177
Abstract The main conclusions from recent global and regional assessments are reviewed, with an emphasis on China, India, Africa, and the United States. Most stud-ies have provided primarily “best-guess” point estimates, often supplemented with a few sensitivity analyses, but without a comprehensive measure of uncertainties. Although some useful lessons have been learned, most existing estimates of food secu-rity risks leave much to be desired. We explore these estimates, some of their strengths and weaknesses, and some additional opportunities for measuring uncertainties.10.1 I ntroduction
This book has focused on the theory and data behind models used to evaluate climate change impacts, rather than on the output of such models. In part this was because knowing the output of models is of little help without an understanding of the capa-bilities and limitations of the underlying models, and in part because the current pace of new research on applications of models is so rapid that a book devoted to this topic is sure to be outdated in a few years. Yet we recognize that a current summary of the literature will be useful to most readers, and so here provide a brief review of recent findings. For more exhaustive reviews, the readers are encouraged to consult recent assessments by various international groups (e.g., Easterling et al. 2007).
As with any discussion of impacts, a useful starting point is to define the scale and variable of interest. As described in Chapter 2, most people in the world are net buyers of food, and consume diets dominated by the three main staples (rice, wheat, and maize). The prices of these commodities are therefore among the most relevant to food security. Most people also live in communities that trade beyond their local borders, in many cases with places across the globe. For this reason the price of food in given country often depends more on global supply and demand than on local production.
A global perspective is therefore critical to assessing climate change impacts, even if one is interested in a single country (Reilly et al. 1994). Yet assessments for
Chapter 10Global and Regional Assessments
David Lobell and Marshall Burke
D. Lobell and M. Burke (eds.), Climate Change and Food Security ,
Advances in Global Change Research 37, DOI 10.1007/978-90-481-2953-9_10,
? Springer Science + Business Media, B.V . 200D. Lobell ( ) and M. Burke
Stanford University, CA, USA
1
178 D. Lobell and M. Burke individual regions or countries can still be useful, in two main ways. First, they allow more detailed study of local climate and crop changes that can feed into global assessments, which often have very rough estimates of regional yield responses.1 Second, they can focus on local adaptation options that would improve local yields in the face of climate change, regardless of global price changes. This chapter therefore addresses both global and regional studies.
10.2 G lobal Assessments
The first major studies of global agricultural impacts began roughly 20 years ago, as agriculture was one of the first sectors for which impacts of climate change were thought to be important (Kane et al. 1992; Rosenzweig and Iglesias 1994; Rosenzweig and Parry 1994). Then, as now, these efforts focused on linking three basic modeling pieces that had been previously developed and applied indepen-dently: (1) models of climate response to higher CO 2; (2) models of crop yield responses to climate change, higher CO 2, and, in some cases, potential farmer adap-tations; and (3) models of adjustments in the world food economy in response to differential yield effects in different regions (Fig. 10.1). Some studies focused solely on changes in worldwide aggregate production and prices, while others also investi-gated changes in food security, typically measured by the number of malnourished.One of the first seminal assessments considered the global impacts of doubling CO 2 from pre-industrial levels (Rosenzweig and Parry 1994) by utilizing a network of crop modelers from around the world, who provided estimates of yield impacts from locally calibrated models for prescribed climate changes in over 100 sites in 18 countries. These site-level estimates were then used to infer national level
Fig. 10.1 Outline of a common approach to estimating global impacts of climate change on food prices, production, trade, and hunger. Climate scenarios generated from climate models are used to drive crop yield models for individual locations. The simulated yield responses are then summed for different regions and input into a food trade model, which determines the equilibrium price for each commodity and the associated crop areas, yields, production, and trade for each region. The price changes are also often used to estimate the change in number of people at risk of hunger 1
For example, yield changes in Rosenzweig and Parry (1994) were prescribed by interpolating results from crop model simulations at individual sites, of which the only sites in sub-Saharan Africa were for maize in Zimbabwe.
17910 Global and Regional Assessments production changes for all cereals in all countries, based on similarities of agronomic characteristics among crops and agro-ecological environments among countries. The results were then aggregated into regional yield changes according to the regions defined in the Basic Linked System (BLS) model of agricultural trade.
Figure 10.2 presents the yield changes used as input to BLS for the four major com-modity groups treated in that study: wheat, rice, coarse grains, and oilseeds. These
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Fig. 10.2 Yield changes for doubled CO 2 (climate change plus CO 2 fertilization effects) used as input into global trade model in Rosenzweig and Parry (1994), based on crop model simulations for 100+ sites. Bars show yield changes using climate scenarios from the NASA GISS climate model, “+” indicates values for GFDL climate model, and “x” for UKMO climate model. The magnitude of CO 2 fertilization was 4%, 11%, 12%, and 17% for coarse grains, rice, wheat, and oilseeds, respectively. All yield changes correspond to simulations without any farmer adaptation
180 D. Lobell and M. Burke results exhibit some of the main features of yield changes in most global assessments. First, the net yield impacts of climate change and doubled CO 2 tends to be negative for most but not all region–commodity combinations. Second, high latitude countries tend to have lower impacts than tropical countries because they start from a cooler baseline. Third, C 3 crops (rice, wheat, and oilseeds) tend to have lower impacts than maize because of greater CO 2 fertilization. Fourth, yield impacts varied substantially for dif-ferent climate model scenarios, three of which were considered in the study.
An important innovation by Rosenzweig and colleagues was to examine the potential impact of adaptation in a systematic way. Each modeling group was asked to perform simulations with no adaptation (i.e. climate change and CO 2 effects only, as shown in Fig. 10.2), with “level 1” adaptations, where tactical decisions such as planting date and cultivar choice were optimized, and with “level 2” adaptations, which included more costly adaptations such as development of new irrigation infrastructure and new crop varieties. This design allowed an evaluation of the benefits of both small and large investments in adaptation.
Table 10.1 summarizes the global production changes that result from the yield changes illustrated in Fig. 10.2, as well as those under different adaptation scenarios. The authors concluded that, assuming the full effects of CO 2 fertilization were realized, impacts on global cereal production ranged from negligible to slight declines (<10%) depending on the climate model used. Level 1 adaptations had a fairly small effect on overall impacts, but more expensive level 2 adaptations were effective in minimizing negative outcomes.
The associated changes in cereal prices for doubled CO 2 and no adaptation ranged from 25% to 150% for the three climate scenarios, with increases in the number of malnourished by 10–60% (malnourishment prevalence in the BLS model increased by roughly 1% for each 2.5% increase in prices). For adaptation level 2, when global production changes ranged from +1 to ?2%, price changes ranged from ?5% to +35%, and malnourished populations changed by between ?2% and +20%. The role of on-farm vs trade adaptations in these projections are discussed further in Chapter 8.
Many subsequent global assessments have been conducted since the early 1990s (Reilly et al. 1994; Parry et al. 1999; Fischer et al. 2002; Darwin 2004; Parry et al. 2004; Fischer et al. 2005), providing some consensus on several key points:Table 10.1 The projected impacts of doubled CO 2 on global cereal production (% change) for different climate models, adaptation levels, and with and without CO 2 fertilization (from Rosenzweig and Parry 1994; see text for details on adaptation levels)
Scenario GISS GFDL UKMO Climate change only ?11?12?20
With CO 2 fertilization ?1?3?8With CO 2 and adaptation Level 10?2?6With CO 2 and adaptation Level 2
10?2Climate models: GISS = Goddard Institute for Space Studies (4.2, 11); GFDL = Geophysical Fluid Dynamics Laboratory (4.0, 8); UKMO = United Kingdom Meteorological Office (5.2, 15). Numbers in parentheses are global average change in temperature (°C) and precipitation (%) for each model.
18110 Global and Regional Assessments (i) Global price increases associated with a doubling of CO 2 range from negligible for moderate climate change to significant for more extreme climate scenarios.
As a doubling of CO 2 is likely to be reached near mid-century, studies that evalu-ate transient scenarios out to 2100 generally also consider higher CO 2 levels. Long-term price impacts tend to increase as CO 2 levels are increased, because the positive effects of higher CO 2 are increasingly outweighed by the negative impacts of associated climate changes.2 Equilibrium price changes thus often exceed 10% by the end of the century for most emissions scenarios (Easterling et al. 2007). (ii) Impacts are generally more negative for developing countries than developed
countries. This arises mainly from the fact that most developing countries are in tropical climates with a warmer baseline climate, so that warming more quickly pushes crops beyond their optimum temperature range. In addition, tropical countries tend to rely more on C 4 crops like maize, sorghum, and millet that exhibit small CO 2 fertilization effects. As a result of more detrimental effects in developing nations, trade models anticipate substantial expansion of trade flows from North to South.
(iii) Although the general North-South gradient in impacts is seen in most models,
there can be substantial heterogeneity within regions owing to the specific pat-terns of rainfall and temperature changes. For example, the United States exhib-ited among the most severe yield losses out of all nations in a study that used the Hadley Center’s HadCM3 model (Parry et al. 1999). Even neighboring countries can exhibit quite different responses depending on details of rainfall simulations. This is illustrated by simulated cereal yield impacts by 2050 in India and Pakistan from Fischer et al. (2002), where impacts ranged from 16% higher to 10% lower in India relative to Pakistan depending on the climate model (Fig. 10.3).
(iv) Adaptations can substantially reduce the impacts of climate change, but rela-
tively easy options such as planting date shifts generally have only a small impact while more expensive changes provide most of the benefit (see Chapter 8). The simulated benefits of adaptations are tempered by two caveats. First, few studies have explicitly incorporated the costs of these adaptations into mea-sures of economic impact, nor have they performed a clear cost-benefit analy-sis. Second, most studies find that adaptation is likely to proceed more effectively in developed nations, thus exacerbating the North–South gradient in impacts and the trade imbalances that result. Additional issues such as how quickly farmers can actually perceive climate trends are discussed in Chapter 8.
10.3 R egional Assessments
In addition to estimates of regional yield changes that have been developed in the process of global assessments (e.g., Fig. 10.2), there is also a growing wealth of studies focused on particular regions. Indeed, the literature is too vast to provide an exhaustive review. 2
However, higher emissions scenarios can actually reduce near-term impacts since the CO 2 fer-tilization effect responds instantly to higher CO 2 levels while the climate system takes several decades to respond.
