Impact of climate change on maize yield in China from 1979 to 2016

Climate change severely impacts agricultural production, which jeopardizes food security. China is the second largest maize producer in the world and also the largest consumer of maize. Analyzing the impact of climate change on maize yields can provide effective guidance to national and international economics and politics. Panel models are unable to determine the group-wise heteroscedasticity, cross-sectional correlation and autocorrelation of datasets, therefore we adopted the feasible generalized least square(FGLS) model to evaluate the impact of climate change on maize yields in China from 1979–2016 and got the following results:(1) During the 1979–2016 period, increases in temperature negatively impacted the maize yield of China. For every 1°C increase in temperature, the maize yield was reduced by 5.19 kg 667 m–2(1.7%). Precipitation increased only marginally during this time, and therefore its impact on the maize yield was negligible. For every 1 mm increase in precipitation, the maize yield increased by an insignificant amount of 0.043 kg 667 m–2(0.014%).(2) The impacts of climate change on maize yield differ spatially, with more significant impacts experienced in southern China. In this region, a 1°C increase in temperature resulted in a 7.49 kg 667 m–2 decrease in the maize yield, while the impact of temperature on the maize yield in northern China was insignificant. For every 1 mm increase in precipitation, the maize yield increased by 0.013 kg 667 m–2 in southern China and 0.066 kg 667 m–2 in northern China.(3) The resilience of the maize crop to climate change is strong. The marginal effect of temperature in both southern and northern China during the 1990–2016 period was smaller than that for the 1979–2016 period.     

基于格网的GCM数据修订分析未来海南岛农业水热资源的变化特征

以海南岛为研究区域，选用5个大气环流模式（GCMs）1970−1999年的逐日输出数据和同期地面气象观测数据，使用空间插值降尺度到0.5°×0.5°格网。以格网单元为基础，应用系统误差修订（修正值法或比值法）和多模式集合平均方法（贝叶斯模型平均法BMA或等权重平均法EW），训练与验证GCMs输出值并进行综合修订。在此基础上，分析RCP2.6、RCP4.5和RCP8.5情景下，未来海南岛近期（2020−2059年）和远期（2060−2099年）农业水热资源，包括年平均气温、1月平均气温、≥10℃积温、≥20℃积温、年降水量、1月降水量和≥20℃界限温度生长期间降水量的变化特征。结果表明：GCMs输出值的系统误差和BMA权重系数在格网间存在较大的空间差异，且GCMs输出值低估逐日最高气温约3.55℃，高估逐日最低气温约1.19℃，逐日降水量仅为观测值的54.35%。基于格网的综合修订，可有效降低GCMs输出值在空间上的不确定性，BMA与EW的修订结果相似，均优于单一GCM模式。通过格网BMA综合修订后，最高气温、最低气温和降水量在验证期的相关系数r分别约提升0.10、0.07和0.06；均方根误差RMSE分别约降低2.38℃、1.01℃和1.01mm；较单一GCM相对观测值的偏差平均约减少3.25℃、1.13℃和25.67mm。未来海南岛农业热量资源在空间上主要表现为从中部向外围逐渐升高，高温主要分布在南部至西部沿海地区，年平均气温的增幅全岛较为接近，1月平均气温、≥10℃积温和≥20℃积温的增幅分别表现为由东向西、由北向南和由中部向外围递减。在时间上，RCP8.5情景下所有农业热量资源均为极显著增加且增温最快，RCP4.5情景为先增加后平缓，RCP2.6情景较为平缓，远期无显著增温。未来海南岛降水资源在空间上转为由东向西逐步递减的格局，南部和北部沿海地区降水变率增加，西部和中部降水变率减少，在时间上无显著变化趋势。随着未来海南岛气候变暖和降水格局的改变，农作物适宜种植面积扩大，会对农业生产带来巨大挑战，应提前布局，做好趋利避害。     

云南不同气候带农业气候资源变化特征研究

为分析全省平均以及各气候带光能、热量、水分资源的时空变化特征,使用云南115站及6个气候带代表站1961—2018年的气候要素计算各地农业气候资源统计量。结果表明,光能资源变化以减少趋势为主,出现显著突变,各气候带太阳辐射变化可能会引起云南太阳辐射高低值中心发生变化,2009年以来辐射明显增加可能会导致太阳辐射出现新的变化趋势和突变点;热量资源一致显著增加,喜凉及喜温作物的活动积温、积温持续时间及无霜期长度均显著增加,并且呈现初日提前、终日推后的趋势,对作物生长有利;水分资源总体呈现减少趋势,尤其21世纪以来下降趋势明显,亚热带地区暖干化现象突出,干旱风险等级较高。     

