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福建省农民人均纯收入的组合预测分析 总被引：1，自引：1，他引：0
利用福建省1978-2011年的农民人均纯收入数据,在建立ARMA模型、Holt-Winters非季节模型、二次曲线模型、灰色GM(1,1)模型的基础上,以各个模型预测的MAPE调和平均数为权重建立组合预测模型,对福建省2012-2015年的农民人均纯收入进行预测。结果表明,组合预测的MAPE值为0.1821%,高于各个单项预测模型。且在2012-2015年期间,年均增长率达到10.85%。 相似文献
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组合预测模型在东北地区粮食产量预测中的应用 总被引：1，自引：0，他引：1
采用最优加权方法,建立了基于灰色预测模型、灰色马尔科夫预测模型及逻辑斯蒂预测模型的组合模型;并根据东北地区1949-2008年粮食产量资料,利用组合模型预测了该地区未来10年的粮食产量.结果得到,灰色预测、马尔科夫预测、逻辑斯蒂预测和组合预测方法的预测粮食产量的平均相对百分误差分别为:12.74%,3.02%,13.29%,2.87%,结果证明组合预测模型可以较好地提高粮食产量的预测精度.通过组合模型预测结果表明,到2015年东北地区的粮食产量可以达到1.25亿t,可以完成该地区增产150亿kg粮食的任务,到2018年,粮食产量预计可达1.38亿t,东北地区增粮潜力巨大. 相似文献
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本文通过对运城市2005—2018年气象数据和苹果年产量数据进行分析,构建运城市苹果产量早期预测模型。首先,采用HP滤波法将运城市苹果年产量分为趋势产量和气象产量。其次,分别对苹果物候期：发芽期、花期、幼果期、膨果期、成熟期建立多元线性回归模型,研究每个物候期对苹果气象产量影响的强弱。最后,选取对苹果气象产量影响最强的幼果期建立BP神经网络早期预测模型,并对其进行验证。结果表明：选取幼果期建立的BP神经网络苹果产量早期预测模型其预测结果相对平均误差为7.08%,使用2019年相关数据验证BP神经网络产量早期预测模型的精度为89.6%,表明该模型能够较为准确的预测苹果产量,可为农作物产量早期预测提供理论支持。 相似文献
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陕西省苹果单产非线性预测模型研究 总被引：2，自引：1，他引：1
为了更好地服务陕西省苹果产业发展,客观定量预测陕西省苹果产量,为政府部门制定苹果产销及产业发展提供科学决策依据,选择影响苹果产量的主要气候资源为预测因子,利用非线性回归分析方法,对陕西省5个苹果主要生产基地县分别建立单产气象因子非线性回归预测模型。模型检验显示,5县各年苹果单产预测的准确率均达73%以上,且该模式能够较准确地反映各县苹果单产情况,预测效果较好。该预测模型是对苹果产量预测技术方法的首次尝试,思路清晰、可操作性强,可为其他省份建立苹果产量预测方法及其他经济林果产量预测方法提供借鉴。 相似文献
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基于logistic和灾减率方法制作玉米产量的预测 总被引：2，自引：2，他引：0
利用多元回归预报模型与灾减率相结合探讨粮食产量预报方法。依据本溪县玉米单产和气候资料,利用logistic方法建立玉米趋势产量序列,将分离的气象产量转换为相对气象产量,进行相关筛选预报因子,组建预测模型。结果表明：运用logistic方法构建的玉米趋势产量序列,提高了趋势产量拟合的精度;选取气象因子具有一定的生物学意义,增强了预测模型的科学性;多元回归预测模型与灾减率订正预测玉米单产正确率达90%。为粮食产量预报的定量化和精细化提供科学的依据。 相似文献
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基于单纯形法的加工番茄种植规划研究 总被引：4，自引：1，他引：3
为了解决加工番茄种植与订单生产之间需求不平衡的问题,本文运用单纯形法建立番茄种植面积与番茄酱产量之间的数学模型,提出通过合理安排不同品种番茄种植比例,使番茄酱厂在现有条件下番茄酱加工实现产能、效率最大化。以新疆某番茄酱厂及当地番茄种植为例,通过将番茄酱实际产量和规划产量进行对比,提出种植计划建议。该方法在农产品种植规划方面具有应用价值和实际意义。 相似文献
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基于SPSS的日照市小麦产量年景预测模型 总被引：8，自引：2，他引：6
摘 要：根据1979-2008年日照市气象资料和小麦产量资料,运用数理统计的方法进行筛选,采用SPSS统计软件对小麦产量有影响的气候因子进行相关性分析,筛选出2个相关性较高的因子,建立多元回归模型。并且对历年产量进行检验,计算表明小麦预测产量与实际产量拟合率较高,预测精度最高为100%,最低为82%,平均精度为90%。预测模型具有较高的信度和实用性,可作为小麦产量定量预报的有效工具之一,为农业生产管理和农产品流通贸易提供决策依据。 相似文献
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【目的】分析中国马铃薯产业发展潜力,为马铃薯产业发展决策和粮食安全提供参考。【方法】应用时间序列预测法,从马铃薯产量潜力、市场发展潜力、加工业发展潜力和贸易发展潜力四方面来预测2020年中国马铃薯产业发展的潜力。【结果】从世界单产水平和趋势单产看2020年马铃薯产量分别达28518.72 万t 和11602.67万t;马铃薯人均消费量和总消费量分别达53.12 kg/人和11686.68万t;马铃薯加工量达1152.11万t;马铃薯出口额和进口额分别达40770.08万美元和27642.9万美元。【结论】中国马铃薯产量、消费量、加工量、进出口额都继续呈上身趋势,未来中国马铃薯产业发展具有较大的发展潜力。 相似文献
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利用环境减灾卫星估测增城水稻产量 总被引：1，自引：1，他引：0
为了探讨将国产环境减灾卫星遥感影像应用于田块破碎度大,生长季多云、雨天气的增城地区水稻产量估测的可行性。试验于2010年在增城地区进行,获取了水稻生长季长势、产量信息,及多时相环境减灾卫星遥感影像,提取了水稻种植面积信息,并基于“光谱信息-长势-产量”间相互关系,利用主成分分析算法建立水稻产量估测模型。结果表明,国产环境减灾卫星的特点可使其有效获取研究区水稻遥感影像,便于准确提取水稻种植面积及估测产量。本研究获得的2010年早稻种植面积的提取精度在97.3%,估产模型的预测决定系数为0.73,预测相对误差为12%。推动了国产卫星在该区域的应用。 相似文献
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Based on the gray forecast theory, this paper studies the principle and deficiency in power load forecasting by the basic grey model and other improved models, and introduces a new method -the combination grey model to forecast the long-medium power load. Based on an example, the basic grey model, other improved models and combination grey model are used to forecast power load and results of all models are analyzed and compared. The calculation results show that forecasting power load by grey theory is credible and simple. For this type of complex problems such as forecasting the long-medium power load, the combination grey model is specially useful because of it's high precision and facility. The method can be used as one of the tools of forecasting the long-medium power load. 相似文献
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为了研究浮山县7—9月夏闲期降水量对翌年冬小麦产量的影响,做出翌年小麦产量的趋势预报,提出相应措施和对策,指导小麦生产趋利避害。通过对山西省浮山县1980—2009年7—9月夏闲期降水量与翌年冬小麦产量的分析,利用SPSS软件,分离趋势产量和气象产量,建立小麦趋势产量回归方程;通过气象产量与夏闲期降水量的分析,建立两者的对数方程,最终建立小麦产量预报模型：Y=2.995T+68.102lnX-254.578。结果表明：浮山县1980—2009年7—9月夏闲期降水量与翌年冬小麦产量拟合率较高,预测精度最高为99%,平均预测精度为80%。通过研究得出：浮山县7—9月降水量与翌年冬小麦产量相关性显著,预测精度较高,但因未考虑到小麦生育期降水量的影响,故如遇较大范围严重自然灾害时,预测精度会大大降低。 相似文献
