GROWTH AND YIELD MODELS FOR DAHURIAN LARCH PLANTATIONS

Growth and yield models were developed for individual tress and stands based on 336 temporary plots with 405 stem analysis trees of dahurian larch ( Larix gmelinii( Rupr. )Rupr.) plantations throughout Daxing'anling mountains. Several equations were selected using nonlinear regression analysis. Results showed that the Richards equation was the best model for estimating tree height, stand mean height and stand dominant height from age; The Power equation was the best model for prediction tree volume from DBH and tree height; The logarithmic stand volume equation was good for predicting stand volume from age, mean height, basal area and other stand variables. These models can be used to construct the volume table, the site index table and other forestry tables for dahurian larch plantations.

Weather Yield Model for the Semi Tropical Region (Pakistan)

Weather models are essential tools for checking of the effect of the weather elements in terms of their effect on the production of the crop. This research is an attempt to see the effect of only two variables i.e., temperature and rainfall for the division Faisalabad (semitropical region of Pakistan).The model fitted is of the linear form:the values of a,b, c have been found. The expected yield has been calculated by using the aridity indices (X1 and X2 ) and the result in the form of coefficient of determination R2 has been found equal to 0.166. The significance of the regression coefficient has been tested, which shows that the contribution to the yield from aridity index at germination and that at ripening is significant.The wheat yields are the results of a wide variety of variables, most of which show varying degree of relationship with one another, some positive and some negative in terms of output. These variables may be technology, fertilizers, pesticides, epidemics, kinds of seeds used, market price of crop and the area under cultivation etc, which can be the source of variation in the wheat yield. Since rainfall during germination and temperature at the ripening periods are the necessary factors for the yield of wheat, for this purpose these parameters have been studied in order to their contribution.

Growth and yield models for Dahurian larch plantations

Several equations were selected using nonlinear regression analysis for setting up growth and yield modehe of Dahurian larch (Laris gmelinii Rupr.) plantations. Data of 405 stem analysis trees were collected from 336 temporary plots throughout the Daxing’an Mountains. Results showed that the Richards equation was the best model for estimating tree height, stand mean height and stand dominant height by age, the Power equation was the fdiest model for predicting tree volume by DBH and tree height, and the Logarithmic stand vofume equation was good for predicting stand volume from age, mean height. basal area and other stand variables. These models can be used to construct volume tabIes, site index table and other forestry tables for Dahurian ghantations.

Sustainable fisheries in shallow lakes:an independent empirical test of the Chinese mitten crab yield model

Next to excessive nutrient loading,intensive aquaculture is one of the major anthropogenic impacts threatening lake ecosystems.In China,particularly in the shallow lakes of mid-lower Changjiang(Yangtze) River,continuous overstocking of the Chinese mitten crab(Eriocheir sinensis) could deteriorate water quality and exhaust natural resources.A series of crab yield models and a general optimum-stocking rate model have been established,which seek to benefit both crab culture and the environment.In this research,independent investigations were carried out to evaluate the crab yield models and modify the optimum-stocking model.Low percentage errors(average 47%,median 36%) between observed and calculated crab yields were obtained.Specific values were defined for adult crab body mass(135 g/ind.) and recapture rate(18%and 30%in lakes with submerged macrophyte biomass above and below 1 000 g/m^2)to modify the optimum-stocking model.Analysis based on the modified optimum-stocking model indicated that the actual stocking rates in most lakes were much higher than the calculated optimum-stocking rates.This implies that,for most lakes,the current stocking rates should be greatly reduced to maintain healthy lake ecosystems.

Yield Estimation for a Single Purpose Multi-Reservoir System Using LP Based Yield Model

Application of optimization techniques for determining the optimal operation policy for reservoir is a major area in water resources planning and management. Linear programming, ruled by evolution techniques, has become popular for solving optimization problems in diversified fields of science. An LP-based yield model (YM) has been used to reevaluate the annual yield available from the reservoirs for irrigation. This paper extends the basic yield model and presents a yield model for a multiple-reservoir system consisting of single-purpose reservoirs. Optimum yield of reservoirs system is calculated by yield model. The objective is to achieve prespecified reliability for irrigation and to incorporate an allowable deficit in the annual irrigation target. The yield model is applied to a system of two reservoirs in theManarRiverinIndia. This model can act as a better screening tool in planning by providing outputs that can be very useful in improving the efficiency and accuracy of detailed analysis methods such as simulation.