182
D. Lobell and M. Burke
Instead we present below a brief summary for four key regions. We focus here on projected impacts in the absence of adaptation, with potential effects of adaptation addressed in Chapter 8.
10.3.1 C hina
Rice remains the main staple in China as it has for thousands of years, accounting for over one-quarter of the calories and roughly one-sixth of the protein consumed in 2003 (FAO 2007). Wheat, soybean, and maize are also important components of the modern Chinese diet, consumed both directly and indirectly via animal products (Chapter 2). Nearly all rice fields in China are irrigated (Huke and Huke 1997), while the majority of wheat and maize fields are rainfed.
China is commonly viewed as facing relatively benign impacts of climate change on agriculture. For example, Rosenzweig and Parry (1994) projected mod-erate yield declines for rice and maize but slight increases for wheat and soybean (Fig. 10.2). A main reason for the modest impacts is that most of the rainfed crops 2010
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Fig. 10.3 Yield changes in India and Pakistan by 2050 (relative to 1961–1990) simulated with the Agro-ecological Zone (AEZ) models of Fischer et al. (2002) for three different climate model scenarios: ECHAM (Max-Planck Institute of Meteorology), HADCM2 (Hadley Centre for Climate Prediction and Research), and CGCM1 (Canadian Centre for Climate Modelling and Analysis). Not only do average yield changes vary with climate scenario, but the relative impacts in India and Pakistan differ greatly, likely due to the spatial distribution of rainfall change
18310 Global and Regional Assessments are concentrated in Northern China where fairly cool temperatures predominate for most of the growing season. In Southern China, where rice is the dominant crop, widespread irrigation is assumed to prevent any significant losses that would arise from greater water stress.
Even with these moderating factors, warming is expected to harm yields in most assessments. Figure 10.4a summarizes estimates of rice yield changes from various crop-modeling studies of China that considered a range of warming. Studies often differ by a factor of two, depending on the crop model used, the projected change in rainfall, and other factors. Yet all show a negative response for warming. A recent Ricardian analysis of revenues from 8,405 households throughout China also found a negative marginal impact of temperature on the average crop revenues (Wang et al. 2008).
Thus, most projected gains in agriculture in China result from a fertilization effect of CO 2 that is simulated to overwhelm climate related losses. This CO 2 effect is illustrated for rice in Fig. 10.4 by the arrows, whose lengths indicate that the magnitude of CO 2 fertilization also differs considerably by study. Some prominent studies appear to include CO 2 effects that are much bigger than suggested by recent field experiments (see Chapter 7). For example, one analysis of impacts by 2050 projected rice, wheat, and maize yield losses under an A2 emission scenario of 12.4, 20.4, and 22.8%, respectively, without CO 2 fertilization, but yield gains of 6.2, 20.0, and 18.4% with CO 2 fertilization (Lin et al. 2005; Xiong et al. 2007). This corresponds to 18.6, 40.4, and 41.2% yield boost from CO 2 concentrations of 559 ppm, reflecting a major role of reduced water stress from stomatal closure in the version of the CERES models used in that study.
China India
20200% Y i e l d C h a n g e 0% Y i e l d C h a n g e
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?200123456Local Temperature Change (C)0123456
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a
b Fig. 10.4 Crop model estimates of rice yield changes for different levels of warming for (a ) China and (b ) India, as reported in various studies. Black dots indicate effects without CO 2 fertilization, and gray dots with CO 2 fertilization, with arrows connecting points from the same study. The only difference between points connected by arrows is thus the simulated effect of CO 2. Values were derived from three studies for China (Matthews 1995; Lin et al. 2005; Tao et al. 2008), and five for India (Matthews 1995; Lal et al. 1998; Saseendran et al. 2000; Aggarwal and Mall 2002; Krishnan et al. 2007)
184 D. Lobell and M. Burke Most yield impact assessments for China to date assume a constant supply of irrigation water. Given the crucial role that irrigation plays in Chinese agriculture, however, the potential of climate change to increase or decrease water availability and demand is also of concern. Tao et al. (2008) estimated water use in rice at five stations in China for various climate scenarios, and found in nearly all cases that a shortened growing season resulted in overall lower crop evapotranspiration and water use. Water use was further curtailed when CO 2 effects on stomatal closure were taken into account, even when yield gains were simulated. As a result of lower water use, irrigation demand was reduced in nearly all cases except where precipitation was projected to decline. These results suggest that shorter seasons and lower ET rates will tend to diminish water use in agriculture, but the demand for (and surely the availability of) irrigation water will also depend on uncertain precipitation trends.
10.3.2 I ndia
Indian agriculture is characterized by a wide range of crops, most prominent among them rice (44 Mha harvested in 2007), wheat (28 Mha), and millet (11 Mha) (FAO 2007). In contrast to China, most crops in India are grown in relatively hot condi-tions. Spring and summer temperatures commonly exceed 40°C even in the current climate. Thus, one would expect crops to be more sensitive to warming. Indeed, a survey of rice crop modeling studies indicate that even with CO 2 fertilization, warming above 2°C is likely to lower Indian rice yields (Fig. 10.4b ). Only a single study with nearly a 40% boost from higher CO 2 (Matthews 1995) shows yield gains for more than 2°C warming.
The sensitivity of Indian crops to warming is also evident in statistical analysis of time series data (Table 10.2). Using the approach outlined in Chapter 5 to esti-mate impacts by 2030, many important crops are anticipated to incur yield losses (Lobell et al. 2008). Combined with the fact that India possesses the highest popu-lation of undernourished people in the world (Chapter 2), food security in India appears particularly vulnerable to climate change.
In a Ricardian study, net farm revenues in India were similarly found to respond negatively to warming, with a 12% reduction in revenue for a scenario with 2°C warm-ing and a 7% increase in rainfall (Dinar et al. 1998). Thus, whether using crop models, time series based models, or panel based methods, the expected effects of warming are negative for most crops. In the near term, CO 2 benefits may counteract these losses, although not in the case of prominent C 4 crops such as millet and sugarcane.Like China, India is heavily reliant on irrigation and thus the future reliability of water resources will likely play a crucial role in determining the net impact of cli-mate change. Declines in irrigation water, whether resulting from climate change or other factors such as increased urban demand, could greatly increase the sensitiv-ity of crops to higher temperatures. For example, crop model simulations for wheat in Northwest India suggest that the net impact of 1°C warming is roughly double
18510 Global and Regional Assessments for a scenario reflecting severe water conservation than under business as usual irrigation (Lal et al. 1998). Unfortunately, the future effects of climate change on water resources in South Asia have not, to our knowledge, been closely examined as of this writing.
10.3.3 S ub-Saharan Africa
Each of the 47 countries in sub-Saharan Africa (SSA) has its own mix of primary crops and diets (Chapter 2). Nonetheless, as a whole Africa can be characterized as a conti-nent heavily dependent on C 4 cereals (maize, sorghum, and millets), cassava, ground-nuts, and, to a lesser extent, rice and wheat. SSA is also generally characterized by hot growing conditions relative to much of the developed world, although Southern African growing seasons can be relatively cool. Outside of a few countries, such as Sudan, irrigation is very rare in SSA, with roughly 5% of cereal area in irrigation as of 1995 and little growth expected by 2025 (Rosegrant et al. 2002).
Given these conditions – a warm baseline climate, a lack of irrigation, and a predominance of C 4 crops unlikely to respond strongly to higher CO 2 – it is not surprising that model projections of climate impacts on SSA crops have tended to be negative. In a study using CERES-Maize for all countries in SSA and Latin America, Jones and Thornton (2003) project a fairly average modest decline of 10% by 2055 using a climate scenario from the Hadley CM2 model, though they point to a wide range of impacts between and even within several countries.
Others have suggested more negative impacts, most notably the fourth assess-ment report of the IPCC, whose chapter on Africa concludes that “reductions in yield in some countries could be as much as 50% by 2020” (Boko et al. 2007). Table 10.2 Estimated sensitivity of average national yields of Indian crops to a 1°C rise in aver-age growing season temperature, based on time series analysis of 1961–2002 data (adapted from Lobell et al. 2008). The model included both average growing season temperature and rainfall. In several cases (e.g., millet) most of the model’s predictive skill came from rainfall, while tempera-ture sensitivities were not significant
Inferred yield change (%) per °C
Crop
Percentage contribution to calories in Indian diet Model r 2Mean Standard deviation Wheat
220.27?2.60.7Rice
270.63?4.0 2.0Sugarcane
140.03?0.1 2.2Millet
30.63?4.2 4.4Sorghum
20.140.8 6.5Maize
20.16?3.6 2.5Soybean
20.11?7.4 5.5Groundnut
20.67?3.4 5.5Rapeseed 20.45?7.4 2.5
186 D. Lobell and M. Burke Though this statement is accompanied only by a citation of a discussion paper on historical losses in drought years in Northern Africa, it nonetheless received wide-spread media attention.
There has been less work specifically devoted to SSA than many other regions, and as a result the understanding of potential outcomes and uncertainties has been quite limited (Challinor et al. 2007). The World Bank recently commissioned a series of cross-sectional studies of crop revenue in Africa (e.g., Kurukulasuriya et al. 2006), with mixed results but generally negative impacts. Time series models indicate that Southern Africa is extremely sensitive to warming, more so than the warmer tropical regions (Lobell et al. 2008). A likely reason for this is that fertilizer rates and average yields in Southern Africa are considerably higher, so that there is more room for damage in hot years relative to cool years.
Combining data from all countries into a panel analysis for the 1961–2002 period, a recent analysis by Schlenker and Lobell (2009) attempted to estimate the probability of different levels of yield impacts by 2050 for five major crops in SSA: maize, sor-ghum, millet, groundnuts, and cassava. The first four were found to have significant negative responses (not accounting for CO 2 fertilization), even in the case of millet that is generally regarded as relatively tolerant of hot conditions. There was no clear rela-tionship between cassava production and either temperature or rainfall, likely because cassava harvests are irregular and therefore collection of production data and definition of growing season weather are much more difficult than for other crops.
