Food Versus Biofuels: Environmental and Economic Costs

The rapidly growing world population and rising consumption of biofuels intensify demands for both food and biofuels. This exaggerates food and fuel shortages. The use of food crops such as corn grain to produce ethanol raises major nutritional and ethical concerns. Nearly 60% of humans in the world are currently malnourished, so the need for grains and other basic foods is critical. Growing crops for fuel squanders land, water and energy resources vital for the production of food for human consumption. Using corn for ethanol increases the price of US beef, chicken, pork, eggs, breads, cereals, and milk more than 10% to 30%. In addition, Jacques Diouf, Director General of the UN Food and Agriculture Organization, reports that using food grains to produce biofuels is already causing food shortages for the poor of the world. Growing crops for biofuel not only ignores the need to reduce fossil energy and land use, but exacerbates the problem of malnourishment worldwide.     

Cement Manufacture and the Environment Part II: Environmental Challenges and Opportunities

Construction materials account for a significant proportion of nonfuel materials flows throughout the industrialized world. Hydraulic (chiefly portland) cement, the binding agent in concrete and most mortars, is an important construction material. Portland cement is made primarily from finely ground clinker, a manufactured intermediate product that is composed predominantly of hydraulically active calcium silicate minerals formed through high-temperature burning of limestone and other materials in a kiln. This process typically requires approximately 3 to 6 million Btu (3.2 to 6.3 GJ) of energy and 1.7 tons of raw materials (chiefly limestone) per ton (t) of clinker produced and is accompanied by significant emissions of, in particular, carbon dioxide (CO₂), but also nitrogen oxides, sulfur oxides, and particulates. The overall level of CO₂ output, about 1 ton/ton clinker, is almost equally contributed by the calcination of limestone and the combustion of fuels and makes the cement industry one of the top two manufacturing industry sources of this greenhouse gas. The enormous demand for cement and the large energy and raw material requirements of its manufacture allow the cement industry to consume a wide variety of waste raw materials and fuels and provide the industry with significant opportunities to symbiotically utilize large quantities of by-products of other industries.
This article, the second in a two-part series, summarizes some of the environmental challenges and opportunities facing the cement manufacturing industry. In the companion article, the chemistry, technology, raw materials, and energy requirements of cement manufacture were summarized. Because of the size and scope of the U.S. cement industry, the article relies primarily on data and practices from the United States.     

四川宜宾市野生动植物资源产业现状及经济效益分析

近年来,国家越来越重视野生动植物保护与利用,对野生动植物的利用也逐渐产业化.通过收集资料和实地走访的方式,从野生动植物驯养繁殖、生产加工、观赏三方面,透析四川省宜宾市野生动植物各产业经营活动企业的经营状况、经营规模以及物种种类、数量、地理分布等现状,并通过主成分分析(PCA)对经济效益进行分析,结果显示:(1)投资规模是影响产业化企业经济效益的第一主成分,其中投入总资金对经济效益的影响程度最高,达0.594;(2)第二主成分主要反映企业的养殖规模,以产业化企业的养殖种类对经济效益的影响程度最大,为0.708;(3)产业化经营企业经济效益的综合评分结果显示,经济效益回报最高的企业分值为3.330,综合反映了投入资金与投资规模之间的综合作用关系.     

CRISPR/Cas9基因组编辑技术在农业动物中的应用

CRISPR(Clustered regularly interspaced short palindromic repeats)/Cas(CRISPR associated proteins)是在细菌和古细菌中发现的一种用来抵御病毒或质粒入侵的获得性免疫系统.目前已发现的CRISPR/Cas系统包括Ⅰ,Ⅱ和Ⅲ型,其中Ⅱ型系统的组成较简单,由其改造成的CRISPR/Cas9技术已成为一种高效的基因组编辑工具.自2013年CRISPR/Cas9技术成功用于哺乳动物基因组定点编辑以来,应用该技术进行基因组编辑的报道呈现出爆发式的增长.农业动物不仅是重要的经济动物,也是人类疾病和生物医药研究的重要模式动物.本文综述了CRISPR/Cas9技术在农业动物中的研究和应用进展,简述了该技术的脱靶效应及减少脱靶的主要方法,并展望了该技术的应用前景.     

3种城市生境间蚂蚁群落组成的比较研究——以四川省南充市城区为例

作为重要的人为影响因素,城市化改变了自然生境的景观结构,并影响到动物群落的组成。蚁科Formicidae动物种类繁多,且易于捕获,是研究城市中不同土地利用类型对动物群落影响的理想指示生物。2018年5月,在四川省南充市城区中选择"公园与绿地""旱地"和"乔木林地"3种土地利用类型作为不同的生境类型,并通过陷阱法对蚂蚁群落组成进行调查。共采集到1 847只个体;其中,津岛铺道蚁Tetramorium tsushimae在"公园与绿地"中的个体数占比最高(73.80%),并明显高于"旱地"群落中占比最高的中华小家蚁Monomorium chinense(25.48%)和"乔木林地"群落中占比最高的宽结大头蚁Pheidole nodus(28.79%)。3种生境间,群落的蚂蚁个体数和物种丰富度间差异无统计学意义,但"公园与绿地"的Simpson多样性指数和Pielou均匀度指数均显著低于"旱地"和"乔木林地"的;"公园与绿地"(q=0.444)和"旱地"(q=0.500)与"乔木林地"间的群落组成均存在中度不相似,而"公园与绿地"与"旱地"间为中度相似(q=0.647)。因此,不同的城市土地...     