Identification of Failure Years by Modification of Yield Model in Isapur Reservoir, India

The failure years for a desired annual reliability of a water use were prespecified in the yield model. These failure years were identified prior to the solution of the model by observing the critical period in the given annual flow record. Some initial trials of the yield model were usually sufficient to identify the failure years. Simulation can be employed for exact identification of these failure years. A method for determination of failure years through the yield model is presented in this paper, as an alternative to simulation. The yield model employing this method is useful when the reliability of water use is to be considered as a decision variable. The method for determination of failure years by yield model is adopted for further analysis and compare with the simulation model, one of the failure year is not matching with the common set of failure years in the simulation model.

Evaluation of global gridded crop models in simulating sugarcane yield in China

中国是全球第三大甘蔗生产国,中国甘蔗产量模拟可服务于全球食糖和乙醇的生产和贸易.全球格点作物模式CLM5-crop和LPJmL已实现对甘蔗的模拟,但它们在中国的模拟能力未知.本文评估结果表明:两个模式均严重低估了甘蔗产量,模拟均不足观测的1/4.CLM5-crop能有技巧地模拟产量的空间分布特征,而LPJmL不能.两个模式均不能合理模拟产量的年际变化,且低估了产量的上升趋势.模式低估甘蔗产量的部分原因是模式假设收割的是甘蔗的穗而非茎.

Scratch-concerned yield modeling for IC manufacturing involved with a chemical mechanical polishing process

In existing integrated circuit (IC) fabrication methods,the yield is typically limited by defects generated in the manufacturing process.In fact,the yield often shows a good correlation with the type and density of the defect.As a result,an accurate defect limited yield model is essential for accurate correlation analysis and yield prediction.Since real defects exhibit a great variety of shapes,to ensure the accuracy of yield prediction,it is necessary to select the most appropriate defect model and to extract the critical area based on the defect model.Considering the realistic outline of scratches introduced by the chemical mechanical polishing (CMP) process,we propose a novel scratch-concerned yield model.A linear model is introduced to model scratches.Based on the linear model,the related critical area extraction algorithm and defect density distribution are discussed.Owing to higher correspondence with the realistic outline of scratches,the linear defect model enables a more accurate yield prediction caused by scratches and results in a more accurate total product yield prediction as compared to the traditional circular model.

Yield modeling of elliptical defect

Physical defects have always played an important role in integrated circuit(IC)yields,and the design sensitivity to these physical elements has continued to increase in today’s nanometer technologies.The modeling of defect out-lines that exhibit a great variety of defect shapes is usually modeled as a circle,which causes the errors of critical area estimation.Since the outlines of 70%defects approximate to elliptical shapes,a novel yield model associated with elliptical outlines of defects is presented.This model is more general than the circular defects model as the latter is only an instance of the proposed model.Comparisons of the new and circular models in the experiment show that the new model can predict yield caused by real defects more accurately than what the circular model does,which is of significance for the prediction and improvement of the yield.

Evaluation of Sediment Yield Predicting Models of Ghana

Fluvial sediment transport data is a very important data for effective water resource management.However,acquiring this data is expensive and tedious hence sediment yield modeling has become an alternative approach in estimating river sediment yields.In Ghana,several sediment yield predicting models have been developed to estimate the sediment yields of ungauged rivers including the Pra River Basin.In this paper,10 months sediment yield data of the Pra River Basin was used to evaluate the existing sediment yield predicting models of Ghana.A regression analysis between predicted sediment yield data derived from the models and the observed suspended sediment yields of the Pra Basin was done to determine the extent of estimation of observed sediment yields.The prediction of suspended sediment yield was done for 4 out of 5 existing sediment yield predicting models in Ghana.There were variations in sediment yield between observed and predicted suspended sediments.All predicted sediment yields were lower than observed data except for equation 3 where the results were mixed.All models were found to be good estimators of fluvial sediments with the best model being equation 4.Sediment yield tends to increase with drainage basin area.

Analysis and calculation model of energy consumption and product yields of delayed coking units

Delayed coking is an important process consumption and light oil yield are important factors used to convert heavy oils to light products. Energy for evaluating the delayed coking process. This paper analyzes the energy consumption and product yields of delayed coking units in China. The average energy consumption shows a decreasing trend in recent years. The energy consumption of different refineries varies greatly, with the average value of the highest energy consumption approximately twice that of the lowest energy consumption. The factors affecting both energy consumption and product yields were analyzed, and correlation models of energy consumption and product yields were established using a quadratic polynomial. The model coefficients were calculated through least square regression of collected industrial data of delayed coking units. Both models showed good calculation accuracy. The average absolute error of the energy consumption model was approximately 85 MJ/t, and that of the product yield model ranged from 1 wt% to 2.3 wt%. The model prediction showed that a large annual processing capacity and high load rate will result in a reduction in energy consumption.

