The effect of household wealth on the adoption of improved maize varieties in Zambia

Production and price risks that could render input use unprofitable sometimes prevent rural households from benefiting from input technological change. The household’s ability to cope with such risks and hence benefit from input technological change is often positively related to its wealth or stock of productive assets. Empirical evidence, however, suggests a non-linear relationship between wealth and adoption of new agricultural technologies so that within a rural community, households on the lower wealth continuum behave differently from those on the higher level. Using farm level data collected from 300 randomly selected households in three districts of Zambia in 2004/2005 crop season, this paper first stratifies households into poorly- and well-endowed households based on their access to productive assets and estimates separate double-hurdle models for the adoption of improved, high yielding maize (IHYM) varieties for each group. The results show that factors influencing the adoption and use intensity of IHYM varieties differ between the two groups. This draws attention to the need for recommending wealth group-specific interventions to increase the adoption and use intensity of such varieties and their subsequent impacts on food security and general livelihoods of the households. The explicit testing for the possibility that differences in household wealth affect the way in which other variables influence adoption decisions is the paper’s unique contribution to the adoption literature.     

Adoption and Impacts of Sustainable Agricultural Practices on Maize Yields and Incomes: Evidence from Rural Zambia

This paper uses a multinomial endogenous treatment effects model and data from a sample of over 800 households and 3,000 plots to assess the determinants and impacts of adoption of sustainable agricultural practices (SAPs) on maize yields and household incomes in rural Zambia. Results show that adoption decisions are driven by household and plot level characteristics and that the adoption of a combination of SAPs raises both maize yields and incomes of smallholder farmers. Adoption of improved maize alone has greater impacts on maize yields, but given the high cost of inorganic fertiliser that limits the profitability of adoption of improved maize, greater household incomes are associated rather with a package involving SAPs such as maize–legume rotation and residue retention.     

Insuring Against Losses from Transgenic Contamination: The Case of Pharmaceutical Maize

Concerns about the risk of food supply contamination have limited the development and commercialization of certain pharmaceutical plants. This article develops an insurance pricing model that helps translate these concerns into a cost-benefit analysis. The model first estimates the physical dispersal of maize pollen subject to a number of weather parameters. This distribution is then validated with the limited amount of currently available field trial data. The physical distribution is then used to calculate the premium for a fair-valued insurance policy that would fund the destruction of possibly contaminated fields. The flexible framework can be readily adapted to other crops, management practices, and regions.     

Design Factors Influencing Willingness‐to‐Pay Estimates in the Becker‐DeGroot‐Marschak (BDM) Mechanism and the Non‐hypothetical Choice Experiment: A Case of Biofortified Maize in Zambia

Two of the experimental methods used to estimate willingness‐to‐pay (WTP) for a non‐market good, the Becker‐DeGroot‐Marschak (BDM) mechanism and the non‐hypothetical choice experiment (nHCE) often lead to significantly different WTP estimates, complicating the choice between the methods. In Zambia the same group of researchers used both techniques to evaluate WTP for orange maize, which provides more vitamin A than other varieties. This provided an opportunity to analyse the sources of the difference. In the BDM experiment, one group of respondents was provided with more training opportunities than the other, and made higher bids. Accounting for lexicographic behaviour in the nHCE reduced the estimated WTP. These two design factors together resulted in a decrease in the WTP difference for orange maize (1,279–632 ZMK) although the difference remains statistically significant. More training was also shown to eliminate the effects of different orders in which maize varieties were presented.     

基于Hybrid Maize 模型的3个生态地点玉米产量潜力评估

[目的]准确评估玉米高产措施的增产潜力,量化设计高产玉米技术模式,为合理应用玉米高产措施挖掘玉米产量潜力提供理论依据和技术参考。[方法]选取北京、泰安和榆林为研究地点,以当前大面积推广应用的玉米品种郑单958为供试品种,利用Hybrid-Maize模型,对"直播"、"晚收"和"增密"3种主要玉米高产措施下的玉米产量潜力动态响应规律进行模拟分析,进而设计构建适宜不同地点的玉米高产技术模式并评价其较当前生产模式下的增产潜力。[结果]在北京和泰安,播期推迟至5月20日后有利于玉米产量潜力增加,直播较套种措施下的玉米产量潜力在北京和泰安分别提高18%和20%;适时晚收可有效增加玉米产量潜力,在中产年份,每推迟收获1d,产量潜力在北京和泰安分别能够增加214kg/hm~2和204kg/hm~2;增密措施是提高产量潜力的重要措施,不同地点均表现为在密度高于7万株/hm~2后,增产效应降低。不同生态区的玉米合理密度不同,北京和泰安的合理密度在7. 5万～8万株/hm~2,榆林的合理密度在8. 5万～9万株/hm~2;当前的管理措施下,不同地点玉米长期平均产量潜力为12～15t/hm~2。通过措施优化组合能有效提高玉米产量潜力,不同地点措施优化途径不同。总体上,设计构建的高产技术模式较当前生产模式下的玉米长期平均产量潜力提高15%～41%。[结论]当前玉米生产主推技术(直播、晚收、增密)能有效提升玉米产量潜力,但其适用范围在不同地点有所不同,Hybrid-Maize模型是实现玉米定量设计栽培的重要手段。     

