福建国有林场森林防火工程体系建设成效

福建是我国重点林区,也是森林火灾的重灾区,福建国有林场系统重视森林防火工程体系建设,强化林火阻隔网络、林火预报与监控,林火交通通讯,组建快速扑火队伍等“四网二化”建设,取得显著成效。     

Semi-automatic reduction and upscaling of large models: A farm management example

Research questions at the regional, national and global scales frequently require the upscaling of existing models. At large scales, simple model aggregation may have a prohibitive computational cost and lead to over-detailed problem representation. Methods that guide model simplification and revision have the potential to support the choice of the appropriate level of detail or heterogeneity within upscaled models. Efficient upscaling will retain only the heterogeneity that contributes to accurate aggregated results. This approach to model revision is challenging, because automatic generation of alternative models is difficult and the set of possible revised models is very large. In the case where simplification alone is considered, there are at least n²−1 possible simplified models where n is the number of model variables. Even with the availability of High Performance Computing, it is not possible to evaluate every possible simplified model if the number of model variables is greater than roughly 35. To address these issues, we propose a method that extends an existing procedure for simplifying and aggregating mechanistic models based on replacing model variables with constants. The method generates simplified models by selectively aggregating existing model variables, retaining existing model structure while reducing the size of the set of possible models and ordering them into a search tree. The tree is then searched selectively. We illustrate the method using a catchment scale optimization model with c. 50,000 variables (Farm-adapt) in the context of adaptation to climatic change. The method was successful in identifying redundant model variables and an adequate model 10% smaller than the original model. We discuss how the procedure can be extended to other large models and compare the method to those proposed by others. We conclude by urging model developers to regard their models as a starting point and to consider the need for alternative models during model development.     

Adoption of renewable soil fertility replenishment technologies in the southern African region: Lessons learnt and the way forward

Low soil fertility is one of the most important biophysical constraints to increasing agricultural productivity in sub‐Saharan Africa. Several renewable soil fertility replenishment (RSFR) technologies that are based on nutrient re‐cycling principles have been developed in southern Africa. Some success stories have been recorded (e.g. nitrogen‐fixing legumes), but the adoption of RSFR technologies has generally lagged behind scientific advances thereby reducing the potential impacts of the technologies. This paper describes the major RSFR technologies being promoted in the region, synthesizes available information regarding their adoption by farmers, and identifies the challenges, key lessons learnt and the way forward for up‐scaling RSFR technologies in the region. The review indicated that farmer uptake of RSFR technologies depends on several factors that can be grouped into broad categories: technology‐specific (e.g. soil type, management regime), household‐specific (e.g. farmer perceptions, resource endowment, household size), policy and institutions context within which RSFR is disseminated (inputs and output prices, land tenure and property rights), and geo‐spatial (performance of species across different bio‐physical conditions, location of village). Adoption of RSFR technologies can be enhanced by targeting them to their biophysical and social niches, facilitating appropriate policy and institutional contexts for dissemination, understanding the broader context and dynamics of the adoption process, a paradigm shift in the approach to the dissemination of RSFR (e.g. expanding RSFR to high value crop systems, exploring synergy with inorganic fertilizer) and, targeted incentive systems that encourage farmers to take cognizance of natural resource implications when making agricultural production decisions.     

风电场建设对周边生态环境的影响及其恢复措施

风电场在其建设及运行期间,会对周边的生态环境造成一定影响。文章具体以喀左县双庙风电场建设为例,探讨风电场的建设期和运营期对周边生态环境产生的影响,同时提出了风电场生态环境恢复的具体方法和措施。为风电场的生态环境保护提供借鉴。     

杭州市农贸市场的噪声特点分析

对农贸市场噪声进行了监测分析。结果发现,农贸市场营业时,市场内的噪声超标,其最小值出现在中午,最大值出现在下午下班高峰期;农贸市场一天内不同时段噪声污染级的数值大小有一定的差距;用噪声冲击指数来评定农贸市场声环境质量,其声环境质量极差。     