A comparison of the impact probability distributions for maize with point-esti-mates from previous studies is shown in Fig. 10.5 for four countries (for a more complete comparison, see Schlenker and Lobell 2009). The estimates of Parry et al. (1999) and Jones and Thornton (2003) generally fall within the distributions, although they tend toward the optimistic end of the range in most countries. Impacts projected by the FAO model (Fischer et al. 2002) in contrast appear much more optimistic than the other three studies. Since Fischer et al. (2002) only report estimates
Fig. 10.5 Estimated probability distribution of maize yield impacts of climate change, without adaptation, in selected African countries, based on Schlenker and Lobell (2009). Gray bars show 25th–75th percentile of estimates, whiskers show 5th–95th percentile, and middle vertical line shows median projection. Point estimates from Parry et al. (1999) (“P”), Fischer et al. 2002 (“F”), and Jones and Thornton (2003) (“JT”) are shown for comparison
18710 Global and Regional Assessments of impacts with CO 2 fertilization, we have subtracted 4% from each projection, equivalent to the reported CO 2 response for the AEZ model (Tubiello et al. 2007). This may explain some of the positive bias in the FAO relative to the other models, but in general the FAO results appear hard to justify, especially given the limited documentation of the model’s performance in simulating present-day African yields. Overall, the results in Fig. 10.5 support the notion that climate change presents a serious risk to African crop yields, and that losses by 2050 could easily exceed 20% in many countries.
10.3.4 U nited States of America
The United States dominates international trade of several agricultural commodi-ties. It is the leading exporter of maize, soybeans, and wheat flour, and therefore production of these crops in the United States has an important influence on food prices throughout the world and thus on food security. The United States is also one of the most extensively studied regions in terms of climate change impacts. An early analysis based on crop models indicated that some Northern regions would gain from temperature increases, but that in most important production regions, such as the Corn Belt and Southern Great Plains, warming accompanying a dou-bling of CO 2 would reduce yields by an amount roughly equal to the fertilization effect of CO 2 (Adams et al. 1990). Thus, it was concluded that a doubling of CO 2 would result in small net changes to major commodities in the United States, and that adaptation to warming could even result in net benefits.
These conclusions have been generally supported by many subsequent studies, many of which are reviewed in a recent national summary report (CCSP 2008). This report also highlights some factors that have not been considered in most modeling studies, such as the likely northward migration of weeds. The range of several important pests, such as corn earworm, are also currently constrained by winter temperatures and can be expected to expand greatly in future climates (Diffenbaugh et al. 2008). There may also be smaller than expected CO 2 benefits for the most widely grown crop, maize (see Chapter 7). Finally, there might also be greater impacts from extreme heat episodes than most currently used models antici-pate. For instance, Schlenker and Roberts (2008) find that yields of maize, soy-beans, and cotton in the United States are very sensitive to extreme heat (see Chapter 6). Such results raise important questions about the generally low sensitiv-ity of process-based crop models to warming in these systems.
As some key regions such as California and Nebraska are heavily dependent on irrigation, changes in water resources will also be important. For much of the coun-try, the future direction of rainfall trends remains ambiguous and therefore water availability may increase or decrease (Thomson et al. 2005a). In the West, heavy dependence on snowpack combined with anticipated higher temperatures will very likely result in reduced water availability during the growing season months (Maurer and Duffy 2005). Interestingly, total national irrigated area was projected
188 D. Lobell and M. Burke to decrease across a range of climate scenarios, because rainfed crops became more competitive in scenarios of rainfall increases while irrigation water became limiting in scenarios of drying (Thomson et al. 2005b).
10.4 M easuring Uncertainties
The conclusions outlined above represent, in most cases, a relatively broad consen-sus among researchers. However, it is important to emphasize that all global and most regional assessments to date can best be characterized as “best-guess,” usually supplemented with some simple sensitivity tests such as impacts with and without CO 2 fertilization or adaptation. Yet a consensus among best guesses does not imply that we fully understand the risks associated with climate change, even at global scales (see Chapter 1). True uncertainty analysis, which attempts to quan-tify the total uncertainty inherited from all individual sources of uncertainty and estimate probabilities of different outcomes, has generally been absent. In some cases, failing to consider uncertainties can lead to a false sense of confidence about projections. In other cases, simple sensitivity tests can overstate uncertainties, such as comparing results from models run with and without CO 2 fertilization (i.e. 30% or 0% fertilization) when the true range of uncertainty likely lies in between.
In our opinion, the remaining key need in impact assessments is to better char-acterize uncertainty and risks. This may include incorporating new processes (such as ozone or pest damage) but will likely center on better understanding the pro-cesses already treated in current models. Accomplishing this task is likely beyond the means of any single research group, given that no group has the number or diversity of models needed to evaluate the full suite of uncertainty sources. A proven strategy for assessing uncertainty is thus to compare model outputs from different groups, so-called model intercomparison projects (MIPs), such as is com-monly done with climate models (see Chapter 3).
Periodic literature reviews and syntheses, such as those by the IPCC, provide use-ful insight into uncertainties but are not true MIPs in two important respects. First, studies often differ widely in the specific questions they address, and as a result typi-cally evaluate different outcomes, time scales, and spatial scales of interest. For example, many studies have used equilibrium scenarios of doubled CO 2, while others have used transient climate change projections. In the former, the climate system has come to equilibrium with atmospheric CO 2 and therefore tends to be warmer than climate at the time of CO 2 doubling in a transient simulation, because the climate system takes decades to respond to changes in CO 2. Doubled CO 2 experiments are therefore difficult to interpret as projections for any particular year. A more straight-forward disparity is that many studies examine only production impacts or global commodity prices while others calculate changes in number of malnourished.
A second major challenge in the absence of MIPs is that most studies do not examine all relevant sources of uncertainty, and even a collection of studies will often all treat some model components in the same way. For example, most existing global assessments employ the same economic trade model (BLS), so that uncertainties associated with the structure of the trade model cannot be fruitfully explored.
10 Global and Regional Assessments
189 Only when multiple groups follow the same model experiment design can a siz-able number of simulations for the same variable of interest, and a systematic evalu-ation of the main potential sources of uncertainty, be ensured. One of the main obstacles to implementing MIPs is the substantial amount of foresight, coordina-tion, and resources required to run multiple combinations of state-of-the art climate, crop, and trade models. But such MIPs will be crucial if we are to make progress in measuring uncertainties in impact or adaptation assessments.
An alternative, although by no means a substitute, to MIPs is to represent uncer-tainty in each component with simple statistical models that are computationally much more efficient and can readily represent uncertainties. This approach is exem-plified by Tebaldi and Lobell (2008), who attempted to compute probability distri-butions of climate change impacts on global average yield changes for maize, wheat, and barley production for 2030 (see Chapter 3).
With all of the potential sources of uncertainty, one may wonder whether it is really necessary to examine each equation in each model or if a few key equations deserve most of the scrutiny. In fact, evaluating each equation is likely neither fea-sible nor necessary, but which should we focus on? Some insight into this question can be gained by evaluating projections with individual factors varied one at a time over a plausible range of values. This type of sensitivity analysis was used by Lobell and Burke (2008) to evaluate sources of uncertainty for projections of yield losses by 2030 in developing world regions.
Uncertainties from four factors were considered in the study: projected temperature change from climate models, projected precipitation change, sensitivity of crops to warming (estimated using time series analysis), and sensitivity of crops to rainfall. In most regions uncertainties related to rainfall were surprisingly small relative to temperature – surprising because year-to-year rainfall variations can be so important to crop production. However, temperature trends are much larger relative to historical variability than rain-fall, with mean temperature trends typically twice as big as historical standard deviation while rainfall trends were much smaller than historical variability. In particular, that study identified crop sensitivity to temperature as a key unknown, i.e. there was often a big difference between impacts using a low vs high estimate of temperature sensitivity. Thus, efforts to quantify and reduce impact uncertainties would be well served by a focus on crop temperature responses. Uncertainties in future rainfall trends were less important overall, but emerged as the critical factor in a few key crops, such as rice and millets in South Asia.
10.5 S ummary
This chapter has provided a glimpse into results from global and regional assess-ments conducted over the past decades. The key points are summarized below: Global assessments have generally concluded small changes in global prices for a ?
, with gains in developed countries balancing losses in the tropics.
doubling of CO
2
190 D. Lobell and M. Burke Yet these conclusions have been based on a relatively small number of models, and the sources and magnitudes of uncertainty have not been well quantified.
Projections for CO ?
2 concentrations more than double preindustrial levels (280
ppm) suggest price increases and negative impacts for food security.
Regional assessments in China tend to show negative effects of warming but net ?
positive yield changes when including CO
2 fertilization, though many of these
studies have unusually large amounts of modeled fertilization (upwards of 40% for mid-century).
Projections tend to be negative for India and Sub-Saharan Africa even over the next ?
few decades, indicating that these two regions face relatively large risks of crop yield losses. Given the concentration of malnourished populations in these regions (see Chapter 2), these changes are of great importance to global food security.
The United States will likely experience downward pressure on maize yields, a ?
C
4 crop with limited CO
2
fertilization. Yield losses from warming will likely be
balanced in other crops by CO
2 effects in the next few decades.
Both regional and global assessments would benefit from more explicit consid-?
eration of uncertainties from a variety of sources. A particularly important source of uncertainty, among processes currently represented in models, appears to be temperature sensitivity of crops. Effects of pests, diseases, extreme effects, and ozone represent additional factors that are not currently in most models.
A relatively costly but invaluable approach to quantifying uncertainties is to ?
have multiple modeling groups perform identical experiments with different models. An alternative is to approximate uncertainty in individual model com-ponents with statistical distributions, which lends itself to rapid propagation of errors using Monte Carlo techniques.
