Gardening Map Of Warming U.S. Has Plant Zones Moving North
The new version of the map includes 13 zones, with the addition for the first time of zones 12 (50-60 degrees F) and 13 (60-70 degrees F). U.S. Department of Agriculture hide caption
toggle caption U.S. Department of Agriculture
The new version of the map includes 13 zones, with the addition for the first time of zones 12 (50-60 degrees F) and 13 (60-70 degrees F).
U.S. Department of Agriculture
It’s official: Gardeners and farmers can count on warmer weather. If that’s you, it might be a good time to rethink those flower and vegetable beds for this year’s growing season.
That’s the word from the U.S. Department of Agriculture, which released a new version of its “Plant Hardiness Zone Map” this week, the first update since 1990. The color-coded zones on this map of the United States are widely used as a guide for what perennial flowers will survive in a particular area, or when to plant your vegetables.
Here’s how it works: The higher the zone number, the warmer your average low temperature during wintertime.
Now the zones have shifted northward. The new map shows that in much of the country, winters aren’t as cold as they used to be, and spring planting comes earlier.
So if you’re thinking of planting a White Dogwood tree in your front yard, for instance, you may have read that it will thrive in zones 5-8, which covers most of the southern half of the country.
But if you live in northern Iowa — good news — you can now plant that tree! You are now in zone 5, instead of 4.
Gardeners in Manhattan, Kansas, may also rejoice over new options now that the town has moved from zone 5 to zone 6. The new version of the map also includes two new zones at the warmed end of the spectrum: zone 12 (50-60 degrees Fahrenheit) and 13 (60-70 degrees Fahrenheit).
The nationwide shift in the planting season provoked lots of questions about just how much to attribute to climate change. USDA officials, while introducing their new map to reporters, insisted that they were making no claims about global warming.
The last iteration of the Plant Hardiness Zone Map, from 1990. In the 2012 map, many zone boundaries have shifted significantly. U.S. Department of Agriculture hide caption
toggle caption U.S. Department of Agriculture
The last iteration of the Plant Hardiness Zone Map, from 1990. In the 2012 map, many zone boundaries have shifted significantly.
U.S. Department of Agriculture
Some of the shifting zone boundaries, they said, were the result of more sophisticated mapping. For the first time, the new map takes into account the effects of elevation, large lakes, and whether a place is located in a valley or on top of a ridge. They admitted, however, that most of the changes were due to using temperature data from recent years, which have been relatively toasty.
Unlike previous hardiness maps, the USDA won’t sell poster-sized versions of this one. But there’s an interactive version, available on the web, where you can explore the map in exquisite detail.
Global plant hardiness zones for phytosanitary risk analysis
Zonas globais de resistência às plantas para análise de risco fitossanitário
Roger D. MagareyI, II, ; Daniel M. BorchertII; Jay W. SchlegelIII
INorth Carolina State University/Center for Integrated Pest Management, 1730 Varsity Drive, Suite 300 – 27606 – Raleigh, NC
IIUSDA/Center for Plant Health Science and Technology, Plant Epidemiology and Risk Analysis Laboratory, Plant Protection and Quarantine, Animal Plant Health Inspection Service – 27606 – Raleigh, NC
IIIZedX Inc., 369 Rolling Ridge Drive – 16823 – Bellefonte, PA
Plant hardiness zones are widely used for selection of perennial plants and for phytosanitary risk analysis. The most widely used definition of plant hardiness zones (United States Department of Agriculture National Arboretum) is based on average annual extreme minimum temperature. There is a need for a global plant hardiness map to standardize the comparison of zones for phytosanitary risk analysis. Two data sets were used to create global hardiness zones: i) Climate Research Unit (CRU) 19732002 monthly data set; and ii) the Daily Global Historical Climatology Network (GHCN). The CRU monthly data set was downscaled to five-minute resolution and a cubic spline was used to convert the monthly values into daily values. The GHCN data were subjected to a number of quality control measures prior to analysis. Least squares regression relationships were developed using GHCN and derived lowest average daily minimum temperature data and average annual extreme minimum temperatures. Error estimate statistics were calculated from the numerical difference between the estimated value for the grid and the station. The mean absolute error for annual extreme minimum temperature was 1.9ºC (3.5ºF) and 2/3 of the stations were classified into the correct zone.