Probabilistic Modelling of Cattle Farm Distribution in Australia

A probabilistic Bayesian method called weights of evidence (WofE) was used to develop a synthetic dataset of cattle farm locations at a national scale across Australia. The synthetic dataset was required for the modelling of livestock movements with a view to assessing biosecurity implications. The WofE method is based on the analysis of spatial relationships between evidential patterns with respect to an event, such as the actual location of a farm. The evidential patterns of cattle farms were derived from maps of land use, land tenure, drainage systems, roads, settlements and long-term averaged rainfall. These evidential patterns were used for delineating and ranking land areas suitable for cattle farming. For each evidential pattern statistics such as a positive weight, a negative weight and a contrast were calculated for estimating the degree of correlation between the evidential patterns and known farm locations. The integrated evidential patterns of known farms were then used for estimating posterior probabilities and splitting land into five different classes according to its suitability for farming.

Sensitivity analysis for models of greenhouse gas emissions at farm level. Case study of N(2)O emissions simulated by the CERES-EGC model

Modelling complex systems such as farms often requires quantification of a large number of input factors. Sensitivity analyses are useful to reduce the number of input factors that are required to be measured or estimated accurately. Three methods of sensitivity analysis (the Morris method, the rank regression and correlation method and the Extended Fourier Amplitude Sensitivity Test method) were compared in the case of the CERES-EGC model applied to crops of a dairy farm. The qualitative Morris method provided a screening of the input factors. The two other quantitative methods were used to investigate more thoroughly the effects of input factors on output variables. Despite differences in terms of concepts and assumptions, the three methods provided similar results. Among the 44 factors under study, N₂O emissions were mainly sensitive to the fraction of N₂O emitted during denitrification, the maximum rate of nitrification, the soil bulk density and the cropland area.     

Children's behavioral traits and risk of injury: Analyses from a case-control study of agricultural households

Problem

Children on family agricultural operations have high risk of injury. The association between children's behavioral traits and their risk of injury is not well understood.

Method

Data from the Regional Rural Injury Study-II were used to assess behavioral risk factors for injury to children ages six to < 20 years. A total of 379 injury events (cases) and 1,562 randomly selected controls were identified. Adjusted odds ratios (OR) and 95% confidence intervals (CI), calculated using logistic regression, were used to estimate injury risk in reference to behavioral traits.

Results

Injury risks were greater for children with high levels of depressive symptoms (OR = 1.9, CI = 1.0-3.7) and aggression (OR = 1.6, CI = 0.9-2.7), and low levels of careful/cautious behavior (OR = 1.8, CI = 1.1-2.9). Children with low levels of self-regulation had reduced risks (OR = 0.4, CI = 0.2-0.8).

Discussion

Results suggest that children's behaviors affect their risk of agricultural injury. Additional research could elucidate mechanisms and inform interventions.

Impact on industry

The development of multifaceted, sustainable approaches for prevention is necessary for this unique population. These findings suggest a need for interventions that incorporate specific behavior-related risk factors in the context of family farms and ranches.     

Conversion of specialised dairy farming systems into sustainable mixed farming systems in Cuba

From the 1960s onwards, a ‘High External Input’ dairy production model was applied widely in Cuba. Overall milk production of the national herd increased considerably, but the system was inefficient from both a financial and energetic point of view. In the early 1990s, after the abrupt end of inflow of capital and other resources from Eastern Europe, the dairy sector collapsed. In the short term, the modern infrastructure of milk production deteriorated and the sector experienced profound vulnerability. However, in the longer term, this situation stimulated a search for more sustainable approaches, such as low external input Mixed Farming Systems (MFS). The current study aimed to evaluate two small scale prototype farms to assess the implications of converting ‘Low External Input’ Dairy Farming Systems into MFS. Fifteen agro-ecological and financial indicators were selected and monitored over a 6-year period. Two configurations of MFS, i.e. the proportion of the farm area occupied by arable crops, were tested: 25 and 50%. Productivity, energy efficiency and cost-effectiveness all improved following conversion. Total energy input was low for both farms and decreased over time, whereas energy efficiency was high and increased over time. Human labour input was high directly following conversion, but decreased by one-third over the 6-year period. This study demonstrates, at an experimental scale, the potential of MFS to achieve ecological, productivity and financial advantages for dairy production in Cuba. Readers should send their comments on this paper to: BhaskarNath@aol.com within 3 months of publication of this issue.     