Using ORYZA2000 to model cold rice yield response to climate change in the Heilongjiang province, China

Rice(Oryza sativa L.) is one of the most important staple crops in China. Increasing atmospheric greenhouse gas concentrations and associated climate change may greatly affect rice production. We assessed the potential impacts of climate change on cold rice production in the Heilongjiang province, one of China's most important rice production regions. Data for a baseline period(1961–1990) and the period 2010–2050 in A2 and B2 scenarios were used as input to drive the rice model ORYZA2000 with and without accounting for the effects of increasing atmospheric CO2 concentration. The results indicate that mean,maximum, and minimum temperature during the rice growing season, in the future period considered, would increase by 1.8 °C under the A2 scenario and by 2.2 °C under the B2 scenario compared with those in the baseline. The rate of change in average maximum and minimum temperatures would increase by 0.6 °C per 10-year period under the A2 scenario and by 0.4 °C per 10-year period under the B2 scenario. Precipitation would increase slightly in the rice growing season over the next 40 years. The rice growing season would be shortened and the yield would increase in most areas in the Heilongjiang province. Without accounting for CO2 effect, the rice growing season in the period 2010–2050 would be shortened by 4.7 and 5.8 days,and rice yields would increase by 11.9% and 7.9%, under the A2 and B2 scenarios, respectively.Areas with simulated rice yield increases greater than 30.0% were in the Xiaoxing'an Mountain region. The simulation indicated a decrease in yield of less than 15% in the southwestern Songnen Plain. The rate of change in simulated rice yield was 5.0% and 2.5% per 10 years under the A2 and B2 scenarios, respectively. When CO2 effect was accounted for, rice yield increased by 44.5% and 31.3% under the A2 and B2 scenarios, respectively. The areas of increasing yield were sharply expanded. The area of decreasing yield in the western region of Songnen Plains disappeared when increasing CO2 concentration was considered. The stability of rice yield would increase from 2010 to 2050. Overall, the simulation indicates that rice production will be affected positively by climate change in the next 40 years in the Heilongjiang province, China.

Optimal salinity for dominant copepods in the East China Sea, determined using a yield density model

From 1997 to 2000, four field surveys were conducted in the East China Sea (ECS) (23°30′-33°00′N, 118°30′-128°00′E). A field data yield density model was used to determine the optimal salinities for 19 dominant copepod species to establish the relationship between surface salinities and abundance of those species. In addition, ecological groups of the copepods were classified based on optimal salinity and geographical distribution. The results indicate that the yield density model is suitable for determining the relationship between salinity and abundance. Cosmocalanus darwini, Euchaeta rimana, Pleuromamma gracilis, Rhincalanus cornutus, Scolecithrix danae and Pareucalanus attenuatus were determined as oceanic species, with optimal salinities of >34.0. They were stenohaline and mainly distributed in waters influenced by the Kuroshio or Taiwan warm current. Temora discaudata, T. stylifera and Canthocalanus pauper were nearshore species with optimal salinities of <33.0 and most abundant in coastal waters. The remaining 10 species, including Undinula vulgaris and Subeucalanus subcrassus, were offshore species, with optimal salinity ranging from 33.0-34.0. They were widely distributed in nearshore, offshore and oceanic waters but mainly in the mixed water of the ECS.

Simple nonlinear model for the relationship between maize yield and cumulative water amount

Both the additive and multiplicative models of crop yield and water supply are polynomial equations, and the number of parameters increases linearly when the growing period is specified. However, interactions among multiple parameters occasionally lead to unreasonable estimations of certain parameters, which were water sensitivity coefficients but with negative value. Additionally, evapotranspiration must be measured as a model input. To facilitate the application of these models and overcome the aforementioned shortcomings, a simple model with only three parameters was derived in this paper based on certain general quantitative relations of crop yield （Y） and water supply （W）. The new model, Y/Y-W＊/（W＊＋w＊）, fits an S or a saturated curve of crop yield with the cumulative amount of water. Three parameters are related to biological factors： the yield potential （Y＊）, the water requirement to achieve half of the yield potential （half-yield water requirement, wh）, and the water sensitivity coefficient （k）. The model was validated with data from 24 maize lines obtained in the present study and 17 maize hybrids published by other authors. The results showed that the model was well fit to the data, and the normal root of the mean square error （NRMSE） values were 2.8 to 17.8% （average 7.2%） for the 24 maize lines and 2.7 to 12.7% （average 7.4%） for the 17 maize varieties. According to the present model, the maize water-sensitive stages in descending order were pollen shedding and silking, tasselling, jointing, initial grain filling, germination, middle grain filling, late grain filling, and end of grain filling. This sequence was consistent with actual observations in the maize field. The present model may be easily used to analyse the water use efficiency and drought tolerance of maize at specific stages.