岩溶流域典型农业区水体氢氧同位素的空间异质性及形成机制

分别于平水期和枯水期采集了花溪河流域典型农业区地表水和地下水样品。利用氢氧同位素示踪技术,结合土地利用类型对研究区不同水体的补给来源、季节变化及主要影响过程进行了分析,并对不同水体氢氧同位素值进行了空间插值分析,同时对其形成机制进行了分析,阐明了不同土地利用类型影响下的主要水文过程。结果表明:(1)研究区不同水体的主要补给来源为当地大气降水,月亮湖水库受蒸发作用影响明显,地表水和地下水的δD和δ~(18)O整体上呈现平水期高于枯水期的特征。(2)地下水的δD和δ~(18)O在枯水期与平水期均呈现明显的空间分异性特征,西部水田/水库集中区富集,东部旱地集中区贫化,土地利用对研究区环境水文过程影响明显。该研究结果有助于了解不同土地利用方式下地表水对地下水的影响,为流域管理提供科学依据。     

气候变化情景下流苏香竹潜在分布区预测

流苏香竹（Chimonocalamus fimbriatus）是云南特有珍稀竹种，主要分布于云南西南部。文章以野外调查获取的流苏香竹分布信息为主，运用最大熵模型（MaxEnt）同时结合地理信息系统（ArcGIS），基于19个气候因子，预测其在当前及未来气候变化情景下的潜在分布区。结果表明：当前流苏香竹的高适生区和中适生区主要分布于德宏州、保山市和临沧市等地，除迪庆州、丽江市和昭通市外，云南其他区域均有低适生区零星分布。在未来2050s和2070s的2个时间段，基于2种不同共享社会经济路径（SSP1-2.6和SSP5-8.5），流苏香竹的高适生区面积呈减少的趋势，尤其是SSP5-8.5路径下，高适生区面积仅为当前的12.51%（2050s）和18.63%（2070s）；中、低适生区在SSP1-2.6路径下，显著扩张（2050s）或略微扩张（2070s），在SSP5-8.5路径下，则大幅收缩。流苏香竹野外实际分布区及其潜在分布区均以斑块状为主，可能与云南特殊的地形、地貌有关。影响流苏香竹分布的主导气候因子为最湿月份降水量、最暖月份最高温度、最干季度降水量和平均气温日较差。流苏香竹对气候变化比较敏感，根据其野外分布状况，建议以就地保护为主、迁地保护为辅，在其潜在适生区内适当引种栽培。     

Impact of grassland contract policy on soil organic carbon losses from alpine grassland on the Qinghai–Tibetan Plateau

Carbon storage in the soils on the Qinghai–Tibetan Plateau plays a very important role in the global carbon budget. In the 1990s, a policy of contracting collective grasslands to smaller units was implemented, resulting in a change from the traditional collective grassland management to two new management patterns: a multi‐household management pattern (MMP: grassland shared by several households without enclosures) and a single‐household management pattern (SMP: grassland enclosed and used by only one household). In 2016, 50 MMP and 54 SMP winter pastures on the Qinghai–Tibetan Plateau were sampled to assess the differences in soil organic carbon (SOC) between the two management patterns. Results showed that average SOC was significantly greater under MMP than under SMP, with an estimated 0.41 Mg C/ha/yr lost due to SMP following the new grassland contract. Based on the government's grassland policy, four grassland utilization scenarios were developed for both summer and winter pastures. We found that if the grassland were managed under SMP, likely C losses ranged between 0.31 × 10⁷ and 6.15 × 10⁷ Mg C/yr across the Qinghai–Tibetan Plateau relative to MMP, which more closely resembles pre‐1990s grassland management. Previous estimates of C losses have only considered land use change (with cover change) and ignored the impacts driven by land management pattern changes (without cover change). The new data suggest that C losses from the Qinghai–Tibetan Plateau are greater than previously estimated, and therefore that the grassland contract policy should be reviewed and SMP households should be encouraged to reunite into the MMP. These findings have potential implications for land management strategies not only on the Qinghai–Tibetan Plateau but also other grazing regions globally where such practices may exist.     

