气象条件对广西春大豆生长发育及产量的影响

本文用1983—1985年广西春大豆区试播种资料与气象资料进行了相关统计分析。结果表明,影响广西春大豆各生育阶段生育日数的关键气象因子:播种至出苗期为土壤温度;出苗至始花期为日平均气温和降水日数;始花至成熟期为日照时数;出苗至成熟期为降水日数。影响产量的关键气象因子为始花至成熟期的温度日较差、平均相对湿度和降水量。温度日较差和降水量对广西春大豆各生育阶段生育日数的影响不显著。     

Genetic variation in high-protein pea

Of the four high-protein genotypes isolated after induced mutations and breeding, two exhibited height and yield nearly equal to the Bonneville pea variety, but matured later. Their stability for protein yield over generations and locations, aminogram pattern and nutritional value remains to be determined. Path analysis and correlations indicate that developing a short-statured pea with higher yield and increased protein content may not be possible with the available breeding methods and germ plasm.     

小麦叶锈菌越夏与气象因子相关性初步分析

小麦叶锈病是一种严重威胁小麦安全生产、依靠气流传播的真菌病害,近年来发生呈逐年加重趋势。为明确气象因子对小麦叶锈菌越夏的影响,本研究通过对全国698个气象站点7月-8月最热10 d的日均温和平均日最高气温进行回归分析,对7月-8月0 cm平均地温、平均风速、平均降水量、平均日照时数和平均相对湿度进行空间插值,提取了93个小麦叶锈菌越夏调查点的气象数据,再与调查点小麦叶锈菌能否越夏进行相关性分析,结果显示小麦叶锈菌越夏与7月-8月最热10 d日均温和最热10 d平均日最高气温之间存在极显著相关性(P<0.01),与其他气象因子相关性不显著(P> 0.05),结果为小麦叶锈病的越夏区划奠定了基础。     

Integrating multiple electromagnetic data to map spatiotemporal variability of soil salinity in Kairouan region,Central Tunisia

Soil salinization is a major problem affecting soils and threatening agricultural sustainability in arid and semi-arid regions,which makes it necessary to establish an efficient strategy to manage soil salinity and confront economic challenges that arise from it.Saline soil recovery involving drainage of shallow saline groundwater and the removal of soil salts by natural rainfall or by irrigation are good strategies for the reclamation of salty soil.To develop suitable management strategies for salty soil reclamation,it is essential to improve soil salinity assessment pro cess/mechanism and to adopt new approaches and techniques.T his study mapped a recovered area of 7200 m2 to assess and verify variations in soil salinity in space and time in K airouan region in Central Tunisia,taking into account the thickness of soil materials.Two electromagnetic conductivity meters(EM38 and EM31)were used to measure the electrical conductivity of saturated soil-paste extract(ECe)and apparent electrical conductivity(E Ca).Multiple linear regression was established between ECe and ECa,and it was revealed that ECa-EM38 is optimal for E Ce prediction in the surface soils.Salinity maps demonstrated that the spatial structure of soil salinity in the region of interest was relatively unchanged but varied temporally.Variation in salinity at the soil surface was greater than that at a depth.These findings can not only be used to track soil salinity variations and their significance in the field but also help to identify the spatial and temporal features of soil salinity,thus improving the efficiency of soil management.     

Modelling the biological invasion of Prosopis juliflora using geostatistical-based bioclimatic variables under climate change in arid zones of southwestern Iran

