耕地保护与可持续发展的辩证思考

国土资源尤其是耕地资源的有效保护与合理利用．是我国可持续发展的重要资源保障，耕地保护与全面建设小康社会密切相关。为此．从耕地保护对社会发展的重大意义出发，分析了耕地保护中的基本矛盾．并就耕地资源的可持续利用提出了建议。     

平地漫灌灌溉水净化与循环利用研究

为提高设施农业的水分利用率，设计了平地漫灌灌溉水净化与循环利用系统；阐述了系统的组成、节水灌溉原理与工作过程。理论分析可知，系统具有较高的输水利用率、灌水利用率和降水利用率，是农业节水灌溉的有效新途径。     

沈阳北部污水处理厂污泥土地利用可行性研究

采用污泥田间小区试验及污泥渗漏试验的方法,开展了沈阳北部污水处理厂污泥土地利用可行性研究.结果表明,沈阳北部污水处理厂污泥中N、P、K养分含量丰富,污泥中的Cd元素是污泥土地利用的主要限制因素.通过污泥土地利用限制因素法,确定了沈阳北部污水处理厂污泥总施用量为45 t·hm^-2.污泥渗漏、田间试验结果表明,只要污泥总施用量控制在45 t·hm^-2,NO3-N不会对地下水构成污染危害,土壤中的重金属含量也不会超过二级土壤环境质量标准,污泥可安全地进行土地利用.     

气候变化与大麦生产

分析了新洋农场近14年来的气候变化,年平均气温达14.4℃,比1990年前24年提高了0.6℃,且有升幅逐渐加大趋向。近5年平均气温比1990年前提高了0.9℃。气温升高以冬季最明显。近14年11月中旬至3月中旬升温1.15℃,而极端最低气温的升幅比平均气温更大。降水量变化的最显著特点是变率增大,近14年降水量的变异系数为35.0%,前24年为22.3%。从种植区划、品种、播期、耕作制度、密度、抗灾、病虫草害防治、种植结构调整、品质控制和秸秆还田等方面提出了应变对策。     

绿色低碳视域下长江经济带耕地生态外溢价值评估及时空变化分析

为推进长江经济带耕地保护和绿色低碳发展,利用当量因子法、环境成本法等测算耕地生态服务价值和负外部性价值,结合耕地生态足迹和生态承载力,将长江经济带11省（直辖市）划分为盈余区、平衡区与赤字区,对耕地生态外溢价值进行评估,并分析时空变化特征。结果显示：（1）2011-2021年长江经济带耕地生态价值总量呈上升趋势,由3 670.37亿元上涨至4 661.55亿元,耕地生态外溢价值缓慢上升,由1 353.82亿元上涨至1 970.77亿元。（2）长江经济带耕地资源整体处于盈余状态,耕地生态价值总体呈现出“东低西高”的态势,四川省耕地生态价值和盈余量最高,上海市耕地生态价值最低。（3）2011-2021年间,浙江省和上海市处于耕地生态赤字区,四川省处于耕地生态高盈余区,重庆市和贵州省处于耕地生态平衡状态,江西省、安徽省和江苏省耕地生态盈亏无显著变化,云南省和湖北省耕地生态盈余水平有所上升,湖南省耕地生态盈余水平有所下降。基于此,长江经济带各省（直辖市）应充分利用自身资源和优势,制定和实施适应性的耕地保护策略,推动耕地资源的可持续利用,为实现长江经济带的生态优先、绿色低碳发展目标做出贡献。     

经济林种子生理研究进展

经济林种子是育苗、造林的物质基础,种子品质的好坏直接关系到苗木生产及造林的成效。本文就经济林种子的休眠与萌发、种子的老化及劣变、种子活力等种子生理工作研究现状进行了较为系统的总结,并提出了今后经济林种子生理的研究方向和发展趋势。     