Role of self-sufficiency,productivity and diversification on the economic sustainability of farming systems with autochthonous sheep breeds in less favoured areas in Southern Europe

Traditional mixed livestock cereal- and pasture-based sheep farming systems in Europe are threatened by intensification and specialisation processes. However, the intensification process does not always yield improved economic results or efficiency. This study involved a group of farmers that raised an autochthonous sheep breed (Ojinegra de Teruel) in an unfavourable area of North-East Spain. This study aimed to typify the farms and elucidate the existing links between economic performance and certain sustainability indicators (i.e. productivity, self-sufficiency and diversification). Information was obtained through direct interviews with 30 farms (73% of the farmers belonging to the breeders association). Interviews were conducted in 2009 and involved 32 indicators regarding farm structure, management and economic performance. With a principal component analysis, three factors were obtained explaining 77.9% of the original variance. This factors were named as inputs/self-sufficiency, which included the use of on-farm feeds, the amount of variable costs per ewe and economic performance; productivity, which included lamb productivity and economic autonomy; and productive orientation, which included the degree of specialisation in production. A cluster analysis identified the following four groups of farms: high-input intensive system; low-input self-sufficient system; specialised livestock system; and diversified crops-livestock system. In conclusion, despite the large variability between and within groups, the following factors that explain the economic profitability of farms were identified: (i) high feed self-sufficiency and low variable costs enhance the economic performance (per labour unit) of the farms; (ii) animal productivity reduces subsidy dependence, but does not necessarily imply better economic performance; and (iii) diversity of production enhances farm flexibility, but is not related to economic performance.     

Scaling up farmer‐managed natural regeneration in Africa to restore degraded landscapes

We present examples of large‐scale farmer‐managed natural regeneration of woody species in Africa to create new agroforestry systems. We also analyze two cases in Ethiopia of large‐scale natural regeneration. The available evidence shows that the average costs per hectare of promoting on‐farm natural regeneration are low as soon as farmers are familiar with this practice and begin to spontaneously adopt it. Based on these cases, current ambitious forest restoration targets can be achieved, but this goal requires a shift from tree planting to assisted natural regeneration. We propose a scaling‐up strategy for natural regeneration based on experience gained mainly in the West African Sahel.     

An approach to holistically assess (dairy) farm eco-efficiency by combining Life Cycle Analysis with Data Envelopment Analysis models and methodologies

Eco-efficiency is a useful guide to dairy farm sustainability analysis aimed at increasing output (physical or value added) and minimizing environmental impacts (EIs). Widely used partial eco-efficiency ratios (EIs per some functional unit, e.g. kg milk) can be problematic because (i) substitution possibilities between EIs are ignored, (ii) multiple ratios can complicate decision making and (iii) EIs are not usually associated with just the functional unit in the ratio’s denominator. The objective of this study was to demonstrate a ‘global’ eco-efficiency modelling framework dealing with issues (i) to (iii) by combining Life Cycle Analysis (LCA) data and the multiple-input, multiple-output production efficiency method Data Envelopment Analysis (DEA). With DEA each dairy farm’s outputs and LCA-derived EIs are aggregated into a single, relative, bounded, dimensionless eco-efficiency score, thus overcoming issues (i) to (iii). A novelty of this study is that a model providing a number of additional desirable properties was employed, known as the Range Adjusted Measure (RAM) of inefficiency. These properties altogether make RAM advantageous over other DEA models and are as follows. First, RAM is able to simultaneously minimize EIs and maximize outputs. Second, it indicates which EIs and/or outputs contribute the most to a farm’s eco-inefficiency. Third it can be used to rank farms in terms of eco-efficiency scores. Thus, non-parametric rank tests can be employed to test for significant differences in terms of eco-efficiency score ranks between different farm groups. An additional DEA methodology was employed to ‘correct’ the farms’ eco-efficiency scores for inefficiencies attributed to managerial factors. By removing managerial inefficiencies it was possible to detect differences in eco-efficiency between farms solely attributed to uncontrollable factors such as region. Such analysis is lacking in previous dairy studies combining LCA with DEA. RAM and the ‘corrective’ methodology were demonstrated with LCA data from French specialized dairy farms grouped by region (West France, Continental France) and feeding strategy (regardless of region). Mean eco-efficiency score ranks were significantly higher for farms with <10% and 10% to 30% maize than farms with >30% maize in the total forage area before correcting for managerial inefficiencies. Mean eco-efficiency score ranks were higher for West than Continental farms, but significantly higher only after correcting for managerial inefficiencies. These results helped identify the eco-efficiency potential of each region and feeding strategy and could therefore aid advisors and policy makers at farm or region/sector level. The proposed framework helped better measure and understand (dairy) farm eco-efficiency, both within and between different farm groups.