Keywords: climate, plant diseases, minimum temperature
Zonas de resistência às plantas, definidas pelo ” United States Department of Agriculture National Arboretum” com base na média anual das temperaturas mínimas extremas, são amplamente utilizadas para a seleção de plantas perenes e para a análise de risco fitossanitário. Há necessidade de um mapa global para padronizar a comparação de zonas nas análises de risco fitossanitário. Dois bancos de dados climatológicos foram utilizados para criar tais zonas globais de resistência às plantas: i) conjunto de dados mensais de 19732002 da ” Climate Research Unit (CRU)” ; e ii) dados climatológicos diários da ” Daily Global Historical Climatology Network (GHCN)” . Os dados mensais da CRU foram ajustados a uma escala reduzida de resolução de cinco minutos, e um ajuste cúbico foi empregado para converter os dados mensais para diários. Os dados da RDGH foram submetidos a várias medidas de controle de qualidade antes de serem empregados nas análises. Relações de regressão pelo método dos mínimos quadrados foram desenvolvidas usando dados da RDGH, resultando nos mais baixos valores médios diários de temperatura mínima e média anual das temperaturas mínimas extremas. Os erros estatísticos estimados foram calculados a partir da diferença numérica entre os valores estimados para a malha e os observados nas estações climatológicas. O erro médio absoluto para a temperatura mínima extrema anual foi 1,9ºC (3,5ºF), o que possibilitou a classificação de 2/3 das estações dentro das zonas corretas.
Palavras-chave: clima, doenças de plantas, temperatura mínima
The growth and survival of most terrestrial plants is influenced by extreme low temperature (Woodward & Williams, 1987). For example, the distribution of evergreen broadleaf vegetation has been shown to be correlated with 15ºC (5ºF) (Woodward & Williams, 1987). A commonly used indicator of the influence of climate on plant growth and survival is the hardiness zone. The United States Department of Agriculture National Arboretum (USDA-NA) hardiness zones are based on the average annual extreme minimum temperature (Cathey, 1990) but other definitions of hardiness zones also exist. For example, the Canadian hardiness zones take into account the influence of seven variables including frost free days, average minimum temperature and precipitation of the coldest month (McKenney et al., 2001).
Hardiness zones were developed primarily for making planting recommendations for perennial plants (Cappiello & Littlefield, 1994; Cathey, 1990; McKenney et al., 2006), but have also been used for phytosanitary risk analysis as an indicator of establishment potential (Evans et al., 2007; Jefferson et al., 2004; Venette & Gould, 2006). As an indication of climatic suitability, the USDA Animal Plant Health Inspection Service (APHIS) uses plant hardiness zones for its commodity risk analyses (USDA, 2003). For example, under this 2003 version of the guidelines, a pest receives a high ranking for climate suitability if it occurs in four or more plant hardiness zones, but a low ranking if it occurs in a single plant hardiness zone.
One of the limitations of the current risk analysis procedure is the lack of a single global plant hardiness map. Currently risk analysts must consult multiple sources (plant, 2007), which may use different and often undefined methodologies and time periods, making comparisons with US hardiness zones difficult. In addition, climate change has created a need to update many of the older maps. The objective of this study was to create an updated, uniform global plant hardiness map which could be used as a phytosanitary risk analysis tool.
MATERIAL AND METHODS
Two sources of weather data were used to generate the plant hardiness zone maps, the Climate Research Unit (CRU) monthly data and the Daily Global Historical Climatology Network (GHCN) station data. The global CRU monthly average data set, which covers a period of more than 100 years, is a well known data set for climate change studies and is available from the Intergovernmental Panel of Climate Change (IPCC) website. The CRU data set used for this project consists of gridded monthly average minimum temperature data for the 19732002 period at a resolution of 0.5 degree latitude × 0.5 degree (approximately 55 km depending on latitude) longitude. A major advantage of using the gridded CRU data set is that it contains values for all land areas of the world except Antarctica.
The CRU gridded monthly data were used to compute thirty-year monthly and daily averages. A proprietary version of an optimal interpolation technique (OI) (3-D interpolation) (Lorenc, 1981) was used to downscale the resolution from 0.5 degrees to 5-minutes (9.2 km). This 3-D interpolation technique takes into account the point elevation of the 9.2 km pixel. A cubic spline was employed to convert the monthly minimum temperature averages to daily values. These average minimum daily values were used to determine the lowest daily value that occurs during the year.