Cumulative impact of GM herbicide-tolerant cropping on arable plants assessed through species-based and functional taxonomies

Background, aim and scope

In a gradualist approach to the introduction of crop biotechnology, the findings of experimentation at one scale are used to predict the outcome of moving to a higher scale of deployment. Movement through scales had occurred for certain genetically modified herbicide-tolerant (GMHT) crops in the UK as far as large-scale field trials. However, the land area occupied by these trials was still <1% of the area occupied by the respective non-GM crops. Some means is needed to predict the direction and size of the effect of increasing the area of GMHT cropping on ecological variables such as the diversity among species and trophic interactions. Species-accumulation curves are examined here as a method of indicating regional-scale impacts on botanical diversity from multiple field experiments.

Materials and methods

Data were used from experiments on the effect of (GMHT) crops and non-GM, or conventional, comparators in fields sown with four crop types (beet, maize, spring and winter oilseed rape) at a total of 250 sites in the UK between 2000 and 2003. Indices of biodiversity were measured in a split-field design comparing GMHT with the farmers’ usual weed management. In the original analyses based on the means at site level, effects were detected on the mass of weeds in the three spring crops and the proportion of broadleaf and grass weeds in winter oilseed rape, but not on indices of plant species diversity. To explore the links between site means and total taxa, accumulation curves were constructed based on the number of plant species (a pool of around 250 species in total) and the number of plant functional types (24), inferred from the general life-history characteristics of a species.

Results

Species accumulation differed between GMHT and conventional treatments in direction and size, depending on the type of crop and its conventional management. Differences were mostly in the asymptote of the curve, indicative of the maximum number of species found in a treatment, rather than the steepness of the curve. In winter oilseed rape, 8% more species were accumulated in the GMHT treatment, mainly as a result of the encouragement of grass species by the herbicide when applied in the autumn. (Overall, GMHT winter oilseed rape had strong negative effects on both the food web and the potential weed burden by increasing the biomass of grasses and decreasing that of broadleaf weeds.) In maize, 33% more species—a substantial increase—were accumulated in the GMHT than in the conventional, consistent with the latter’s highly suppressive weed management using triazine herbicides. In the spring oilseed rape and beet, fewer species (around 10%) were accumulated in the GMHT than the conventional. The GMHT treatments did not remove or add any functional (life history) types, however. Differences in species accumulation between treatments appeared to be caused by loss or gain of rarer species. The generality of this effect was confirmed by simulations of species accumulation in which the species complement at each of 50 sites was drawn from a regional pool and subjected to reducing treatment at each site. Shifts in the species-accumulation parameters, comparable to those measured, occurred only when a treatment removed the rarer species at each site.

Discussion

Species accumulation provided a set of simple curve-parameters that captured the net result of numerous local effects of treatments on plant species and, in some instances, the balance between grass and broadleaf types. The direction of effect was not the same in the four crops and depended on the severity of the conventional treatment and on complex interactions between season, herbicide and crop. The accumulation curves gave an indication of potential positive or negative consequences for regional species pools of replacing a conventional practice with GMHT weed management. In this and related studies, a range of indicators, through which diversity was assessed by both species and functional type, and at both site and regional scales, gave more insight into effects of GMHT treatment than provided by any one indicator.

Conclusions

Species accumulation was shown to discriminate at the regional scale between agronomic treatments that had little effect on species number at the field scale. While a comprehensive assessment of GM cropping needs to include an examination of regional effects, as here, the costs of doing this in all instances would be prohibitive. Simulations of diversity-reducing treatments could provide a theoretical framework for predicting the likely regional effects from in-field plant dynamics.

Recommendations and perspectives

Accumulation curves potentially offer a means of linking within-site effects to regional impacts on biodiversity resulting from any change in agricultural practice. To guide empirical measurement, there is a scope to apply a methodology such as individual-based modelling at the field scale to explore the links between agronomic treatments and the relative abundance of plant types. The framework needs to be validated in practice, using species-based and functional taxonomies, the latter defined by measured rather than inferred traits.