Invasive species have been the focus of ecologists due to their undesired impacts on the environment.The extent and rapid increase in invasive plant species is recognized as a natural cause of global-biodiversity loss and degrading ecosystem services.Biological invasions can affect ecosystems across a wide spectrum of bioclimatic conditions.Understanding the impact of climate change on species invasion is crucial for sustainable biodiversity conservation.In this study,the possibility of mapping the distribution of invasive Prosopis juliflora(Swartz)DC.was shown using present background data in Khuzestan Province,Iran.After removing the spatial bias of background data by creating weighted sampling bias grids for the occurrence dataset,we applied six modelling algorithms(generalized additive model(GAM),classification tree analysis(CTA),random forest(RF),multivariate adaptive regression splines(MARS),maximum entropy(Max Ent)and ensemble model)to predict invasion distribution of the species under current and future climate conditions for both optimistic(RCP2.6)and pessimistic(RCP8.5)scenarios for the years 2050 and 2070,respectively.Predictor variables including weighted mean of CHELSA(climatologies at high resolution for the Earth’s land surface areas)-bioclimatic variables and geostatistical-based bioclimatic variables(1979–2020),physiographic variables extracted from shuttle radar topography mission(SRTM)and some human factors were used in modelling process.To avoid causing a biased selection of predictors or model coefficients,we resolved the spatial autocorrelation of presence points and multi-collinearity of the predictors.As in a conventional receiver operating characteristic(ROC),the area under curve(AUC)is calculated using presence and absence observations to measure the probability and the two error components are weighted equally.All models were evaluated using partial ROC at different thresholds and other statistical indices derived from confusion matrix.Sensitivity analysis showed that mean diurnal range(Bio2)and annual precipitation(Bio12)explained more than 50% of the changes in the invasion distribution and played a pivotal role in mapping habitat suitability of P.juliflora.At all thresholds,the ensemble model showed a significant difference in comparison with single model.However,Max Ent and RF outperformed the others models.Under climate change scenarios,it is predicted that suitable areas for this invasive species will increase in Khuzestan Province,and increasing climatically suitable areas for the species in future will facilitate its future distribution.These findings can support the conservation planning and management efforts in ecological engineering and be used in formulating preventive measures.     

间隔覆盖条件下坡面产流产沙状况的BP神经网络模拟

本文以塿土和黄绵土作为实验材料,尝试使用BP神经网络方法(Back-Propagation neural network)模拟人工降雨条件下,间隔覆盖坡面的产流产沙状况。通过设置不同坡度、降雨强度、面积比,获得各种因素不同水平组合下的实测数据;以实际降雨强度、坡度、面积比、径流起始时间和初始含水率5个因子为输入变量、坡面产流量和产沙量为输出变量,利用BP神经网络模型与多元线性回归模型对数据进行模拟分析,并检验其模拟效果。研究结果表明:训练样本集平均相对误差为18.23%,预测样本集平均相对误差为5.21%;与多元线性回归模型相比,BP神经网络模型拟合精度较高,拟合效果更理想,表现出更强的预测能力。另外,比较不同土质坡面产流量与产沙量模拟效果,塿土优于黄绵土。从本研究的结果看,BP神经网络模型应用于坡面产流产沙模拟预测,省时省力,方便快捷,具有一定的应用潜力,但其实际的模拟预测能力尚需进一步探索。     

Optimization of water and nitrogen management for surge-root irrigated apple trees in the Loess Plateau of China

The Loess Plateau is one of the main regions for growing apple trees in China, but a shortage of water resources and low utilization of nitrogen have restricted its agricultural development. A 2-year field experiment was conducted which included three levels of soil water content(SWC), 90–75%, 75–60%, and 60–45% of field capacity, and five levels of nitrogen application(N_(app)), 0.7, 0.6, 0.5, 0.4 and 0.3 kg/plant. The treatments were arranged in a strip-plot design with complete randomized blocks with three replications. For both years, the water and N_(app) had significant(P0.05) effects on leaf area index(LAI), yield, water use efficiency(WUE) and nitrogen partial factor productivity(NPFP) while the interaction effect of water and N_(app) on yield, WUE and NPFP was significant(P0.05) in 2018, and not in 2017. For the same SWC level, WUE first increased, then decreased as N_(app) increased, while NPFP tended to decrease, but the trend of LAI with different N_(app) was closely related to SWC. At the same N_(app), the LAI increased as SWC increased, while the WUE and NPFP first increased, then decreased, but the yield showed different trends as the SWC increased. The dualistic and quadric regression equations of water and N_(app) indicate that the yield, WUE and NPFP cannot reach the maximum at the same time. Considering the coupling effects of water and N_(app) on yield, WUE and NPFP in 2017 and 2018, the SWC level shall be controlled in 75–60% of field capacity and the N_(app) is 0.45 kg/plant, which can be as the suitable strategy of water and N_(app) management for the maximum comprehensive benefits of yield, WUE and NPFP for apple trees in the Loess Plateau and other regions with similar environments.     