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林业有害生物防治承包合同书 甲方： 乙方： 根据《中华人民共和国合同法》及相关规定，甲、乙双方本着平等、自愿的原则，经过友好协商，就乙方承包XX县XX镇面山及县境内高速公路沿线林业有害生物承包防治事宜，特订立本合同，以资信守。 一、承包内容 （一）工程名称：林业有害生物承包防治 （二）防治面积：每代X万亩 （三）承包内容：以政府购买社会化服务的形式，乙方负责春、秋两代松毛虫的防治工作。 （四）防治单价及金额：单价X元/亩,总金额：XX万（元）。防治药物由林业局代购20吨，6500元/吨，共13万元，其余及秋季所需药物由防治公司负责，防治工费为XX万元。 二、防治时间 2015年3月20日-4月25日；2015年8月-9月。 三、结算方式 （一）春代防治结束并验收达标后甲方向乙方支付合同防治工费的50%费用（8.35万元）。 （二）秋代防治结束验收达标后再次支付剩余合同防治工费的50%费用。 （三）乙方提供报账所需票据。甲方现金或转账至乙方指定账户。 四、承包防治服务标准
按合同签订附件《2015年松毛虫防治作业设计》（以下简称《作业设计》）中所提出的技术要求标准进行技术服务，并按《作业设计》标准进行施工验收。 五、甲方责任 （一）甲方负责提供技术指导，宣传施药过程中防止人畜中毒的注意事项和所要采取的技术措施，做好喷洒质量监督管理。 （二）在承包期内应配合乙方工作，提供必要的施工条件，施工结束后组织相关人员开展防治效果验收。 （三）对乙方服务质量不满意，应及时主动告知乙方，并提出合理建议双方共同协商，不断完善服务措施。 （四）按时支付工程款项，保证乙方正常施工。 六、乙方责任 （一）乙方在防治过程中及防治后，上岗防治施工作业人员必须严格遵守防治作业操作规程，保证防治质量和防治作业安全，不能使原有生态遭到破坏，严防机械起火引发森林火灾，否则一切法律后果由乙方自负。 （二）防治过程中乙方须遵循安全生产措施进行防治，做好安全施工培训，作业过程中严格遵守甲方的各项规章制度，工作人员在防治过程中发生的人畜中毒和伤病事故等一切意外事故由乙方自理，甲方概不负责。 （三）甲方如需调整防治地块及范围，乙方需配合调整防治。
投资建设珠海横琴岛现代商务科技综合体 项目建议书 香港诚丰集团 2013年3月31日
目录 第一章项目基本情况 (6) 项目简述 (6) 项目名称 (6) 项目性质 (6) 项目建设地点 (6) 项目的指导思想和原则 (6) 项目内容及规模 (7) 项目的战略定位 (10) 现实背景 (11) 政策依据 (12) 项目建设的可行性分析 (12) 区位及交通优势 (12) 环境优势 (13) 旅游资源优势 (13) 政策优势 (13) 项目建设的必要性分析 (14) 符合国务院关于横琴开发的有关政策规定 (14) 有利于优化产业结构，提高产业创新能力 (14) 有利于促进总部经济发展，带动周边商业和旅游业繁荣.. 15第二章项目申报单位的基本情况 (16) 申报单位概况 (16)
第三章市场分析 (18) 项目地位分析 (18) 项目生存的市场经济条件分析 (19) 高新技术产业发展分析 (19) 总部经济基础条件分析 (22) 高端现代服务业发展基础分析 (23) 创意文化产业的发展条件分析 (24) 城市发展对项目的驱动分析 (26) 生态优势 (26) 人才资源优势 (26) 产业集聚优势 (27) 开放型经济发展优势 (27) 本项目独特优势分析 (28) 聚集创新优势 (28) 整合优势 (28) 强强联手优势 (29) 第四章投资估算 (30) 投资概况 (30) 估算编制依据 (30) 估算范围 (30) 各分项投资估算 (31)
资金筹措 (32) 权益资金筹措 (32) 债务资金筹措 (32) 第五章投资回收期测算 (34) 项目收入与税金预测 (34) 收入预测 (34) 税金预测 (34) 项目投资效益分析 (34) 投资收益率 (34) 效益费用比 (35) 静态投资回收期 (35) 动态投资回收期 (35) 第六章项目的综合评价 (37) 项目经济评价 (37) 利税贡献 (37) 行业引领 (37) 技术引领 (38) 平台建设 (38) 项目社会评价 (39) 项目风险评估 (40) 市场风险 (40)
农作物病虫害绿色防控技术应用有哪些 绿色防控，是在2006年全国植保工作会议上提出“公共植保、绿色植保”理念的基础上，根据“预防为主、综合防治”的植保方针，结合现阶段植物保护的现实需要和可采用的技术措施，形成的一个技术性概念。病虫害绿色防控是采取生态控制、生物防治、物理防治、科学用药等环境友好型措施来控制有害生物，是贯彻“公共植保、绿色植保、科学植保”的重大举措，是发展现代农业，促进农业生产安全、农产品质量安全、农业生态安全和农业贸易安全的有效途径。农作物病虫害绿色防控主要技术措施： 一、农业防控技术 1、选用抗病虫品种，合理布局作物品种、合理轮作。 2、加强水肥管理，清洁田园。 3、提高生物多样性，推广农作物间作套种技术。 二、物理防控技术 1、色板诱杀 粘虫板诱杀是利用害虫的趋色习性来诱杀害虫。如用黄色粘虫板诱杀有翅蚜、白粉虱、斑潜蝇等害虫的成虫，蓝色粘虫板诱杀蓟马。每亩用15-30块黄蓝板，安装在蔬菜、蓝莓、玫瑰等作物时要高于作物15-20厘米，并随着植株生长不断调整；果园可挂在果树中部。 佳多粘虫板分黄、绿、红、蓝、白、黑、紫、青、粉、灰十种颜色1、打开即用，使用方便2、特殊色谱，双面诱捕，防治效果显著。3、特定板质，平整不卷曲，防水高粘度胶，抗晒、耐雨淋，高温不老化，持久耐用。在温度10℃～70℃的环境中基板无明显变形，胶体不流化，遇水不溶解。在使用中，色泽一致，在强烈阳光照射下向光面与背光面无明显色差。粘虫板材质：PP材料，具有一定的强度、硬度、耐湿，双面涂胶、板面不卷曲。 2、频振式杀虫灯诱杀 频振式杀虫灯由佳多公司1991年研发生产，频振诱控技术主要利用害虫的趋光、波、色、味的特性诱集害虫。高压电网将鳞翅目（甜菜夜蛾、斜纹夜蛾、甘蓝夜蛾、小菜蛾、螟虫、粘虫、地老虎等）、鞘翅目（金龟子等）1327种成虫击晕后落入接虫袋，从而达到防治害虫的目的。可在粮食、蔬菜、果树等作物上使用。一般30-50亩左右安装1盏杀虫灯。 3、性诱剂诱杀 性诱剂（性信息素诱导剂）诱杀是利用昆虫的性外激素，引诱异性昆虫达到诱杀或迷向的作用，影响正常害虫的繁殖，从而减少其后代种群的数量，达到控制的效果。目前，使用性诱剂诱杀可有效控制玉米螟、小菜蛾、甜菜夜蛾、斜纹夜蛾等害虫的成虫。一般每亩使用3个诱捕器。 4、避雨控病技术 葡萄生产上，雨季开始之前，在葡萄树冠顶部搭建简易避雨的拱棚，使葡萄植株、枝蔓、花、果能人为地避开自然雨淋，截断引起葡萄病害发生流行的环境因子，达到控制或减轻如葡萄白腐病、炭疽病、霜霉病、褐斑病等病害的发生，提高葡萄产量、质量。 5、果实套袋技术 果实套袋技术是提高果实品质的有效措施之一，其最大的好处是保护果实免遭农药污染，生产绿色果品。同时套袋后果实与外界隔离，病虫难以侵害果实，可有效降低病虫发生危害。 三、生物防控技术 利用微生物源、植物、动物源农药、抗生素等生物制剂防治农作物病虫，可选用天然除虫菊素、苏云金杆菌（BT）、白僵菌、绿僵菌、阿维菌素、捕食螨、烟碱、苦参碱、宁南霉素等防治蚜虫、菜青虫、小菜蛾、叶螨、斑潜蝇、夜蛾类害虫、病毒病、霜霉病等。 四、科学用药技术
我国的主要粮食作物 水稻 (1)南方稻谷集中产区秦岭—淮河以南，青藏高原以东的广大地区，水稻面积占全国的95%左右。按地区差异，又可分为三个区。 ①华南双季籼稻区。包括南岭以南的广东、广西、福建、海南和台湾等五省区。该区属于热带和亚热带湿润区，水、热资源丰富，生长期长，复种指数大，是我国以籼稻为主的双季稻产区。海南等低纬度地区有三季稻的栽培。 ②长江流域单、双季稻区。包括南岭以北、秦岭—淮河以南的江苏、浙江、安徽、江西、湖北、湖南、重庆、四川、上海等省市和豫南、陕南等地区。该区地处亚热带，热量比较丰富，土壤肥沃，降水丰沛，河网湖泊密布，灌溉方便，历年来水稻种植面积和产量分别占全国2/3左右，是我国最大的水稻产区。该区以长江三角洲、里下河平原、皖中平原、鄱阳湖平原、赣中丘陵、洞庭湖平原、湘中丘陵、江汉平原以及成都平原等最为集中。长江以南地区大多种植双季稻，长江以北地区大多实行单季稻与其他农作物轮作。籼稻和粳稻均有分布。 ③云贵高原水稻区。本区地形复杂，气候垂直变化显著，水稻品种也有垂直分布的特点，海拔2000米左右地区多种植籼稻，1500米左右地区是粳、籼稻交错区，1200米以下种植籼稻。本区以单季为主。 (2)北方稻谷分散区秦岭—淮河以北的广大地区是属单季粳稻分散区。稻谷播种面积占全国稻谷总播种面积的5%左右。具有大分散、小集中的特点。