Traditional agriculture in transition: examining the impacts of agricultural modernization on smallholder farming in Ghana under the new Green Revolution

Following the renewed effort at achieving a new green revolution for Africa, emphasis has been placed on modernizing smallholder agriculture through the deployment of improved inputs especially mechanized technologies. In Ghana, the government has in the last decade emphasized the provision of subsidized mechanized ploughing services to farmers alongside a rapidly growing private sector tractor service market. While mechanized technology adoption rates have increased rapidly, the deployment of these technologies has been without critical analysis of the impacts on production patterns and local agrarian systems. This paper examines the distributional impacts of agriculture mechanization on cropping patterns and farm sizes of smallholder farmers in northern Ghana using Geographic Information Systems (GIS) techniques, and semi-structured interviews with smallholder farmers (n=60). Specifically, comparative analysis of the field sizes and cropping patterns of participant farmers prior to and after the adoption of mechanized technologies was conducted. In-depth interviews were used to contextualize the experiences of smallholder farmers toward understanding how mechanization may be impacting traditional agriculture. Our findings reveal a mechanization paradox in which farm sizes are expanding, while cropping patterns are shifting away from traditional staple crops (pearl millet and sorghum bicolor) to market-oriented crops (maize, rice and groundnuts). This transition we argue, has adverse implications on the cultural dimension of food security, the organization of social life, and climate change adaptation. We recommend a retooling of the current agricultural policy focus to ensure context sensitivity for a more robust battle against food insecurity.     

我国人口资源与食物问题

人口增多,耕地减少,部分地区供水不足,是我国人口与资源矛盾的基本格局。由于人口的压力,对资源实行超强度的利用,使较大范围的地区生态环境恶化,严重威胁农业生产,并成为许多地区多灾、低产、贫困的根本原因,较低的食物人均占有水平,将继续成为中国国民经济发展和人民生活水平提高的严重限制因素。增加食物,必须挖掘资源的潜力,开源与节流相结合,以内涵挖潜为主。首先是要立足现有的耕地,致力于提高单产;同时,合理开发水域、山地、草地等资源,广辟食物来源,提高非耕地资源的生产力,提高林牧渔业的发展水平。耕地应以深度开发为主,走资源节约型(节地、节水、节时、节能)的集约化道路。     

Particulate matter exchange between atmosphere and roads surfaces in urban areas

The deposition and the re-suspension of particulate matter (PM) in urban areas are the key processes that contribute not only to stormwater pollution, but also to air pollution. However, investigation of the deposition and the re-suspension of PM is challenging because of the difficulties in distinguishing between the resuspended and the deposited PM. This study created two Bayesian Networks (BN) models to explore the deposition and the re-suspension of PM as well as the important influential factors. The outcomes of BN modelling revealed that deposition and re-suspension of PM10 occurred under both, high-traffic and low-traffic conditions, and the re-suspension of PM2.5 occurred under low-traffic conditions. The deposition of PM10 under low-volume traffic condition is 1.6 times higher than under high-volume traffic condition, which is attributed to the decrease in PM10 caused by relatively higher turbulence under high-volume traffic conditions. PM10 is more easily resuspended from road surfaces compared to PM2.5 as the particles which larger than the thickness of the laminar airflow over the road surface are more easily removed from road surfaces. The increase in wind speed contributes to the increase in PM build-up by transporting particulates from roadside areas to the road surfaces and the airborne PM2.5 and PM10 increases with the increase in relative humidity. The study outcomes provide a step improvement in the understanding of the transfer processes of PM2.5 and PM10 between atmosphere and urban road surfaces, which in turn will contribute to the effective design of mitigation measures for urban stormwater and air pollution.     