Growth simulation and yield prediction for perennial jujube fruit tree by integrating age into the WOFOST model

Mathematical models have been widely employed for the simulation of growth dynamics of annual crops,thereby performing yield prediction,but not for fruit tree species such as jujube tree(Zizyphus jujuba).The objectives of this study were to investigate the potential use of a modified WOFOST model for predicting jujube yield by introducing tree age as a key parameter.The model was established using data collected from dedicated field experiments performed in 2016-2018.Simulated growth dynamics of dry weights of leaves,stems,fruits,total biomass and leaf area index(LAI) agreed well with measured values,showing root mean square error(RMSE) values of 0.143,0.333,0.366,0.624 t ha^-1 and 0.19,and R2 values of 0.947,0.976,0.985,0.986 and 0.95,respectively.Simulated phenological development stages for emergence,anthesis and maturity were 2,3 and 3 days earlier than the observed values,respectively.In addition,in order to predict the yields of trees with different ages,the weight of new organs(initial buds and roots) in each growing season was introduced as the initial total dry weight(TDWI),which was calculated as averaged,fitted and optimized values of trees with the same age.The results showed the evolution of the simulated LAI and yields profiled in response to the changes in TDWI.The modelling performance was significantly improved when it considered TDWI integrated with tree age,showing good global(R2≥0.856,RMSE≤0.68 t ha^-1) and local accuracies(mean R2≥0.43,RMSE≤0.70 t ha^-1).Furthermore,the optimized TDWI exhibited the highest precision,with globally validated R2 of 0.891 and RMSE of 0.591 t ha^-1,and local mean R2 of 0.57 and RMSE of 0.66 t ha^-1,respectively.The proposed model was not only verified with the confidence to accurately predict yields of jujube,but it can also provide a fundamental strategy for simulating the growth of other fruit trees.

Rice Yield Estimation by Integrating Remote Sensing with Rice Growth Simulation Model

Since remote sensing can provide information on the actual status of an agricultural crop, the integration between remote sensing data and crop growth simulation models has become an important trend for yield estimation and prediction.The main objective of this research was to combine a rice growth simulation model with remote sensing data to estimate rice grain yield for different growing seasons leading to an assessment of rice yield at regional levels. Integration between NOAA (National Oceanic and Atmospheric Administration) AVHRR (Advanced Very High Resolution Radiometer) data and the rice growth simulation model ORYZA1 to develop a new software, which was named as Rice-SRS Model, resulted in accurate estimates for rice yield in Shaoxing, China, with an estimation error reduced to 1.03% and 0.79% over-estimation and 0.79% under-estimation for early, single and late season rice, respectively. Selecting suitable dates for remote sensing images was an important factor which could influence estimation accuracy. Thus, given the different growing periods for each rice season, four images were needed for early and late rice, while five images were preferable for single season rice.Estimating rice yield using two or three images was possible, however, if images were obtained during the panicle initiation and heading stages.

Climate Change Modelling and Its Roles to Chinese Crops Yield

Climate has been changing in the last fifty years in China and will continue to change regardless any efforts for mitigation. Agriculture is a climate-dependent activity and highly sensitive to climate changes and climate variability. Understanding the interactions between climate change and agricultural production is essential for society stable development of China. The first mission is to fully understand how to predict future climate and link it with agriculture production system. In this paper, recent studies both domestic and international are reviewed in order to provide an overall image of the progress in climate change researches. The methods for climate change scenarios construction are introduced. The pivotal techniques linking crop model and climate models are systematically assessed and climate change impacts on Chinese crops yield among model results are summarized. The study found that simulated productions of grain crop inherit uncertainty from using different climate models, emission scenarios and the crops simulation models. Moreover, studies have different spatial resolutions, and methods for general circulation model （GCM） downscaling which increase the uncertainty for regional impacts assessment. However, the magnitude of change in crop production due to climate change （at 700 ppm CO2 eq correct） appears within ±10% for China in these assessments. In most literatures, the three cereal crop yields showed decline under climate change scenarios and only wheat in some region showed increase. Finally, the paper points out several gaps in current researches which need more studies to shorten the distance for objective recognizing the impacts of climate change on crops. The uncertainty for crop yield projection is associated with climate change scenarios, CO2 fertilization effects and adaptation options. Therefore, more studies on the fields such as free air CO2 enrichment experiment and practical adaptations implemented need to be carried out.