主要分布在以下三个水源较充足的地区：东北地区水稻主要集中在吉林的延吉、松花江和辽河沿岸；华北主要集中于河北、山东、河南三省及安徽北部的河流两岸及低洼地区；西北主要分布在汾渭平原、河套平原、银川平原和河西走廊、新疆的一些绿洲地区。北方分散产区的水稻以一季粳稻为主，稻米质量较好。 小麦 （1）春小麦区我国春小麦占全国小麦总产量的10%以上，主要分布于长城以北，岷山、大雪山以西气候寒冷、无霜期短的地区，小麦只能在春天播种，当年收割，是一年一熟制作物。其中黑龙江、内蒙古、甘肃和新疆为主要产区。 （2）北方冬麦区分布在长城以南，六盘山以东，秦岭—淮河以北的各省区，包括山东、河南、河北、山西、陕西等省。是我国最大的小麦生产区和消费区，该区小麦的播种面积和产量均占全国的2/3以上，有我国的“麦仓”之称。 （3）南方冬麦区分布在秦岭—淮河以南、横断山以东地区。安徽、江苏、四川和湖北等省为集中产区，大部分为棉麦和稻麦两熟制。本区居民以稻米为主食，故小麦商品率较高。 玉米 玉米属高产作物，经济价值较高，是我国最主要的杂粮，在粮食作物中仅次于水稻、小麦，居第三位。产量仅次于美国，居世界第二位。玉米对自然条件要求不严格，在我国分布很广，各地都有分布，其中以吉林、山东、河北、辽宁、四川产量最多。 其他作物 (1)高粱 高粱具有抗旱、耐涝、耐盐碱、适应性强的特性，所以在我国北方干旱地区、涝洼及盐碱地区多有种植。高粱在我国分布很广，以东北平原最为集中，其次为黄河中下游和淮北平原一带。 (2)谷子 谷子是我国传统粮食作物。谷子具有较强的抗旱能力，需水量少，比小麦低1.5倍左右，对土壤要求不严格，生长期较短。谷子容易储藏，适宜作储备粮，营养价值较高，主要分布在
林业有害生物防治工作 技术办法 集团公司文件内部编码：（TTT-UUTT-MMYB-URTTY-ITTLTY-
张掖黑河湿地国家级自然保护区 省级财政林业有害生物防治补助资金项目 实 施 方 案 按照《森林病虫害防治条例》有关规定，认真贯彻“预防为主，科学防控，依法治理，促进健康”的林业有害生物防治方针，紧紧围绕我市林业有害生物发生危害及防治工作实际，以控制重点区域主要有害生物为目标，有效保护湿地资源，巩固湿地恢复与治理成果，促进林业生态体系和林业产业体系又好又快发展。 一、项目概况 近年来，我市按照“保护黑河湿地，建设生态张掖”的总体思路，强势推进黑河流域湿地保护工程、市区园林绿化建设工程，使全市的湿地资源得到了有效的保护，市区园林绿化水平及档次不断得到提升。但是长期以来，由于以黄斑星天牛为主的杨树蛀干害虫在湿地自然保护区和市区园林绿地中呈蔓延之势，导致蚜虫、槐木虱、红蜘蛛、蚧壳虫等病虫害危害不断加剧，给自然保护区的资源管理和市区园林绿化苗木造成较大的经济损失，严重危及我市生态环境安全。尽管我局采取了多项措施，组织技术人员开展了林业有害生物监测调查及防治工作，但由于病虫害发生面积大，防治资金严重短缺，使有害生物防治工作效果不明显。为加强自然保护区及市区园林绿地有害生物检疫监测、宣传培训及防治工作，防止林业有害生物扩散蔓延，确保黑河湿地资源生态系统安全和市区园林绿化建设成果， 二、指导思想
大力加强林业有害生物监测预警、检疫御灾、防治减灾、应急反应和防治法规体系建设，实现林业有害生物防治的标准化、规范化、科学化、法制化、信息化，大力加强重要有害生物重点治理，建立健全突发事件应急机制，大力推进无公害防治、预报实施专群结合、联防联治、标本兼治，确保危险性有害生物扩散蔓延趋势得到较大缓解，扭转林业有害生物严重发生的局面，使主要林业有害生物的发生范围和危害程序大幅度下降，促进森林健康成果。 项目实施内容 有害生物监测预警设，防治技术人员培训，检疫检验和除害处理 二、项目目标 林业有害生物成灾率控制在3.5‰以下，总体防治率达到85%以上，确保全市生态安全。 三、主要发生的林业有害生物 全市主要林业有害生物发生种类14种，其中虫害7种，即松毛虫、天牛、木蠹蛾、杨舟蛾、刺蛾、叶甲、蚜虫。 病害7种，即褐斑病、炭疽病、软腐病、烟煤病、锈病、黑斑病、赤枯病。 需监测的主要林业有害生物对象有线虫病、黄斑星天牛、介壳虫等。 四、主要林业有害生物防治方案 林业有害生物防治，严禁使用国家禁用的化学农药，突出营林技术、生物农药及其它无公害杀虫剂防治措施。食叶害虫的无公害防治率要达到100%，对其它林业公害生物，无公害防治率要达到80%以上。防治以生物防治、物理防治、穴状抚育或改变林相为主；天牛以诱捕器诱杀为主；其它病虫害以营林措施及修技抚育等为主。确保无公害防治率达标。 (一)松毛虫
县林果病虫害防治技术方案 我县分布的主要病虫害有：糖槭蚧、大球蚧、吐伦球坚蚧、堆粉蚧、春尺蠖、沙枣跳甲、鼠害等，寄主有：枣、桃、杏、酸梅、苹果、梨、沙枣、柳、杨、榆、槐、葡萄等林木及其苗木。 森林病虫害防治的总体思路： 1、森林病虫害的防治要贯彻“预防为主，综合治理”的森保方针，严格执行《森林病虫害防治条例》，实行“谁经营、谁受益、谁防治“的责任制度。 2、加强检疫，严防病虫害的人为传播是预防森林病虫害发生危害的前提，要广泛开展森林植物的检疫工作。 3、采取实际步骤，大力推进以营林为主的治本措施。 (1)、培育良种壮苗，加强对抗病优良树种的引进工作。 (2)、根据分类经营的原则，针对不同的林种制定相应的营造混交林的标准。 (3)、加强低产林改造，有计划的将病虫害严重的纯林改造成混交林。 (4)、加强林木的栽培管理，及时灌水、施肥，增强树势，合理修剪，保持树木通风透光，为林木的生长创造良好的条件，可提高树体抗病虫能力，以而减轻病虫危害。 一、蚧类害虫防治技术方案（大球蚧、糖槭蚧、吐伦球坚蚧） （一）防治对策 根据“预防为主，综合治理”的方针和保护生态环境的原则，坚持以适地适树和选择抗性树种为主的原则，以生物制剂、仿生农
药和植物性杀虫剂为主导，协调运用人工、物理、化学的防治措施，降低虫口密度，压缩发生面积，切实控制其蔓延危害。 （二）防治措施 1、生物防治 果树落叶（11月中旬）后，用清园剂（融杀蚧螨）100倍液或石硫合剂5波美度喷雾，这是压低蚧类害虫虫口基数的关键措施。 2、人工防治 （1）结合修剪，剪除虫口密度大的枝条，带出果园集中烧毁； （2）冬季农闲时期，组织农民用毛刷刷除枝条上的蚧类害虫； （3）刮除老树皮，用涂白剂进行树干涂白。 3、化学防治 对防效差的果园，可采取春季补防和若虫期防治的办法。 （1）杏、桃、巴旦木等树在3月5日至3月20日前，红枣在4月5日至20日，核桃在3月25日至4月5日，葡萄、石榴在开墩时，用速扑杀、蚧虫毒、乐斯本1000－1500倍液喷雾防治。 （2）在5月中旬至6月上旬，初孵若虫期采用速扑杀、蚧虫毒、乐斯本1000－1500倍液喷雾。 二、春尺蠖防治技术方案 （一）防治对策 根据“预防为主，综合治理”的方针和保护生态环境的原则，坚持以适地适树和选择抗性树种为主的原则，以生物制剂、仿生农药和植物性杀虫剂为主导，协调运用人工、物理、化学的防治措施，降低虫口密度，压缩发生面积，切实控制其蔓延危害。 1、轻度发生区
深圳前海及珠海横琴 考察报告
目录 一、考察的基本情况 (1) （一）前海合作区 (1) 1.前海合作区区域范围 (1) 2.前海合作区战略定位 (2) 3.前海合作区主导产业 (2) 4.前海合作区发展目标 (4) 5.前海合作区管理机构设置 (4) （二）深圳市前海金融控股有限公司考察情况 (5) 1.公司基本情况介绍 (5) 2.公司赴港发行人民币债券 (5) （三）前海融资租赁考察情况 (6) 1.前海融资租赁介绍 (6) 2.前海融资租赁发展优势 (6) 3.前海融资租赁发展目标 (7) （四）珠海横琴考察情况 (7) 1.横琴定位 (7) 2.横琴产业发展 (9) 3.横琴制度创新 (10) 4.横琴发展目标 (10) 5.横琴七大产业 (11) 二、考察启示及工作建议 (11)
1.前海管理局政企合一 (11) 2.通过法制保障稳定的发展 (12) 3.土地出让，弹性年期 (13) 4.融资租赁因地制宜 (13) 5.横琴以产业促进招商 (14) 附件：前海及横琴优劣势分析精点 (15)
一、考察的基本情况 （一）前海合作区 1.前海合作区区域范围 2010年8月26日，国务院批复同意《前海深港现代服务业合作区总体发展规划》（以下简称《规划》），这是国务院批复的第十五个区域发展规划，是前海合作区成立的法律依据。 前海合作区是十五个区域（上海浦东新区、天津滨海新区、珠海横琴新区、福建海峡西岸经 济区等）中面积最小的，只有15平方 公里。在前海合作区范围内，还存在 一个保税港区，实行封闭管理，规划 控制面积3.71209平方公里，保税港 区2008年10月18日由国务院批准设 立。除去保税港区，前海合作区真正 的面积只有11平方公里多一点。 前海合作区位于深圳西部蛇口半岛的西侧、珠江口的东岸，依山面海，与伶仃洋相望，地处珠三角区域发展主轴和沿海功能拓展带的十字交汇处，由双界河、月亮湾大道、妈湾大道和前海湾海堤岸线合围而成。所有土地系由填海造地而来，土地及自然资源条件十分优越，是目前深圳特区内尚未开发的最大一块处女地。