彭阳县玉米干旱风险评估与区划

［目的］彭阳县玉米种植区域广泛,但干旱灾害频发,常造成玉米减产甚至绝收。因此加强彭阳县玉米干旱灾害风险评估与区划有着重要的现实意义。［方法］利用彭阳县气象数据以及地形、土壤类型等数据,基于灾害风险评估原理,运用相关分析法、层次分析法,结合GIS技术对彭阳县玉米干旱灾害进行风险评估与区划。［结果］彭阳县北部及西部降水少,玉米干旱危险性较高,南部和东部降水多,玉米干旱危险性较低; 草庙乡土壤保水性最差,玉米干旱敏感性最高,白阳镇、城阳乡土壤保水性最好,玉米干旱敏感性最低; 新集乡、白阳镇、草庙乡耕地面积所占总面积最大,玉米干旱暴露性最高,小岔乡耕地面积所占总面积最小,玉米干旱暴露性最低; 白阳镇灌溉田比例最高,玉米防灾减灾能力最强,彭阳北部1镇3乡都没有灌溉农田,玉米防灾减灾能力最弱。综合来看,彭阳县西北部玉米干旱综合风险较高,西南部和东南部风险较低,其余地区以中等风险为主。［结论］玉米干旱灾害风险评估与区划是科学认识农业干旱的基础,为科学管理以及制定合理有效的防灾减灾方案提供科学参考。     

GIS支持下的玉米种植区土地生态适宜性评价

［目的］文章利用测土配方施肥和耕地地力评价结果,对高县玉米种植区土地进行生态适宜性评价。［方法］在GIS的支持下,结合层次分析法与空间插值等方法,选取气候条件、立地条件、土壤理化性状、土壤养分4个方面10个因子进行评价。［结果］（1）研究区玉米种植区可以划分为高度适宜区、适宜区、次适宜区和不适宜区,其面积分别为7 4073hm2、24 9386hm2、17 1160hm2和6 8230hm2,分别占耕地总面积的132%、443%、304%和121%; （2）影响该区域玉米种植适宜性的主要指标有生育期间≥10℃积温、生育期间降雨量、有机质、速效钾; （3）经实地调查核实与对比分析,评价结果与实际相符,适合玉米生长区域主要在河流沿岸阶地、平坝以及缓丘、低丘区,不适合种植在中部、西南部和东南部高丘、低山区。［结论］该研究认为对于次适宜种玉米的区域应以平整土地、增厚土层、培肥地力为重点进行耕地质量建设; 高度适宜和适宜区,可以引领优质玉米发展形成优势突出和特色鲜明的玉米产业带。反之,不适宜种植玉米的地区则应及时调整农业产业结构,改种其他作物或退耕还林。     

1978~2014年间中国玉米生产的时空特征变化研究

［目的］厘清中国玉米生产的时空变化特征,有助于保障玉米的持续、稳定供应。［方法］文章采用经验模态分解等方法,基于玉米总产、播种面积及单产3个指标,从全国和省域两个尺度系统分析1978~2014年中国玉米生产的时空特征。［结果］（1）研究期间,全国玉米产量增加了1.60亿t,其波动量呈现先增大后减小的趋势,波动指数的变化幅度不大; 玉米播种面积增加了1716万hm2,其变化趋势与玉米总产量的变化趋势基本一致;玉米单产从1978年的2802.7kg/hm2波动性增至2014年的5809.1kg/hm2,总体波动幅度大于玉米播种面积和总产量的波动幅度。（2）研究期间,省域玉米增加量呈现出“北高南低”的特征,中国玉米生产重心进一步北移;省域玉米播种面积增加量呈现出“北高南低”“西高东低”的特征,东北区和华北区的玉米播种面积进一步扩大;省域玉米单产增加量呈现出“西高东低”“北高南低”的特征,省域单产以趋势增长为主,但年际间的波动较剧烈。［结论］1978~2014年,省域间玉米生产分异特征明显,建议制定差别化的玉米生产策略。     

基于最小距离法的RADARSAT-2遥感数据旱地作物识别

利用雷达遥感技术进行作物识别是当前作物遥感监测的研究热点之一,但利用雷达遥感技术进行旱地作物识别的相关研究较少,该文以RADARSAT 2雷达遥感数据对两种旱地作物玉米和棉花进行识别。以河北省枣强县为研究区,对其区域内的玉米和棉花进行识别。首先分析了与卫星过顶时刻同步采集的作物参数与后向散射系数之间的相关性发现,在植株高度、生物量、作物含水量、叶面积指数这四个作物参数中,植株高度与后向散射系数的相关性最大,其次是作物含水量;同时,通过最小距离法应用多时相、多极化雷达遥感数据进行作物识别,其精度可达到85%,通过与资源三号光学遥感数据结合,其作物识别精度提高到了93%。研究结果表明,雷达遥感数据应用于旱地作物识别是可行的,雷达遥感数据与光学遥感数据的结合能提高旱地作物识别的精度。该研究为应用雷达遥感数据进行旱地作物识别提供了参考。     

Conservation tillage vs. conventional tillage: long-term effects on yields in continental,sub-humid Central Europe,Hungary

The present study reports novel data concerning Conservation Tillage (CT) in the continental sub-humid climate zone in Central Europe (Hungary), an area which has been mostly neglected in the course of previous CT studies. The results of a 10-year (2003–2013) comparative study of mouldboard ploughing tillage (PT) and CT (no inversion, using a reduced number of tillage operations and leaving min. 30% crop residues on the soil surface) types are reported. Our extensive monitoring system has provided new and detailed information concerning technologies and yields both from the first, transitional period and, over the following years, of adapted technology. Our results suggest that tillage type was a more important factor in the question of yields than either the highly variable climate of the studied years, or the diverse slope conditions of the plots. During the first three years of technological changeover to CT (2003–2006), a decrease of 8.7% was measured, respective to PT. However, the next seven years (2007–2013) brought a 12.7% increase of CT yields. Our study revealed key factors in the initial reduction of crops during the technological change, and may accordingly serve as a guideline for the shortening or avoidance of decline in the transitional period.