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基于气候适宜指数的湖南早稻产量动态预报 总被引：1，自引：0，他引：1
早稻生长发育和产量形成过程与气象条件密切相关,开展早稻产量动态预报对湖南农业生产和粮食安全具有重要意义。从湖南早稻生长发育的上限温度、最适温度、下限温度、需水量、需光特性等生物学特性出发,建立湖南早稻气候适宜度模型,采用权重系数的方法构建湖南早稻气候适宜指数。选取15个代表站点,统计分析1961—2009年不同时段湖南早稻气候适宜指数与产量丰歉值的关系,建立了基于气候适宜指数的湖南早稻产量动态预报模型,并从模型预测值与实测值增减趋势一致的样本百分率、预报准确率等方面进行回代检验。利用2010—2012年资料进行外推检验。检验结果表明,建立的预报时间为4月30日、5月20日和6月20日的基于气候适宜指数的湖南早稻产量预报模型预报值与实测值增减趋势一致的样本百分率为64%~73%,预报准确率平均为94.7%~96.3%。研究结果表明,建立的基于气候适宜指数的早稻产量动态预报模型,能够满足湖南早稻产量预报业务服务的需要。 相似文献
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Cecilia Manuela Tojo Soler Paulo Csar Sentelhas Gerrit Hoogenboom 《European Journal of Agronomy》2007,27(2-4):165-177
In recent years, maize has become one of the main alternative crops for the Autumn–Winter growing season (off-season) in several regions of Brazil. Water deficits, sub-optimum temperatures and low solar radiation levels are some of the more common problems that are experienced during this growing season. However, the impact of variable weather conditions on crop production can be analyzed with crop simulation models. The objectives of this study were to evaluate the Cropping System Model (CSM)-CERES-Maize for its ability to simulate growth, development, grain yield for four different maturity maize hybrids grown off-season in a subtropical region of Brazil, to study the impact of different planting dates on maize performance under rainfed and irrigated conditions, and for yield forecasting for the most common off-season production system. The CSM-CERES-Maize model was evaluated with experimental data collected during three field experiments conducted in Piracicaba, SP, Brazil. The experiments were completely randomized with three replications for the 2001 experiment and four replications for the 2002 experiments. For the yield forecasting application, daily weather data for 2002 were used until the forecast date, complemented with 25 years of historical daily weather data for the remainder of the growing season. Six planting dates were simulated, starting on February 1 and repeated every 15 days until April 15. The evaluation of the CSM-CERES-Maize showed that the model was able to simulate phenology and grain yield for the four hybrids accurately, with normalized RMSE (expressed in percentage) less than 15%. The planting date analysis showed that a delayed planting date from February 1 to April 15 caused a decrease in average yield of 55% for the rainfed and 21% for the irrigated conditions for all hybrids. The yield forecasting analysis demonstrated that an accurate yield forecast could be provided at approximately 45 days prior to the harvest date for all four maize hybrids. These results are promising for farmers and decision makers, as they could have access to accurate yield forecasts prior to final harvest. However, to be able to make practical decisions for stock management of maize grains, it is necessary to develop this methodology for different locations. Future model evaluations might also be needed due to the release of new cultivars by breeders. 相似文献
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P. Carli G. Caruso V. Fogliano D. Carputo L. Frusciante M. R. Ercolano 《Euphytica》2011,180(3):291-300
Tomato is a food of great relevance both due to its large-scale consumption and its richness in bioactive components. It is
an important component of the traditional Mediterranean diet as well as of other diets. Nowadays, concerns about human health
and environment preservation have changed the objectives of tomato breeding. In this study, eight tomato F1 hybrids and their parental lines were analyzed for nutritional properties and agronomic traits using a new selection method
that combines biochemical and agronomic evaluation. Eight traits contributing to the nutritional quality of tomato (lycopene,
β-carotene, other carotenoids, flavonoids, phenolic acids, vitamins C and E, dry residue) and average yield were assessed