The GHCN station data contains daily values of maximum and minimum temperature and precipitation for more than 43,000 locations worldwide (Peterson & Vose, 1997). The GHCN station data were used for extracting extreme minimum temperature information, but the density of this data were variable both temporally and spatially, especially outside North America and Europe. The variable nature of the GHCN station data makes the global scale assessments of plant hardiness difficult. To ensure stability in determining Plant Hardiness Zones (PHZ) on a global scale regardless of the variability of the GHCN data, relationships between daily CRU data and GHCN data were determined using one-dimensional regressions.
Before the GHCN data could be used in PHZ assessment, some quality control checks were performed on the data. These checks were done in addition to quality control checks performed by the National Climatic Data Center (Gleason, 2002), which produces the GHCN data set. The first check was to determine the degree of completeness for each station. Only those stations that reported at least 67% of the time (or at least 20 years during the 19782007 period were used in this study. There were over 6,500 stations worldwide that met this criterion (Figure 1). Highest station density is seen in North America while parts of Africa, India, Brazil and the Middle East have few, if any stations (Figure 1).
Each station was subjected to plausibility tests to determine if average annual extreme minimum temperatures for the thirty-year period and annual frequencies fell within plausible limits. Minimum temperatures were rejected if they fell outside the bounds of 81.3ºC (130ºF) or 68.8ºC (110ºF). If the minimum temperature was equal to or greater than the maximum temperature for the day, it was rejected. To test if annual frequencies were in plausible limits, the characteristics of each station were compared to ten or more neighboring stations. To pass this quality control check, each candidate station had to have values within 3.0 standard deviations if the stations were located within 5 degrees of each other. If the ten stations were more than 5 degrees apart, the values were allowed to vary up to 3.5 standard deviations before rejection.
The GHCN data were used to create a set of six least squares regression relationships between the lowest average daily minimum temperature data and the average annual extreme minimum temperature. The following procedure was used to create the six regressions. First, global temperature regimes were classified into six zones (Table 1). The GHCN stations in each of those temperature ranges were used to calculate the regression equation for that zone. Next, the CRU climatology was used to determine the lowest average daily minimum temperature for each pixel. The appropriate regression equation based on the CRU derived lowest average daily minimum temperature was applied to estimate the average annual extreme minimum temperature. Finally, the estimated average annual extreme minimum temperature (Figure 2) was used to determine the hardiness classification for all land areas, except Antarctica.
Plant hardiness zones were estimated from annual average extreme minimum temperature using the USDA-NA zone definitions (Cathey, 1990). The USDA-NA definition defines 11 zones from 45.6ºC (50ºF) to above 4.4ºC (40ºF) in 6.25ºC (10ºF) increments. Many of the USDA-NA zones are subdivided into a and b based on 3.125ºC (5ºF) increments. Hardiness zones were using thirty-years (19782007) of GHCN data and by using USDA-NA methodology that was modified by adding zones 12 and 13. Zones 12 and 13 represent an extension of the USDA-NA zones in 10ºF increments above 10ºC (50ºF) and 15.6ºC (60ºF), respectively.
Error estimates were calculated by comparing the estimated average annual extreme minimum temperatures for each station with the observed GHCN station value. Regression equations for average annual extreme minimum temperatures were calculated without inclusion of the target station values. Using those equations, average annual extreme minimum temperatures were calculated for the target stations. Statistics were then derived by comparing the estimated and observed GHCN station values. The calculated statistics included the mean, bias (mean error), mean absolute error (MAE), correlation coefficient (r-square) and the MAE for the 95th percentile (Wilks, 1995). In addition, the numbers and percentages of GHCN stations for which the observed plant hardiness zone was lower, the same or higher than the estimated grid value was calculated without inclusion of the target station values.
The CRU and GHCN data sets provide researchers to ability to create PHZ maps with a more consistent methodology (Figure 3). Moreover, the PHZ maps can be updated annually with little effort. Visual comparisons with the current PHZ map with other PHZ maps across the globe using different data sets and temporal periods show similar patterns. One weakness of the methodology used to create the PHZ map is found over small oceanic islands. PHZ zones may not be correctly classified in some of these areas because data are too limited for the modeling methods used in this study.
