impacting food safety is similar. Physiological methods of bird control include such things as chemo-sterilants, contraceptives, and immune- contraceptive vaccines . These are rarely, if ever, used by growers in agricultural areas because they require extensive permitting and veterinary oversight, often times making their use unfeasible. Linz, Bucher, et. al. identified four limiting factors hindering the use of contraceptive methods and lethal control of birds in agriculture, including: 1) the high cost of implementation combined with challenges related to maintaining long-term control of birds, 2) determining the population level in an area that would be considered acceptable and therefore serves as a level of success, 3) ensuring that the treatment would be directed only at the birds actually causing crop damage, and 4) managing immigration of non-treated birds. Chemical bird deterrents, such as taste and behavioral repellants, are expensive, difficult to apply, not as effective in the field as they are in the lab, need to be licensed, and some overlap with lethal deterrents.

The estimated impact of warming remains robust across all possible combinations

A decline in some commodity markets and a shift in federal crop subsidy programs in the mid 1980s affected different growing regions in different ways. Under these circumstances it would not be surprising if the coefficients on the climate variables varied somewhat over time. In fact, however, they are very robust. Pair-wise Chow tests between the pooled model and the four individual census years in Table 3 reveal that the five climatic variables are not significantly different at the 10 percent level in any of the ten tests. Although we have excluded western counties because their agriculture is dependent on irrigation, what about irrigated areas east of the 100th meridian? To test whether these are affecting our results, we repeat the estimation excluding counties where more than 5% of farmland area is irrigated, and where more than 15% of the harvested cropland is irrigated.We also examine further the influence of population, excluding counties with a population density above 400 people per square mile or a population total above 200,000. The exclusion of the three sets of counties leaves the coefficient estimates virtually unchanged, and the lowest of the three p-values for the test of whether the five climate variables have the same coefficients is 0.85. It is not surprising that excluding irrigated counties east of the 100th meridian has little effect on our regression results,30l plant pot since very few are highly irrigated, and all receive a substantial amount of natural rainfall. Under these circumstances, irrigation is a much smaller supplement to local precipitation, small enough to have little effect on regression results.

By contrast, the p-value for the test of whether the five climate coefficients are the same in counties west of the 100th meridian is 10−11. Including western counties that depend crucially on large-scale irrigation significantly alters the equation. To test whether the time period over which the climate variables are calculated makes any difference, we replicate the analysis using as alternatives to the 30-year histories on which the estimates reported in Table 3 are based, 10- and 50-year averages. Neither of the alternatives yields climate coefficients significantly different from the pooled regression results based on the 30-year histories. These tests suggest that our model is stable for various census years, data subsets, and climate histories. Nevertheless, one might wonder whether there could be problems without liers or an incorrect parametrization. We briefly address these concerns. In a test of the robustness of our results to outliers, the analysis is replicated using median regression, where the sum of absolute errors is minimized both in the first-stage derivation of the parameter of spatial correlation and in the second stage estimation of the coefficients. Again, the climatic variables remain robust and are not significantly different. To test the influence of our covariates on the results we follow the idea of Leamer’s extreme bound analysis and take permutations of our model by including or excluding each of 14 variables for a total of 16,384 regressions.No sign switches are observed in any of the five climatic variables, again suggesting that our results are very stable. Further, the peak-level of degree days is limited to a relatively narrow range. We check sensitivity to the assumed length of the growing season by allowing the season to begin in either March, April, or May and end in either August, September, or October.Finally, in order to examine whether the quadratic specification for degree days in our model is unduly restrictive, we estimate a penalized regression spline for degree days 8◦C −32◦C and find that the quadratic approximation is consistent with the data.

Before turning to the determination of the potential impacts of global warming on the agricultural sector of the U.S. economy as measured by predicted changes in farmland values we briefly consider whether farmers’ expectations have changed over the period covered by our study, and whether this may affect our estimates. In the previous section we regressed farmland values on past climate averages, even though farmland values are determined using forward-looking expectations about future climate. The weather in the U.S. over the past century was viewed as a random drawing from what until recently was thought to be a stationary climate distribution. Our own data are consistent with this: the correlation coefficients between the 30-year average in 1968-1997 and the two previous 30-year averages of the century, i.e., for 1908-1937, and 1938-1967, are 0.998 and 0.996 for degree days , 0.91 and 0.88 for degree days , and 0.93 and 0.93 for precipitation variable. Accordingly, when we use the error terms from our regression and regress them on past values of the three climate averages, none of the coefficients is statistically significant. The same result holds if we move to the shorter 10-year climate averages. This suggests that past climate variables are not a predictor of farmland values once we condition on current climate. As pointed out above, consecutive census years give comparable estimates of the climate coefficients in our hedonic equation and none of them are significantly different.18 Similarly, we check whether the aggregate climate impacts for the four emission scenarios in Table 5 change if we use the 1982 census instead of the pooled model. Even though the standard deviations are fairly narrow, t-tests reveal that none of the eight mean impact estimates are significantly different . We conclude that our results are not affected by any significant change in expectations over the study period.