农作物病虫害绿色防控技术
乐东县农作物病虫害绿色防控技术 海南省乐东黎族自治县是我国重要的南繁育种基地之一，同时也是“中国果菜无公害十强县”“中国香蕉之乡”和国家现代农业示范区。全县年种植水稻40万亩，蔬菜30万亩，香蕉12万亩，橡胶16万亩，芒果9万亩，玉米5万亩，龙眼2万亩，荔枝1万亩，还有槟榔、番薯和花生等作物，是海南省的农业的生产大县。随着农业产业结构的调整，农作物病虫害种类增多，发生危害严重，一些次要害虫上升为主要害虫。为把病虫害危害造成的损失降到最低限度，提升我县农产品质量安全水平，积极推进农作物病虫害绿色防控技术的推广应用，贯彻“公共植保”、“绿色植保”理念，有效控制农作物病虫的危害，促进农业增效、农民增收。现根据历年来的生产实践经验，总结出绿色防控技术，提供与农业界同行进行农业技术交流，以相互切磋学习，共同提高绿色防控技术水平，为农作物增产增收保驾护航。 1、农业防治技术 1.1选用抗病、虫品种，合理布局。 1.2栽培管理。精耕细作，适时抢墒播种或覆膜，促进早出苗、出壮苗。加强水肥管理，施足腐熟的有机肥，增施磷钾肥，提高植株抗病力。采取微灌、滴灌并注意田间排水，降低湿度，减轻病害发 1.3清洁田园。及时清除病残体，减少病菌侵染源；秋耕深翻，降低越冬虫源；结合中耕除草，及时清除田间、地埂等杂草，减少虫卵。
2、生态控制技术 大力开展植树种草，绿化荒山荒坡，增加植被覆盖度，减少宜于蝗虫等害虫栖息、繁殖的生境。结合农业综合开发和农田建设，将分散的小块耕地连结成大面积农田或退耕还林还草，最大限度地减少田埂、夹荒、地边、地角等适宜蝗虫、草地螟产卵场所。在准确预报的基础上，结合当地优势产业的发展，因地、因时制宜，种植宜发害虫非喜食的作物，如木薯、小杂粮等，以避害或减轻危害。推广采用覆膜起垄或搭架栽培，提高垄间通风透光，采用微灌、膜下滴灌，避免大水漫灌，注意雨后及时排涝，降低田间湿度，保护地并加强通风、透光、降湿，以减轻病害的发生。 3、物理防治技术 3.1黄色粘虫板诱杀 主要利用害虫的趋黄性，在温室大棚内设置黄色粘虫板，诱杀成虫。可用于防治蚜虫、粉虱、斑潜蝇等。作物定植后在温室大棚内选用0.25m×0.2m或0.4m×0.25m的黄色粘虫板悬挂在蔬菜大棚的行间、株间，高出植株顶部，随植株生长的高度及时调整黄板的高度，每亩地挂黄板20-30块。当板上粘满虫子时及时清除或更换。设置黄板以预防为主，主要在害虫发生初期使用。 3.2频振式杀虫灯诱杀 频振式杀虫灯主要利用害虫的趋光特性引诱害虫，并通过高压电网将害虫击晕后落入接虫袋，然后用人工方法或生物防治或化学药剂处理等方法，将害虫消灭，从而达到防治害虫的目的。可在玉米、蔬
主要农作物的种植时间和收获时间与小麦分布 冬小麦：9、10月份播种，次年4、5月份收割（主要在长城以南） 春小麦：春节后播种，8、9月份收获（主要在长城以北） 棉花：4——9月 油菜：12——次年5月 花生：4——10月 甜菜：5——9月 水稻：东北：5、6——10月（单季） 南方：4——7月，7——10、11月（双季） 华北冬麦区，是我国主要的冬麦区，播种面积占我国的47％，总产占我国的53％。一般年份冬麦可安全越冬，大于0℃积温4100℃。可供小麦、早中熟玉米的两熟。水是决定播种面积的限制因子。黄河以北地区多种在灌溉地上，黄淮平原是旱地麦适宜区，生产潜力大。 长江中下游冬麦区，种植面积占12.3％，总产占45％。3－5月江淮平原光温水较协调。3－5月降水量大于450mm的地区属不适宜种麦区。该区小麦商品率较高。 东北春麦区，黑龙江、吉林温度低，春麦适宜。 西北春冬麦区，灌区和黄土高原区。除南疆外主要是春小麦，南疆冬小麦，适应好，生产力高，品质优。 西南麦区，四川盆地、云贵高原。四川冬暖，温水适宜，但光照少，病虫严重。高原光照强，灌溉成熟期温度低，利于高产。
青藏高原冬春麦区，光温水配合利于小麦生长、抽穗－成熟期长达50－80天。降水不足，小麦种在水浇地上。 我国主要农作物的分布及其生长习性 1、小麦分冬小麦和春小麦，冬小麦主要分布在华北及其以南的地区，秋种夏收。油菜主要分布在长江流域，种植和收获季节大致与冬小麦一致。（长江中下游地区有一农谚：“寒露籽，霜降麦”，说的是当地一般在寒露时种油菜，霜降时种小麦。它们一般在端午前后收获，北方地区收获季节要晚些。故称夏收作物） 2、棉花的分布主要是五大商品棉基地。春种秋收 3、水稻在全国种植普遍，可结合各地的种植制度来确定其种植和收获季节。如东北地区，一年一熟，则是春种秋收；长江中下游地区一年两熟，有的田地是种双季稻（即一块地中一年种两次水稻），双季稻中，早稻是春种夏收（一般是5。1前插完秧苗，8。1前抢收早稻，抢插晚稻，故将7月下旬至8月上旬这段时间称为双抢），晚稻是夏种秋收（11月初收完）。还有一种是稻麦连种，即当地的冬小麦收获后再种水稻，则这种水稻是夏种秋收（它收获不久再种小麦或油菜）。一般考试只考双季稻。在南方有些地区（如海南）可种三季水稻。 4、花生的分布更为广泛，经山东丘陵和辽东丘陵最多，为春种秋收，其他地方（如浙闽丘陵、两广丘陵、云贵等地也有较广的分布，一般也是春种秋收。 5、大豆主产区在东北平原。根据当地一年一熟的情况，可推测出是春种秋收。
林业有害生物防治工作 技术方案 Document number：WTWYT-WYWY-BTGTT-YTTYU-2018GT
张掖黑河湿地国家级自然保护区 省级财政林业有害生物防治补助资金项目 实 施 方 案 按照《森林病虫害防治条例》有关规定，认真贯彻“预防为主，科学防控，依法治理，促进健康”的林业有害生物防治方针，紧紧围绕我市林业有害生物发生危害及防治工作实际，以控制重点区域主要有害生物为目标，有效保护湿地资源，巩固湿地恢复与治理成果，促进林业生态体系和林业产业体系又好又快发展。 一、项目概况 近年来，我市按照“保护黑河湿地，建设生态张掖”的总体思路，强势推进黑河流域湿地保护工程、市区园林绿化建设工程，使全市的湿地资源得到了有效的保护，市区园林绿化水平及档次不断得到提升。但是长期以来，由于以黄斑星天牛为主的杨树蛀干害虫在湿地自然保护区和市区园林绿地中呈蔓延之势，导致蚜虫、槐木虱、红蜘蛛、蚧壳虫等病虫害危害不断加剧，给自然保护区的资源管理和市区园林绿化苗木造成较大的经济损失，
严重危及我市生态环境安全。尽管我局采取了多项措施，组织技术人员开展了林业有害生物监测调查及防治工作，但由于病虫害发生面积大，防治资金严重短缺，使有害生物防治工作效果不明显。为加强自然保护区及市区园林绿地有害生物检疫监测、宣传培训及防治工作，防止林业有害生物扩散蔓延，确保黑河湿地资源生态系统安全和市区园林绿化建设成果， 二、指导思想 大力加强林业有害生物监测预警、检疫御灾、防治减灾、应急反应和防治法规体系建设，实现林业有害生物防治的标准化、规范化、科学化、法制化、信息化，大力加强重要有害生物重点治理，建立健全突发事件应急机制，大力推进无公害防治、预报实施专群结合、联防联治、标本兼治，确保危险性有害生物扩散蔓延趋势得到较大缓解，扭转林业有害生物严重发生的局面，使主要林业有害生物的发生范围和危害程序大幅度下降，促进森林健康成果。 项目实施内容 有害生物监测预警设，防治技术人员培训，检疫检验和除害处理 二、项目目标 林业有害生物成灾率控制在‰以下，总体防治率达到85%以上，确保全市生态安全。 三、主要发生的林业有害生物 全市主要林业有害生物发生种类14种，其中虫害7种，即松毛虫、天牛、木蠹蛾、杨舟蛾、刺蛾、叶甲、蚜虫。
横琴概况 ＼ 横琴简介 2009年8月14日，国务院正式批复《横琴总体发展规划》，横琴成为探索粤港澳紧密合作新模式的新载体。 2009年12月16日，横琴新区正式成立，实行比经济特区更加特殊的政策，横琴开发全面启动。目前岛内设一镇、三个社区居委会，下辖11条自然村，新区成立之初，岛内居住人口7000多人，其中常住人口4203人。 横琴新区以合作、创新和服务为主题，充分发挥横琴地处粤港澳结合部的优势，推进与港澳紧密合作、融合发展，逐步把横琴建设成为带动珠三角、服务港澳、率先发展的粤港澳紧密合作示范区。 横琴概况 ＼ 横琴优势 ?最独特的区位 ?最开放的口岸 ?最宽松的体制 ?最优惠的政策 ?最宜居的环境 中国内地惟一与香港、澳门陆桥相连的地方。 依托当前世界上发展最平稳快速的第二大经济体中国大陆。 背靠中国经济最发达的珠三角，面向南海。 是“一国两制”的交汇点，是中国走向世界、世界进入中国的门户。 横琴概况 ＼ 历史沿革 1992年横琴岛被广东定为扩大开放四个重点开发区之一。 2004年时任广东省委书记张德江同志提出将横琴岛创建为“泛珠三角横琴经济合作区”。 2008年12月，国务院通过《珠江三角洲地区改革发展规划纲要》，明确要“规划建设珠海横琴新区等合作区域，作为加强与港澳服务业、高新技术产业等方面合作的载体”。