The method of using regression relationships to estimate average extreme annual temperature provided good results. The grid values of estimated temperature were compared with the observed station temperature. The mean absolute error between the estimated and observed station values was 1.94ºC (3.5ºF) for the thirty-year (19782007) period (Table 2). In addition, the PHZ classification based on the grid value was compared to that based on the station data. Approximately two-thirds of stations are classified into the correct PHZ (Table 3).
The global plant hardiness zone map will allow a phytosanitary analyst to easily compare plant hardiness zones without having to consult multiple maps which may have been created with different methodologies and time periods. Since the thirty-year map was created from the period from 1978 to 2007 data, it is more recent and uniform in methodology than some of the maps that have been used previously for risk analysis. For example, some plant hardiness maps show only a small area of PHZ zone 11 in southern Florida while the recent map indicated a larger portion of the state in that zone. This is an important change since Florida is susceptible to frequent exotic pest introductions (Frank & McCoy, 1995), has the busy port and airport of Miami, and is a major producer of plants, fruits and vegetables.
Because of increased global mobility of pests and influence of climate change on species distribution, it is important for PHZ maps to be updated frequently on a global scale. The influence of climate change on species distribution has been well documented in recent years (Parmesan & Yohe, 2003; Walther et al., 2002). Parmesan & Yohe (2003) used meta-analysis techniques for 1,700 species to document significant range shifts averaging 6.1 km per decade towards the poles and significant mean advancement of spring events by 2.3 days per decade. When climate change is coupled with human interactions there is the potential for more dramatic changes in distribution, for example the spread of thermophilous plants from gardens into surrounding countryside (Walther et al., 2002).
The plant hardiness zones used in this study were based on average annual extreme minimum temperature. In reality, plant survival is likely to be influenced by many factors including snow cover and winter rainfall (DeGaetano & Shulman, 1990; Oullett & Sherk, 1967; McKenney et al., 2001). Plant Hardiness zones are likely to be only a broad surrogate for potential plant distributions (McKenney et al., 2007). Likewise, the establishment of exotic pests will also be influenced by many factors other than plant hardiness. For example CLIMEX, a decision support system for pest risk analysis includes functions that account for growth and stress due to hot, cold, wet and dry (Sutherst et al., 1999). Likewise, the USDA APHIS NAPPFAST system uses infection, day degree and empirical models for risk mapping of exotic pests (Magarey et al., 2007). Prediction systems generally require either biological parameters or detailed distribution data. In contrast, hardiness zones provide a quick and easy method for an analyst to estimate and describe potential distribution. Many commodity pest risk assessments may analyze the climate potential for 20 or more pests including many that have limited or no biological/distribution data. For example, distribution data may be limited to simple literature reports at the scale of a country or less commonly for a secondary political unit. Biological data such as developmental requirements or cold tolerances required for deductive models are also not widely available for pests from developing countries (Nietshcke et al., 2007). Plant hardiness zone maps provide a simple alternative to intensive modeling efforts when detailed distributional and biological data are lacking. Future work may focus on developing hardiness maps incorporating degree days and available moisture.
The plant hardiness zone maps will be made available on the Internet at http://www.nappfast.org. The images will be available as geotiffs and can be imported into a geographic information system.
We would like to thank Dr. Matthew Royer and the Cooperative Agricultural Pest Survey Program for project funding. We thank Dr. Robert Griffin of USDA APHIS CPHST PERAL for reviewing the manuscript. We also appreciate the helpful suggestions of two anonymous reviewers.
GLEASON, B.E. Data documentation for data set 9101. Asheville: National Climatic Data Center, 2002. p.1-26.
UNITED STATES DEPARTMENT OF AGRICULTURE – USDA. Guidelines for pathway-initiated pest risk assessments: version 5.1. Riverdale: USDA/APHIS, 2003. 41 p.
WILKS, D.S. Statistical methods in the atmospheric sciences. San Diego: Academic Press, 1995. 467 p.