In the calculations which follow we use the regression coefficients from the semi-log model, which we have shown to be both plausible and robust, along with predictions from a general circulation model to evaluate the impacts of climate change. The climate model we use for this analysis is the most recent version of the UK Met Office Hadley Model, HadCM3, recently prepared for use in the next IPCC Assessment Report. Specifically, we use the model’s predicted changes in minimum and maximum average monthly temperatures and precipitation for four standard emissions scenarios identified in the IPCC Special Report on Emissions Scenarios . The chosen scenarios span the range from the slowest increase in greenhouse gas concentrations , which would imply a little less than a doubling of the pre-industrial level by the end of the century, to the fastest , associated with between a tripling and a quadrupling, and include two intermediate scenarios . We use the 1960-1989 climate history as the baseline and calculate average predicted degree days and precipitation for the years 2020-2049 and 2070-2099. The former captures impacts in the near to medium term, while the latter predicts impacts over the longer term, all the way to the end of the century, the usual benchmark in recent analyses of the nature and impacts of climate change.Predicted changes in the climatic variables are given in Table 4. Impacts of these changes on farmland values are presented in Table 5 for both the 2020- 2049 and 2070-2099 climate averages under all four emissions scenarios. Not surprisingly,pots with drainage holes results for the near-term 2020-2049 climate averages are similar under all four scenarios. The relative impact ranges from a 10% to a 25% decline in farmland value, which translates into an area-weighted aggregate impact of -$3.1 billion to -$7.2 billion on an annual basis.Although the aggregate impact is perhaps not dramatic, there are large regional differences. Northern counties, that currently experience cold climates, benefit by as much as 34% from the predicted warming, while others in the hotter southern states face declines in farmland value as high as 69%. Similarly, average relative impacts are comparable across scenarios for the individual variables degree days and degree days , but again there are large regional differences. The effect of an increase in the latter variable is always negative because increases in temperature above 34◦C are always harmful, while the effect of the former variable depends on whether a county currently experiences growing conditions above or below the optimal number of degree days in the 8 − 32◦C range.The impact estimates for the longer-term 2070-2099 climate average become much more uncertain as the range of predicted greenhouse gas emission scenarios widens. Predicted emissions over the course of the century are largely driven by assumptions about technological change, population growth, and economic development, and compounding over time leads to increasingly divergent predictions. The distribution of impacts now ranges from a average decline of 27% under the B1 scenario to 69% under the A1FI scenario. At the same time, the sharp regional differences observed already in the near to medium term persist, and indeed increase: northern counties generally benefit, while southern counties generally suffer.

Anexception is found in Appalachia, characterized by a colder climate than other counties at a similar latitude. Regional differences widen as counties with a very cold climate can benefit from continued warming: the maximum positive relative impact now ranges from 29% to 52%. However, the total number of counties with significant gains decreases in most scenarios. For the 2020-2049 time span, 446, 126, 269, and 167 counties, respectively, show statistically significant gains at the 95% level for the scenarios given in Table 4. These numbers change to 244, 202, 4, and 26 for the 2070-2099 time span. By the same token, the number of counties with statistically significant loses increases from 1291, 1748, 1762, and 1873 for the 2020-2049 time span to 1805, 1803, 2234, and 2236 for the 2070-2099 time span. The regional distribution of impacts is shown in Figure 1 for counties with significant gains and loses under the intermediate B2-scenario. The predicted changes are also closer to those in another general circulation model, the DOE/NCAR Parallel Climate Model , which we use as an alternative because it is considered a low-sensitivity model, as opposed to the mid-sensitivity HadCM3; for a given CO2 scenario the temperature changes are lower under the PCM than under the Hadley model. We replicated the impact analysis using the PCM climate forecasts in the appendix available on request. Not surprisingly, the predicted area-weighted aggregate damages are lower. However, the regional pattern remains the same: out of the 73% of counties that have statistically significant declines in farmland values under all four Hadley scenarios by the end of the century, 73% still have significant losses under the PCM A1FI model and 0.7% switch to having significant gains. The magnitude of temperature changes simply shifts the border between gainers and losers. Some of the predicted potential losses, in particular for the high emissions scenario in the later period toward the end of the century, are quite large. However, average temperature increases of 7◦Cwould lead to the desertification of large parts of the South. A way of interpreting the results that places them in the context of other studies and also highlights the role for policy, is that if emissions are fairly stringently controlled over the course of the coming century, as in B1, such that atmospheric concentrations of greenhouse gases remain alittle below double the pre-industrial level, predicted losses to agriculture, though not trivial, are within the range of the historically wide cyclical variations in this sector. If on the other hand concentrations climb beyond three times the pre-industrial level, as in A1F1, losses go well beyond this range. This suggests a meaningful role for policy involving energy sources and technologies, since choices among feasible options can make a major difference. A complete impact analysis of climate change on U.S. agriculture would require a separate analysis for counties west of the 100th meridian. Based on the information presently available, we do not believe the impact will be favorable. A recently published study down scales the HadCM3 and PCM predictions to California and finds that, by the end of the century, average winter temperatures in California are projected to rise statewide by about 2.2 ◦C under the B1 scenario and 3.0−4.0 ◦C under the A1FI scenario . Summer temperatures are projected to rise even more sharply, by about 2.2 − 4.6 ◦C under the B1 scenario and 4.1 − 8.3 ◦C under the A1FI scenario. Winter precipitation, which accounts for most of California’s water supply either stays about the same or decreases by 15-30% before the end of the century.