基于土壤重金属特征的绿色食品产地环境评价——以重庆(江津)现代农业园区为例

以重庆市江津区现代农业园区内4种农用地类型的653个表层土样为研究对象,测定土壤重金属元素含量.依据绿色食品产地环境相关标准,采用重金属污染指数法对标准中所涉及的Cu、Cr、Cd、Hg、As和Pb共6种重金属元素开展土壤环境质量评价和潜在生态风险评价,并结合用地类型确定绿色农产品生产的适宜性土壤分区.结果表明：除元素Cu、Cr和As外,其他元素的平均含量均高于重庆市土壤背景值;除元素As和Hg外,其他元素的平均含量均高于我国土壤背景值.土壤重金属元素Cu、Cr、Cd、Hg、As和Pb的平均浓度均达到《绿色食品产地环境质量》标准要求,但Cd污染已处于安全警戒线内,应高度警惕和严格监管园区内该元素的污染.水田、旱地和果园的土壤环境均以尚清洁为主,其他园地的土壤环境以轻度污染水平为主.部分水田存在一定程度的Pb污染（轻度）、Hg污染（中度）和Cd污染（中度）,旱地存在一定程度的Hg污染（中度）和Cd污染（中度）,果园和其他园地的土壤存在一定程度的Cd的轻、中度污染.全区符合《绿色食品产地环境质量》标准要求的土壤面积为12.47km²,占农用地总面积的63.64%,旱地、果园、水田和其他园地中符合该标准的面积分别占各自总面积的84.83%、68.07%、56.51%和42.09%.4种用地类型的土壤环境生态风险水平均以低等为主,共占研究区总面积的74.70%.Cd和Hg在园区表层土壤重金属生态风险评估中占有较大的贡献率,应列为优先控制污染元素并对其生态效应开展进一步研究.     

不同基质生物炭对厌氧处理餐厨垃圾效能及微生态的影响

探究了剩余污泥（SS）、餐厨垃圾（FW）、玉米芯（CC）、甘蔗渣（BG）4种不同基质生物炭对厌氧生物处理餐厨垃圾效能的影响,对厌氧污泥的关键酶活性、微生物群落分布以及代谢途径等微生态进行了分析.结果表明,厌氧反应器分别加入4种生物炭后,COD平均去除率分别提高了29.49%、23.16%、29.42%、40.32%;傅里叶红外分析表明,投加SS生物炭组出水中羟基、酰胺基以及C-O-C伸缩振动峰减弱.4个厌氧反应器中厌氧污泥的乙酸激酶活性分别为0.40,0.42,0.96,0.98 μmol/g,表明投加CC与BG生物炭促进了餐厨垃圾的厌氧水解酸化过程;厌氧污泥胞外聚合物的蛋白质/多糖之比分别为0.415、0.56、1.89、2.8,投加CC、BG生物炭提高了污泥的稳定性.4个厌氧反应器中拟杆菌门、变形菌门、厚壁菌门为主要菌群,投加BG生物炭促进了变形菌门与厚壁菌门的生长;对于古细菌而言,甲烷杆菌属与甲烷丝菌属为优势种群,SS组的甲烷杆菌属丰度最高（53.48%）,而BG组中甲烷丝菌属丰度最高（42.72%）.KEGG功能分析表明古菌及细菌均以碳水化合物代谢、氨基酸代谢为主;而投加BG与SS生物炭后,微生物膜运输水平得到了提高.     

Analysis of pollutant levels in central Hong Kong applying neural network method with particle swarm optimization

Air pollution has emerged as an imminent issue in modernsociety. Prediction of pollutant levels is an importantresearch topic in atmospheric environment today. For fulfillingsuch prediction, the use of neural network (NN), and inparticular the multi-layer perceptrons, has presented to be acost-effective technique superior to traditional statisticalmethods. But their training, usually with back-propagation (BP)algorithm or other gradient algorithms, is often with certaindrawbacks, such as: 1) very slow convergence, and 2) easilygetting stuck in a local minimum. In this paper, a newlydeveloped method, particle swarm optimization (PSO) model, isadopted to train perceptrons, to predict pollutant levels, andas a result, a PSO-based neural network approach is presented. The approach is demonstrated to be feasible and effective bypredicting some real air-quality problems.     