Received November 01, 2007
Accepted August 18, 2008
Corresponding author <[email protected]>
World Plant Hardiness Zones Map
In a previous article I discussed the U.S. Hardiness Zones (you can read that article here). I use these zones in each of my plant articles. I recently received an email from a reader telling me that the Zones I discuss mean nothing to them as they are in Greece. While I use zones that are tailored to the U.S., there are similar zones outlined for all parts of the world. Many of these zones use the same temperature scales, and others do not, but it is not too hard to go from one to another if you have both map keys available.
In brief, a Hardiness Zone Map divides an area into Zones (typically 1 through 10 based on minimum temperatures, with 1 being the coldest and 10 being the warmest). A plant is placed into one of these Hardiness Zones based upon the lowest temperature it can withstand. As I have stated previously, the benefit of Hardiness Zones is that it provides a starting point for planning which plants can winter-over where you live. However, there are a few drawbacks to the Hardiness Zone Map. It does not consider day length (which changes considerably the further from the equator you go), snow cover (which moderates soil freezing and insulates roots), humidity, frost, or soil moisture. Probably the biggest drawback is that it does not consider how warm your summer will be. The classic example is comparing the Shetland Islands north of Scotland and southern Alabama. Both are listed as bewteen Hardiness Zone 8-9. However, the Shetland Islands are sub-artic and southern Alabama is sub-tropical. There are almost no plants that can grow in both places.
I hope this helps anyone struggling to determine in which Plant Hardiness Zone you reside. Here are the links to pages that have the best Plant Hardiness Zones maps I can find for locations around the world:
- Africa – Not a very detailed map at all, but the only one I could find for the entire continent. There is a much more detaile map of Southern Africa here.
- Australia – Not extremely detailed, but still very useful. Also, it has a comparison to the U.S. which can be quite helpful.
- Canada – Very detailed map, but may be a little hard to use as the zones blend into one another… but I guess that is how things actually are in real life.
- China – Pretty good map. There may be better ones out there, but they are not available (or searchable) in English, and my Mandarin is not good at all. 🙂
- Europe – Pretty good map. There is a list to specific countries that link to a larger (close-up) map.
- India – No specific map found. The only map that I could find is at the top of this post.
- Japan – Fantastic interactive map.
- Russia – Map is fair, but the information is good. Includes areas/countries that belonged to the former USSR.
- South America – Not super detailed and maybe a little hard to see the zone deliniations, but still reliable.
- Southeast Asia – No specific map found. The only map that I could find is at the top of this post.
- United States of America – link to my previous article.
The U.S. Department of Agriculture has released a new Plant Hardiness Zone Map (PHZM). The map is the first updated in over twenty years and incorporates greater accuracy and detail since the last map from 1990. The new map was developed by the USDA’s Agricultural Research Service (ARS) and Oregon State University’s (OSU) PRISM Climate Group and is available in various digital image formats and as an interactive online mapping application. The online mapping was built using Esri technology and has a transparency slider which allows users to see the relationship between the underlying topography and localized differences in plant hardiness zones. The USDA Plant Hardiness GIS data is available in shapefile and raster grid formats from Climate Source for a fee.
The map is divided into 13 zones which represent a spread of 10 degrees Fahrenheit for each zone. Each zone is divided into an A and B zone bands of 5 degrees fahrenheit each. The zones represent the average annual minimum winter temperatures. The new maps uses temperature measurements over a 30-year period 1976-2005. The previous 1990 map only averaged temperature over a 13-year period of 1974-1986 and some temperature shifts have been observed:
Compared to the 1990 version, zone boundaries in this edition of the map have shifted in many areas. The new map is generally one 5-degree Fahrenheit half-zone warmer than the previous map throughout much of the United States.
While it could be interpreted that the changes are due to climate change, the announcement on the map’s publication notes that some of the changes are the result of more detailed and accurate information:
Some of the changes in the zones, however, are a result of new, more sophisticated methods for mapping zones between weather stations. These include algorithms that considered for the first time such factors as changes in elevation, nearness to large bodies of water, and position on the terrain, such as valley bottoms and ridge tops. Also, the new map used temperature data from many more stations than did the 1990 map. These advances greatly improved the accuracy and detail of the map, especially in mountainous regions of the western United States. In some cases, advances resulted in changes to cooler, rather than warmer, zones.
New Plant Hardiness Zone Map