Cohort‐specific variation in juvenile coho salmon habitat use

In this study, we examined summer and fall freshwater rearing habitat use by juvenile coho salmon (Oncorhynchus kisutch) in the quickly urbanising Big Lake drainage in south‐central Alaska. Habitat use was assessed by regressing fish count data against habitat survey information across thirty study sites using generalised linear mixed models. Habitat associations were examined by age‐0 and age‐1+ cohorts separately, providing an opportunity to compare habitat use across different juvenile coho salmon life stages during freshwater rearing. Regression results indicated that the age‐0 cohorts were strongly associated with shallow, wide stream reaches with in‐stream vegetation, whereas age‐1+ cohorts were associated with deeper stream reaches. Furthermore, associations between fork length and habitat characteristics suggest cohort‐specific habitat use patterns are distinct from those attributable to fish size. Habitat use information generated from this study is being used to guide optimal fish passage restoration planning in the Big Lake drainage. Evidence for habitat use partitioning by age cohort during freshwater juvenile rearing indicates that pooling age cohorts into a single “juvenile” stage for the purposes of watershed management may mask important habitat use dynamics.     

亏缺灌溉对南疆棉花生长和水分利用的影响

针对南疆地区水资源短缺、作物水分利用效率低等问题,以棉花为试验材料进行田间小区试验,在棉花现蕾期、开花期以及结铃期分别设置3个亏缺灌溉水平(W₁:50%ET_c,W₂:65%ET_c,W₃:80%ET_c,ET_c为作物蒸发蒸腾量),以全生育期100%ET_c灌溉处理为对照(CK),研究膜下滴灌条件下,不同生育期亏缺灌溉对棉花生长、产量、氮素吸收和水分利用效率的影响.结果表明:现蕾期亏水对棉花株高、叶面积指数、地上干物质生长、氮素吸收和产量有不同程度的抑制效应,但复水后补偿效应显著,其中轻度亏水(W₃)在籽棉产量减少3.48%的条件下,WUE高达1.57 kg/m³,显著高于CK的1.48 kg/m³;开花期亏水,棉花的各项生长指标均有显著降低,复水后补偿效应不显著,不利于棉花生长发育;结铃期亏水对棉花地上干物质累积、氮素吸收和产量均有显著的抑制效应,但在W₂和W₃水平下,WUE均达1.51 kg/m³.综合考虑在保证棉花产量的同时达到节水增产的目的,可在棉花蕾期进行80%ET_c灌水,其他生育阶段实施充分灌溉,来控制营养生长,促进生殖生长,获得更高的水分利用效率.     

长武塬区苹果园和农田相互转换的深层土壤水环境效应

研究长武塬区苹果园和农田相互转换后0~1 000 cm土壤含水量特征，分析了苹果园土壤干燥化和苹果园转换为农田后土壤水分的恢复效应。结果表明：2、7、17、23、29 a苹果园200~1 000 cm的平均土壤含水量分别为22.8%、21.4%、16.8%、15.4%、14.9%。500~1 000 cm土层中，29 a苹果园平均土壤含水量（14.5%）高于23 a的果园（13.3%）；17~29 a的苹果园均表现为轻度干燥化；基于苹果园和农田转换后土壤水分变化情况估算，苹果园最大种植年限为21 a。苹果园转换为农田1、5、10 a后，农田200~1 000 cm土层土壤含水量分别为：15.3%、15.7%和16.2%，恢复到土壤稳定湿度以上的土层厚度分别为140 cm（1 a）、220 cm（5 a）和400 cm（10 a）。     

Soil protection for a sustainable future: options for a soil monitoring network for Ireland

The increased recognition of the importance of soil is reflected in the UN Post‐2015 Development Agenda with sustainable development goals that directly and indirectly relate to soil quality and protection. Despite a lack of legally binding legislation for soil protection, the European Commission remains committed to the objective of soil protection. However, the achievement of a legally binding framework for soil protection relies on the implementation of a soil monitoring network (SMN) that can detect changes to soil quality over time. As beneficiaries do not pay for the provision of soil information, the options for soil monitoring are limited. The use of existing data sets should be considered first. Using Ireland as an example, this research explored the opportunities for a SMN for Ireland considering three existing national data sets. The options for a SMN are considered in terms of their spatial and stratified distribution, the parameters to be measured and an economic analysis of the options proposed. This research finds that for Ireland, either a 10 or a 16 km² grid interval stratified by land use and drainage class offers the best potential in relation to the spatial distribution of existing data sets to reflect local data at a national level. With existing data, the stratified SIS data using the 16 km² grid offers the best value for money, with baseline costs for analysis, excluding field costs, of between €706 481 and €2.8 million. Acknowledging the impossibility of measuring all parameters with ideal frequency, this study proposes a two‐tier system for optimized monitoring frequency. Parameters must anticipate future policy requirements. Finally, the implementation of a SMN must be accompanied by standardized methods, defined thresholds and action mandates to maintain soil quality within allowable limits.