Using improved neural network model to analyze RSP,NOx and NO2 levels in urban air in Mong Kok,Hong Kong

As the health impact of air pollutants existing in ambient addresses much attention in recent years, forecasting of airpollutant parameters becomes an important and popular topic inenvironmental science. Airborne pollution is a serious, and willbe a major problem in Hong Kong within the next few years. InHong Kong, Respirable Suspended Particulate (RSP) and NitrogenOxides NO_x and NO₂ are major air pollutants due to thedominant diesel fuel usage by public transportation and heavyvehicles. Hence, the investigation and prediction of the influence and the tendency of these pollutants are ofsignificance to public and the city image. The multi-layerperceptron (MLP) neural network is regarded as a reliable andcost-effective method to achieve such tasks. The works presentedhere involve developing an improved neural network model, whichcombines the principal component analysis (PCA) technique and theradial basis function (RBF) network, and forecasting thepollutant levels and tendencies based in the recorded data. Inthe study, the PCA is firstly used to reduce and orthogonalizethe original input variables (data), these treated variables arethen used as new input vectors in RBF neural network modelestablished for forecasting the pollutant tendencies. Comparingwith the general neural network models, the proposed modelpossesses simpler network architecture, faster training speed,and more satisfactory predicting performance. This improvedmodel is evaluated by using hourly time series of RSP, NO_x and NO₂ concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP, NO_x and NO₂ concentrationswith the actual data of these pollutants recorded at the monitorystation, the effectiveness of the proposed model has been proven.Therefore, in authors' opinion, the model presented in the paper is a potential tool in forecasting air quality parameters and hasadvantages over the traditional neural network methods.     

Extractive Spectrofluorometric Determination of Quinalphos Using Fluorescein in Environmental Samples

A simple, sensitive, convenient and low cost spectrofluorometric technique for determination of quinalphos is described. Quinalphos is decomposed with sodium ethoxide. Resultant sodium diethyl thiophosphate (Na-DETP) is extracted with a cationic fluorescein as ion pair complex into 1-chloro-2-nitrobenzene. The fluorescence of the ion pair, fluorescein-DETP is measured at 530 nm after excitation at 490 nm. This technique is applicable for the determination of quinalphos in water, food grains and vegetable samples up to ng level with Amberlite XAD-4.     

Ambient Air Quality Monitoring Network Design for Assessing Human Health Impacts from Exposures to Airborne Contaminants

Existing methods of establishing ambientair quality monitoring networks typically evaluateonly parameters related to ambient concentrations ofthe contaminant(s) of interest such as emissionsource characteristics, atmospheric transport anddispersion, secondary reactions, depositioncharacteristics, and local topography. However,adverse health risks from exposures to airbornecontaminants are a function of the contaminant andthe anatomic and physiologic characteristics of theexposed population. Thus, ambient air qualitymonitoring networks designed for the protection ofpublic health or for epidemiological studiesevaluating adverse health impacts from exposures toambient air contaminants should account for bothcontaminant characteristics and human healthparameters. A methodology has been establishedwhich optimizes ambient air quality monitoringnetworks for assessments of adverse human healthimpacts from exposures to airborne contaminants byincorporating human health risk assessmenttechniques. The use of risk assessment techniquesas the basis for designing ambient air qualitymonitoring networks will help to target limitedfinancial and human resources to evaluate humanhealth risks from exposures to airbornecontaminants.     

Mulberry-silkworm food chain--a templet to assess heavy metal mobility in terrestrial ecosystems

Assessment of the food chain mobility of heavy metals inthe natural ecosystem receives more attention nowadays. Inthe present study, mulberry-silkworm food chain has beenfocused as a templet to assess the biomobility of heavymetals in soil-higher plant-insect hierarchy. Both in thecase of Cd and Cu treatments, higher mobility wasobserved in the level-1 (soil–root) followed by level-3(leaf–larva), level-4 (larvae–faecal) and level-2(root–leaf). Consequently, roots accumulated more amountsof Cd and Cu, with a limited transport to the leaves.Among the two metals (Cd and Cu) tested, in the plant,the transfer potential of Cd exceeds that of Cu. Whereasin the case of leaf–larval transfer, Cu precedes overCd. Accumulation of Cd and Cu in all the levels (1–4)tested showed a concentration dependent increase, except inthe level 4 (larva–faecal) of Cd treatment where adeclining trend was noticed.