Assessing the impact of non-governmental organization's extension programs on sustainable cocoa production and household income in Ghana

Globally, cocoa is famous for the production of chocolates and beverages. However, it also serves as a source of raw material for the manufacturing of high-quality cosmetic and pharmaceutical products. Due to its high demand, cocoa is regarded as a major commodity for poverty alleviation in deprived cocoa farming communities. Government has therefore instigated relevant measures to address issues facing the sector by investing in extension programs. These programs act as information dissemination tools for teaching modern farming technologies and enhancing learning among farmers. This study highlighted the effects of the Cocoa Life Project(CLP) by Cooperative for Assistance and Relief Everywhere(CARE) International on farm productivity and income of 200 cocoa farmers in three districts of the Eastern Region, Ghana. Regression on covariates, Heckman's treatment effects model, and propensity scores were used to test the robustness of the estimates. A positive association between extension program participation, farm productivity, and household income was discovered. Specifically, after correcting for selection bias, the estimated results of the Heckman's model showed that the extension program significantly increased participating farmers' cocoa yield by 14.3%. The effects on total farm income showed a significant increase of 25.1 and 42.9%, respectively from regression on propensity scores and the Heckman's model. Across all three models, program participation led to an increase in total household income by 46.1, 31.7, and 69.3%, respectively. Other variables such as farming characteristics, institutional and demographic factors affected farmers' income, depending on the estimation method used. This study reiterated the supporting role played by extension programs in enhancing cocoa production and increasing household income in Ghana. Conclusively, government and development partners should invest in extension programs by providing enough logistics, training qualified agents, and creating a conducive atmosphere to support learning and technology adoption.     

Timing and temperature thresholds of heat stress effects on fertility performance of different parity sows in Spanish herds

High temperature is an environmental factor that impairs sow fertility. In this study, we identified the critical weeks for heat stress effects on aspects of fertility performance, namely weaning-to-first-service interval (WSI) and farrowing rate (FR). We also examined the threshold temperatures above which the fertility performance deteriorated and whether there were any differences between parities regarding heat stress effects or thresholds. Performance data of sows in 142 herds from 2011 to 2016 were matched to appropriate weekly averaged daily maximum temperatures (T_max) from weather stations close to the herds. Two types of ratios (i.e., ratio for WSI and odds ratio for FR) were used to identify the critical weeks for heat stress by comparing the respective measures for two sow groups based on T_max in different weeks around weaning or service events. The ratios for WSI were calculated between groups of sows exposed to T_max ≥ 27 °C or <27 °C in each week before weaning, with the T_max cutoff value based on a recent review study. Similarly, the odds ratios for FR for the two groups were calculated in weeks around service. The weeks with the largest differences in the fertility measures between the two T_max groups (i.e., the highest ratio for WSI and the lowest odds ratio for FR) were considered to be the critical weeks for heat stress. Also, piecewise models with different breakpoints were constructed to identify the threshold T_max in the critical week. The breakpoint in the best-fit model was considered to be the threshold T_max. The highest ratios for WSI were obtained at 1 to 3 wk before weaning in parity 1 and 2 or higher sow groups. The threshold T_max leading to prolonged WSI was 17 °C for parity 1 sows and 25 °C for parity 2 or higher sows. Increasing T_max by 10 °C above these thresholds increased WSI by 0.65, and 0.33 to 0.35 d, respectively (P < 0.01). For FR, the lowest odds ratios were obtained at 2 to 3 wk before service in parity 0, 1, and 2 or higher sow groups. The threshold T_max leading to reductions in FR was 20, 21, and 24 to 25 °C for parity 0, 1, and 2 or higher sow groups, respectively. Increasing T_max by 10 °C above these thresholds decreased FR by 3.0%, 4.3%, and 1.9% to 2.8%, respectively (P < 0.01). These results indicate that the critical weeks for heat stress were 2 to 3 wk before service for FR and 1 to 3 wk before weaning for WSI. The decreases in fertility performance in parity 0 to 1 sows started at temperatures 3 to 8 °C lower than in parity 2 or higher sows.