基于土地利用结构的河流岸带生态系统服务簇识别

河流岸带生态系统服务功能的有效发挥是河流生态系统稳定且健康的重要保障之一。为识别河流岸带土地利用与生态服务价值的空间结构,本文从河流岸带保护角度选取水资源保护、净化环境、水文调节、土壤保持、维持养分循环、生物多样性六项生态服务功能,基于遥感影像解译的土地利用数据,运用修正的当量因子法估算得到生态服务价值的空间分布规律,进而进行层次聚类分析识别河流岸带生态服务簇,并应用于太湖流域湖西区溧阳市县级河流——竹箦河作为实例研究。结果表明：（1）竹箦河河流岸带六项生态服务价值呈现出显著的空间异质性和聚集性。（2）竹箦河河流岸带可划分为五大类生态服务簇,分别为水源涵养型、绿色多元型、经济生态型、生态脆弱型以及耗水污染型。（3）各类服务簇的生态系统服务特点与空间布局分异明显,与岸带实际土地利用结构相协调,揭示了竹箦河河流岸带生态服务功能普遍较薄弱的现状。本文的研究结果可为竹箦河及类似平原地区骨干河流岸带功能区划分以及河流岸带生态保护与修复方案的制定提供理论参考。     

玉米宽窄行种植技术推广模式研究

玉米宽窄行种植技术在实际应用过程中,不仅可以实现对原有技术的创新和优化,还可以 保证玉米种植的质量、产量和销量。本文针对玉米宽窄行种植技术的推广模式进行分析和研究,为当 地农业可持续发展打下良好基础。     

A proposal for enhanced EU herbage VCU and DUS testing procedures

Herbage production is regarded as having environment-friendly credentials. However, as the ruminant production it supports is facing major challenges on sustainability, environmental footprint and human health concerns, EU herbage cultivar testing must contribute to the solutions. Before new cultivars can be sold in a member state (MS) and gain EU-wide marketing, they must pass official tests to prove they are both novel (distinct, uniform and stable, DUS) with improved value for cultivation and use (VCU). Herbage species present specific challenges, as their allogamy imposes a wide within-cultivar variation that adds complexity to DUS tests and their “value” is only realized in ruminant produce. Current VCU systems measure production, chemical composition and disease/stress tolerances, often on large numbers of candidate cultivars, but prohibitive labour costs and logistics mean that animal intake, ruminant output or environmental benefits cannot be measured directly. Furthermore, some candidate cultivars with proven superior VCU fail DUS even though the non-distinct comparison is with a significantly lower performing registered cultivar. To resolve these problem cases, a “vmDUS” distinctness tool is proposed, which uses molecular markers but conforms to UPOV-declared principles. A short overview of current grassland research shows smart proxy measures of animal value can easily and quickly be adopted into an integrated pan-European (EU-VCU) test network. The proposed EU-VCU scheme will reallocate test resources to conduct these additional tests by placing MS in data sharing collaborations, while retaining their national listing authority. The benefits to all stakeholders from adopting these new testing procedures are considered.     

Performance and uncertainty analysis of a short-term climate reconstruction based on multi-source data in the Tianshan Mountains region,China

Short-term climate reconstruction, i.e., the reproduction of short-term(several decades) historical climatic time series based on the relationship between observed data and available longer-term reference data in a certain area, can extend the length of climatic time series and offset the shortage of observations. This can be used to assess regional climate change over a much longer time scale. Based on monthly grid climate data from a Coupled Model Inter-comparison Project phase 5(CMIP5) dataset for the period of 1850–2000, the Climatic Research Unit(CRU) dataset for the period of 1901–2000 and the observed data from 53 meteorological stations located in the Tianshan Mountains region(TMR) of China during the period of 1961–2011, we calibrated and validated monthly average temperature(MAT) and monthly accumulated precipitation(MAP) in the TMR using the delta, physical scaling(SP) and artificial neural network(ANN) methods. Performance and uncertainty during the calibration(1971–1999) and verification(1961–1970) periods were assessed and compared using traditional performance indices and a revised set pair analysis(RSPA) method. The calibration and verification processes were subjected to various sources of uncertainty due to the influence of different reconstructed variables, different data sources, and/or different methods used. According to traditional performance indices, both the CRU and CMIP5 datasets resulted in satisfactory calibrated and verified MAT time series at 53 meteorological stations and MAP time series at 20 meteorological stations using the delta and SP methods for the period of 1961–1999. However, the results differed from those obtained by the RSPA method. This showed that the CRU dataset produced a low degree of uncertainty(positive connection degree) during the calibration and verification of MAT using the delta and SP methods compared to the CMIP5 dataset. Overall, the calibrated and verified MAP had a high degree of uncertainty(negative connection degree) regardless of the dataset or reconstruction method used. Therefore, the reconstructed time series of MAT for the period of 1850(or 1901)–1960 based on the CRU and CMIP5 datasets using the delta and SP methods could be used for further study. The results of this study will be useful for short-term(several decades) regional climate reconstruction and longer-term(100 a or more) assessments of regional climate change.