in fifteen tomato genotypes. Furthermore the pathogen resistances possessed from these genotypes were, also, considered. In
order to select valuable tomato hybrids, a nutritional index (IQUAN) and an agronomic index (AI) were calculated. Our results suggested that the IQUAN nutritional index may be very useful to forecast the nutritional value of F1 hybrids based on parental performance. Combining the use of the IQUAN and AI indexes, we were able to select two hybrids (MR 48 and MR 47) that contain considerable amounts of antioxidants and
acceptable parameters for commercial production. 相似文献
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《European Journal of Agronomy》2008,28(2-4):165-177
In recent years, maize has become one of the main alternative crops for the Autumn–Winter growing season (off-season) in several regions of Brazil. Water deficits, sub-optimum temperatures and low solar radiation levels are some of the more common problems that are experienced during this growing season. However, the impact of variable weather conditions on crop production can be analyzed with crop simulation models. The objectives of this study were to evaluate the Cropping System Model (CSM)-CERES-Maize for its ability to simulate growth, development, grain yield for four different maturity maize hybrids grown off-season in a subtropical region of Brazil, to study the impact of different planting dates on maize performance under rainfed and irrigated conditions, and for yield forecasting for the most common off-season production system. The CSM-CERES-Maize model was evaluated with experimental data collected during three field experiments conducted in Piracicaba, SP, Brazil. The experiments were completely randomized with three replications for the 2001 experiment and four replications for the 2002 experiments. For the yield forecasting application, daily weather data for 2002 were used until the forecast date, complemented with 25 years of historical daily weather data for the remainder of the growing season. Six planting dates were simulated, starting on February 1 and repeated every 15 days until April 15. The evaluation of the CSM-CERES-Maize showed that the model was able to simulate phenology and grain yield for the four hybrids accurately, with normalized RMSE (expressed in percentage) less than 15%. The planting date analysis showed that a delayed planting date from February 1 to April 15 caused a decrease in average yield of 55% for the rainfed and 21% for the irrigated conditions for all hybrids. The yield forecasting analysis demonstrated that an accurate yield forecast could be provided at approximately 45 days prior to the harvest date for all four maize hybrids. These results are promising for farmers and decision makers, as they could have access to accurate yield forecasts prior to final harvest. However, to be able to make practical decisions for stock management of maize grains, it is necessary to develop this methodology for different locations. Future model evaluations might also be needed due to the release of new cultivars by breeders. 相似文献
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Determination to Weighting Coefficient of Combination Forecast Based on Rough Set Theory 总被引：1，自引：0，他引：1
The problem of determination to weighting coefficient is a key and difficulty for combination forecast. A method of determining weighting coefficient based on rough set theory is showed in this paper. Determining weighting coefficient is translated into estimating significance of attributes among rough set. A relation data model about combination forecast is established. Knowledge systems are built through making attribute value into eigenvalue. Under data moving, the weighting coefficients of a combination forecast model are computed by analyzing the dependence and significance of forecasting method for the predicted object. The proposed approach overcomes the subjectivity of traditional determination to weighting coefficient, avoids computing linear or nonlinear extremum problem and makes combination forecast more objective. The validity of the proposed approach is verified with a case. 相似文献