2009年1月10日，时任中共中央政治局常委、国家副主席习近平在考察澳门期间宣布：中央政府已决定同意开发横琴岛，并将在开发过程中充分考虑澳门实现经济适度多元发展的需要。 2009年6月24日，国务院常务会议通过《横琴总体发展规划》，决定将横琴岛纳入珠海经济特区范围。 2009年8月14日，国务院正式批复了《横琴总体发展规划》，将横琴岛纳入珠海经济特区范围，实行更加开放的产业和信息化政策，立足促进粤港澳三地的紧密合作发展，促进港澳繁荣稳定。 2009年12月16日，继天津滨海新区和上海浦东新区之后，中国第三个国家级新区在珠海市横琴挂牌成立，同时该区投资总额逾726亿元人民币的首批四大工程也宣布启动，中共中央政治局委员、广东省委书记汪洋，中共广东省委副书记、广东省省长黄华华，为珠海市横琴新区的党政机构揭了牌。 2010年3月5日，国务院总理温家宝在十一届全国人大三次会议《政府工作报告》中指出，要积极推进港珠澳大桥等大型跨境基础设施建设和横琴岛开发，深化粤港澳合作，密切内地与港澳经济的联系。 2010年3月6日，广东省人民政府和澳门特别行政区政府在北京人民大会堂签署了《粤澳合作框架协议》，确立了合作开发横琴、产业协同发展等合作重点，提出了共建粤澳合作产业园区等一系列合作举措。 2011年3月11日，横琴开发纳入国家“十二五”规划。 2012年3月5日，国务院总理温家宝在《政府工作报告》明确提出：“支持澳门建设世界休闲旅游中心，推进横琴新区建设，促进经济适度多元发展”。 2012年10月30日，中共中央党史研究室编写的《党的十七大以来大事记》由新华社播发，横琴发展被列入2009年记事。 横琴概况 ＼ 大事记 2005年9月10日，中共中央政治局常委、国务院总理温家宝视察广东时第一站就踏上了与澳门最近处仅200米的横琴岛，并由衷赞道：“横琴岛真是一块宝地！”。 对于横琴岛开发问题，温总理做了最新要求和最高指示：横琴规划要和珠海整理规划相衔接，要谋而后动。
农作物病虫害绿色防控技术 实施绿色防控是贯彻"公共植保"和"绿色植保"理念的重大举措,是发展现代农业、建设"资源节约型"和"环境友好型"农业的有效途径.绿色防控的内涵是按照"绿色植保"的理念,以保护农作物,减少化学农药使用为目标,协调运用农业防治、物理防治、生物防治、生态调控等手段,科学、合理、安全使用农药,有效控制农作物病虫害,确保农作物生产安全、农产品质量安全和农业生态环境安全,促进农业增产、农民增收。 一、病虫害绿色防控的意义 农作物病虫害绿色防控，是指采取生态调控、生物防治、物理防治和科学用药等环境友好型措施控制农作物病虫危害的植物保护措施。推进绿色防控是贯彻“预防为主、综合防治”植保方针，实施绿色植保战略的重要举措。 （一）绿色防控是持续控制病虫灾害，保障农业生产安全的重要手段。目前我国防治农作物病虫害主要依赖化学防治措施，在控制病虫危害损失的同时，也带来了病虫抗药性上升和病虫暴发几率增加等问题。通过推广应用生态调控、生物防治、物理防治、科学用药等绿色防控技术，不仅有助于保护生物多样性，降低病虫害暴发几率，实现病虫害的可持续控制，而且有利于减轻病虫危害损失，保障粮食丰收和主要农产品的有效供给。 （二）绿色防控是促进标准化生产，提升农产品质量安全水平的必然要求。传统的农作物病虫害防治措施既不符合现代农业的发展要求，也不能满足农业标准化生产的需要。大规模推广农作物病虫害绿色防控技术，可以有效解决农作物标准化生产过程中的病虫害防治难题，显著降低化学农药的使用量，避免农产品中的农药残留超标，提升农产品质量安全水平，增加市场竞争力，促进农民增产增收。 （三）绿色防控是降低农药使用风险，保护生态环境的有效途径。病虫害绿色防控技术属于资源节约型和环境友好型技术，推广应用生物防治、物理防治等绿色防控技术，不仅能有效替代高毒、高残留农药的使用，还能降低生产过程中的病虫害防控作业风险，避免人畜中毒事故。同时，还显著减少农药及其废弃物造成的面源污染，有助于保护农业生态环境。 二、农作物病虫害绿色防控主推技术 （一）生态调控技术 重点采取推广抗病虫品种、优化作物布局、培育健康种苗、改善水肥管理等健康栽培措施，并结合农田生态工程、果园生草覆盖、作物间套种、天敌诱集带等生物多样性调控与自然天敌保护利用等技术，改造病虫害发生源头及孳生环境，人为增强自然控害能力和作物抗病虫能力。 （1）水稻田翻耕深水灭蛹，在完成田土翻耕和灌水（深度以淹没稻桩为度），持续时间3-4天，可杀死越冬螟虫60-70%。同时在翻耕犁耙时打捞浪渣销毁，可减轻纹枯病、菌核病等病虫杂草。 （2）果园结合冬季修剪，剪除树上病虫枝、病虫干僵果。彻底刮除主干、大枝上的老皮，带出园销毁。 （3）蔬菜田合理轮作，清除前茬作物的病枝烂叶及病虫残体，施足基肥。 （4）果树疏花疏果、果实套袋，控制病虫危害。 （5）科学肥水管理。搞好配方施肥，增施有机肥、磷钾肥，合理施用氮肥，科学管水，增强植株抗病虫能力。 （二）生物防治技术 重点推广应用以虫治虫、以螨治螨、以菌治虫、以菌治菌等生物防治关键措施，加大赤眼蜂、捕食螨、绿僵菌、白僵菌、微孢子虫、苏云金杆菌（BT）、蜡质芽孢杆菌、枯草芽孢杆菌、核型多
作物种类作物作物简介主要分布地区 谷类作物水稻 水稻可以分为籼稻和粳 稻、早稻和中晚稻、糯 稻和非糯稻。短日照作 物 我国水稻主产区主要是东北地区、长 江流域、珠江流域。 小麦 按播种季节可分为春小 麦和冬小麦。 春小麦：主要分布在中温带的东北平 原、河套平原、宁夏平原、新疆和青 藏高原等地。 冬小麦：主要分布在暖温带的黄淮海 平原地区，长江以南地区也有分布玉米 中国第一大粮食作物， 玉米是一种碳4植物， 雌雄同柱。 中国的玉米集中分布在从东北经华 北走向西南的斜长形地带内，其种植 面积约占全国总面积的85％。 豆类作物大豆大豆是喜温作物，夏季 宜有高温，适于我国北 方温带地区栽培。 中国大豆主产区有黑龙江、吉林、内 蒙古、辽宁、安徽、河南省等。 薯芋类作物马铃薯 马铃薯是中国五大主食 之一，其营养价值高、 适应力强、产量大，是 全球第三大重要的粮食 作物，仅次于小麦和玉 米。 中国马铃薯的主产区是西南山区、西 北、内蒙古和东北地区。 甘薯 日照甘薯属喜光的短日 照作物，茎叶利用光能 的时间长，效率高。喜 温，不耐寒 甘薯在中国分布很广，以淮海平原、 长江流域和东南沿海各省最多。 油料作物 油菜 油菜是我国最重要的油 料作物 长江流域是我国油菜的主产区，近年 有“北移南迁”趋向，如黄淮海平 原、辽、黑及华南地区 向日葵 向日葵为世界四大油料 作物之一，主产区北纬 35°至55°之间。 我国向日葵主产区分布在东北、西北 和华北地区，如内蒙古、吉林、辽宁、 黑龙江、山西等省、自治区。 糖料作物甘蔗 甘蔗生长期长，需水肥 量大，喜高温 我国甘蔗种植面积十分有限，主要分 布在台湾、广东、广西、福建、四川、 云南六省。 甜菜 甜菜生长期短，耐盐碱 干旱，喜温凉 我国甜菜主要分布在东北、华北、西 北三个产区,其中东北种植最多。 棉花主要纤维作物 我国棉花生产集中分布在黄河、长江 中下游地区。在我国各棉区中，新疆 光热条件是最适宜种植棉花的，棉花
《横琴总体发展规划》全文 目录 前言 1 第一章横琴开发的重要意义 2 第二章总体要求和发展目标 4 第一节指导思想 4 第二节发展定位 4 第三节目标和任务 5 第三章空间布局7 第一节功能分区7 第二节开发强度9 第四章产业发展12 第一节产业发展目标12 第二节产业发展重点12 第三节产业发展策略17 第五章基础设施19 第一节交通19 第二节能源20 第三节供排水21 第四节信息21 第五节综合防灾22 第六节基础设施发展策略22 第六章公共服务设施24 第一节公共服务设施体系24 第二节公共服务设施发展策略25 第七章生态环境26 第一节生态空间结构26 第二节环境功能区划30
第三节生态环境建设策略28 第八章开发管理模式29 第一节开发模式29 第二节管理模式29 第九章保障措施31 第一节政策支持31 第二节组织实施33 第十章近期建设行动计划35 第一节近期建设规模35 第二节近期建设重点35 前言 市横琴岛地处珠江口西岸，与澳门隔河相望，是珠江口一个环境优美、生态良好的海岛。经过多年建设，基本实现了“六通”（桥通、路通、水通、电通、邮通、口岸通），建成了横琴大桥、莲花大桥，设立了国家一类口岸，修建了贯通全岛的环岛公路，接通了供水管道，建成了11万伏变电站，为下一步发展奠定了良好的基础。 充分发挥横琴毗邻港澳的区位优势，抓住《珠江三角洲地区改革发展规划纲要（2008—2020）》赋予珠三角“科学发展、先行先试”的重大机遇，推进与港澳紧密合作发展，对于促进珠江口西岸地区经济发展形成新的增长极，促进澳门经济适度多元发展和港澳地区长期繁荣稳定都具有重要的战略意义。 为加快横琴开发建设，发挥带动珠三角、服务港澳、率先发展的作用，特制定本规划。规划围为横琴岛，土地总面积106.46平方公里，规划期至2020年。 第一章横琴开发的重要意义
我国主要农作物产地分布全景图 我国农作物的分布情况： 1、影响条件 自然条件：地形、土壤、水源、气候 社会经济：市场、交通、科技、政策 2、耕作制度 即某一地区一年种几次庄稼。 3、主要耕作方式 4、农业特色 5、主要农作物分布 玉米在我国分布很广，主要集中在东北、华北和西南地区，大致形成一个从东北到西南的斜长形玉米栽培带。种植面积最大的省份是山东、吉林、河北、黑龙江、辽宁、河南、四川七省。 6、主要油料作物油菜主要分布在长江流域，但近年来出现'南迁北移'的趋势。 花生主要分布在我国东部的暖温带、亚热带、热带的沙土和丘陵地区。 大豆 （1）东北三省为主的春大豆区 （2）黄淮流域的夏大豆区 （3）长江流域的春、夏大豆区 （4）江南各省南部的秋作大豆区 （5）两广、云南南部的大豆多熟区 其中，东北春播大豆和黄淮海夏播大豆是我国大豆种植面积最大、产量最高的两个地区。
7、糖料作物甘蔗生长习性：喜高温，需水肥量大，生长期长 甜菜生长习性：喜温凉，耐盐碱、干旱，生长期短 附：中国农产品出口贸易基地、中国主要牧区及其优良牲畜品种 8、森林资源我国的森林资源主要分布在： （1）东北的大小兴安岭和长白山地，是我国最大的天然林区 （2）西南横断山区是我国第二大天然林区 （3）东南部的台湾、福建、江西等省山区，以人工林、次生林为主 除森林外，还有多种多样的经济林产品：温带苹果：主要产于山东、河南、河北、陕西、新疆、辽宁等亚热带柑桔：主要产于浙江、福建、江西、四川等省亚热带茶叶：产于南方各省山区热带水果：福建、广东、广西、海南、云南出产香蕉、荔枝、龙眼、菠萝等水果。海南还出产椰子。天然橡胶：云南南部、海南岛、雷州半岛
地理知识：主要农作物的生长习性及分布地区 地理知识：主要农作物的生长习性及分布地区 名称主要分布区生长习性农业区位的选择 粮食作物水 稻 亚洲的粮食——水稻；水稻播种面积最大的国家——印度；最 大生产国——中国；最大出口国——泰国。主要分布于东亚、 东南亚和南亚的季风区，东南亚的热带雨林区，以及埃及、尼 日利亚、西班牙、意大利、美国、古巴、委内瑞拉、巴西 喜高温多雨、地 势低平 水分条件较好的平原地区；在我国 的集中分布于东部季风区、秦岭-- 淮河以南（34°N） 玉 米 单产最高——玉米；玉米最大的生产国和出口国——美国；我 国的主产地在吉林省 喜夏季高温多 雨，生长期较长 夏季高温多雨、生长季较长的地区小麦 播种面积和产量最大的粮食作物；小麦最大生产国——中国； 最大出口国——美国 水热要求不高； 耐寒耐旱、适应 性强 温带大陆性气候，我国冬小麦和春 小的分界线为古长城（或3500℃积 温线），冬小麦一般秋播夏收，春 小麦一般春播夏收 糖料作物甘 蔗 拉丁美洲、巴西、古巴、亚洲，我国台湾、广东、广西、福建、 四川、云南等省区 喜高温，需水肥 量大，生长期长 气候湿润的亚热带丘陵地区(一般 分布在长江以南,但成都平原例 外) 甜 菜 亚欧大陆、北美的中温带地区，我国的黑龙江、吉林、内蒙古、 新疆 喜温凉，耐盐碱、 干旱，生长期短 气候温凉的中温带地区 饮料 茶叶亚洲的印度、中国、斯里兰卡 喜高温多雨、怕 涝 红壤等酸性土壤 咖啡 咖啡原产于非洲的埃塞俄比亚，现分布于拉丁美洲的巴西等， 埃塞俄比亚等 喜高温多雨 高温多雨、无霜害、年均温在20° —22℃左右，年降水量在1000— 1800毫米，荫蔽较好，湿度较大的 山地生长 可可 可可树原产于南美洲亚马逊盆地，现以非洲为主，拉丁美洲亦 生产 较高的温度、湿 度和雨量分配均 匀 要有较高的树木在它上层作荫蔽 保护，本身抗风力弱。所以在热带 雨林带海拔较低处，可可树生长茂 密。 纤维剑麻 剑麻原产于墨西哥，从西沙尔港出口，因而也叫“西沙尔麻”。 现非洲的坦桑尼亚占世界总产量的60％。 喜高温耐旱 黄麻 原产于东南亚，现主要产在南亚地区。孟加拉国黄麻的产量居 世界第一，其次是印度。黄麻在我国南方种植很广，浙江省最 多 喜高温湿润气候 和肥沃的沙质土 壤 全年降水量在1000毫米以上亚麻温带地区的中国、俄罗斯等 喜凉爽湿润气候 和肥沃壤土 长日照作物 橡胶东南亚和南亚地区喜高温多雨
横琴新区控制性详细规划（内容简介） 一、规划依据——法律法规： 1．《中华人民共和国城乡规划法》 2．《中华人民共和国土地管理法》 3．《中华人民共和国环境保护法》 4．《城市规划编制办法》 5．《城市用地分类与规划建设用地标准（GBJ 137-90）》6．《广东省城市控制性详细规划管理条例》 7．《广东省城市控制性详细规划指引》 8．《珠海市城市规划条例》 9．《珠海市土地管理条例》 10．《珠海市环境保护条例》 11．《珠海市城市规划技术标准与准则》 12．其它相关的法律法规、规范和技术管理规定——上层次规划： 1．《珠江三角洲城镇群协调发展规划（2004-2020）》2．《珠江三角洲地区改革发展规划纲要（2008-2020）》3．《珠海市城市总体规划（2001-2020）》 4．《珠海城市空间发展战略研究（珠海2030）》 5．《珠海市重大交通基础设施集疏运网络规划》 6．《横琴总体发展规划》 7．《横琴新区城市总体规划（2009-2020）》
二、规划范围 东隔十字门水道与澳门相邻，南濒南海，西临磨刀门水道，北与珠海南湾城区隔马骝洲水道相望，规划总面积106.46平方公里。 三、发展定位与发展目标 1．发展定位：“一国两制”下探索粤港澳合作新模式的示范区。深化改革开放和科技创新的先行区。促进珠江口西岸地区产业升级发展的新平台。 2．发展目标：经过10到15年的努力，把横琴建设成为连通港澳、区域共建的“开放岛”，经济繁荣、宜居宜业的“活力岛”，知识密集、信息发达的“智能岛”，资源节约、环境友好的“生态岛”。 四、发展规模与空间结构 1．发展规模：规划2015年人口规模为12万人左右，建设用地规模控制在12平方公里；2020年人口规模为28万人左右，建设用地规模控制在28平方公里，人均建设用地100平方米。2．空间结构：规划形成各具特色、紧凑发展、紧密关联的“三片、十区”的功能布局。“三片”为商务服务片、科教研发片、休闲旅游片。“十区”为商务服务片中的口岸服务区、中心商务区和国际居住社区；科教研发片中的教学区、综合服务
农业部办公厅关于推进农作物病虫害绿色防控的意见 农办农[2011]54号 各省、自治区、直辖市农业（农牧、农村经济）厅（委、局），新疆生产建设兵团农业局: 为贯彻落实国务院食品安全工作会议的有关精神，根据农业部2011年农产品质量安全整治工作统一部署，强化“公共植保、绿色植保”理念，转变植保防灾方式，大力推进农作物病虫害绿色防控，保障农业生产安全、农产品质量安全以及生态环境安全，特提出如下意见： 一、推进农作物病虫害绿色防控对保障农业生产安全和农产品质量安全意义重大 农作物病虫害绿色防控，是指采取生态调控、生物防治、物理防治和科学用药等环境友好型措施控制农作物病虫危害的植物保护措施。推进绿色防控是贯彻“预防为主、综合防治”植保方针，实施绿色植保战略的重要举措。 （一）绿色防控是持续控制病虫灾害，保障农业生产安全的重要手段。目前我国防治农作物病虫害主要依赖化学防治措施，在控制病虫危害损失的同时，也带来了病虫抗药性上升和病虫暴发几率增加等问题。通过推广应用生态调控、生物防治、物理防治、科学用药等绿色防控技术，不仅有助于保护生物多样性，降低病虫害暴发几率，实现病虫害的可持续控制，而且有利于减轻病虫危害损失，保障粮食丰收和主要农产品的有效供给。 （二）绿色防控是促进标准化生产，提升农产品质量安全水平的必然要求。传统的农作物病虫害防治措施既不符合现代农业的发展要求，也不能满足农业标准化生产的需要。大规模推广农作物病虫害绿色防控技术，可以有效解决农作物标准化生产过程中的病虫害防治难题，显著降低化学农药的使用量，避免农产品中的农药残留超标，提升农产品质量安全水平，增加市场竞争力，促进农民增产增收。 （三）绿色防控是降低农药使用风险，保护生态环境的有效途径。病虫害绿色防控技术属于资源节约型和环境友好型技术，推广应用生物防治、物理防治等绿色防控技术，不仅能有效替代高毒、高残留农药的使用，还能降低生产过程中的病虫害防控作业风险，避免人畜中毒事故。同时，还显著减少农药及其废弃物造成的面源污染，有助于保护农业生态环境。 二、推进农作物病虫害绿色防控的指导思想、主要原则和目标任务 （一）指导思想 坚持以科学发展观为指导，贯彻“预防为主、综合防治”植保方针和“公共植保、绿色植保”的植保理念，分区域、分作物优化集成农作物病虫害绿色防控配套技术。通过加大政策扶持和宣传发动等措施，大力示范推广绿色防控关键技术，为农业生产安全、农产品质量安全及生态环境安全提供支撑作用。 （二）主要原则 政策扶持。整合资源，多渠道争取各级财政和相关项目扶持，积极探索病虫害绿色防控补贴机制，促进绿色防控技术推广应用。