Modeling the Impacts of Soil Organic Carbon Content of Croplands on Crop Yields in China

This study quantified the impacts of soil organic carbon （SOC） content on the grain yield of crops using a biogeochemical model （DNDC, denitrification-decomposition）. Data on climate, soil properties, and farming management regimes of cropping systems were collected from six typical agricultural zones （northeast, north, northwest, mid-south, east and southwest regions of China, respectively） and integrated into a GIS database to support the model runs. According to the model, if the initial SOC content in the cropland was increased by 1 g C kg^-1, the crop yield may be increased by 176 kg ha^-1 for maize in the northeast region, 454 kg ha^-1 for a maize-wheat rotation in the north region, 328 kg ha^-1 for maize in the northwest region, 185 kg ha^-1 for single-rice in the mid-south region, 266 kg ha^-1 for double-rice in east region, and 229 kg ha^-1 for rice and wheat rotation in southwest region. There is a great potential for enhancing the crop yield by improving the SOC content in each region of China.

Assimilation of temporal-spatial leaf area index into the CERES-Wheat model with ensemble Kalman filter and uncertainty assessment for improving winter wheat yield estimation

To accurately estimate winter wheat yields and analyze the uncertainty in crop model data assimilations, winter wheat yield estimates were obtained by assimilating measured or remotely sensed leaf area index （LAI） values. The performances of the calibrated crop environment resource synthesis for wheat （CERES-Wheat） model for two different assimilation scenarios were compared by employing ensemble Kalman filter （EnKF）-based strategies. The uncertainty factors of the crop model data assimilation was analyzed by considering the observation errors, assimilation stages and temporal-spatial scales. Overalll the results indicated a better yield estimate performance when the EnKF-based strategy was used to comprehen- sively consider several factors in the initial conditions and observations. When using this strategy, an adjusted coefficients of determination （R2） of 0.84, a root mean square error （RMSE） of 323 kg ha-1, and a relative errors （RE） of 4.15% were obtained at the field plot scale and an R2 of 0.81, an RMSE of 362 kg ha-1, and an RE of 4.52% were obtained at the pixel scale of 30 mx30 m. With increasing observation errors, the accuracy of the yield estimates obviously decreased, but an acceptable estimate was observed when the observation errors were within 20%. Winter wheat yield estimates could be improved significantly by assimilating observations from the middle to the end of the crop growing seasons. With decreasing assimilation frequency and pixel resolution, the accuracy of the crop yield estimates decreased; however, the computation time decreased. It is important to consider reasonable temporal-spatial scales and assimilation stages to obtain tradeoffs between accuracy and computation time, especially in operational systems used for regional crop yield estimates.

The impacts of climate change on wheat yield in the Huang-Huai-Hai Plain of China using DSSAT-CERES-Wheat model under different climate scenarios

Climate change has been documented as a major threat to current agricultural strategies.Progress in understanding the impact of climate change on crop yield is essential for agricultural climate adaptation,especially for the Huang-Huai-Hai Plain(3H Plain)of China which is an area known to be vulnerable to global warming.In this study,the impacts of climate change on winter wheat(Triticum aestivum L.)yield between the baseline period(1981–2010)and two Representative Concentration Pathways(RCP8.5 and RCP4.5)were simulated for the short-term(2010–2039),the medium-term(2040–2069)and the long-term(2070–2099)in the 3H Plain,by considering the relative contributions of changes in temperature,solar radiation and precipitation using the DSSAT-CERES-Wheat model.Results indicated that the maximum and minimum temperatures(TMAX and TMIN),solar radiation(SRAD),and precipitation(PREP)during the winter wheat season increased under these two RCPs.Yield analysis found that wheat yield increased with the increase in SRAD,PREP and CO2 concentration,but decreased with an increase in temperature.Increasing precipitation contributes the most to the total impact,increasing wheat yield by 9.53,6.62 and 23.73%for the three terms of future climate under RCP4.5 scenario,and 11.74,16.38 and 27.78%for the three terms of future climate under RCP8.5 scenario.However,as increases in temperature bring higher evapotranspiration,which further aggravated water deficits,the supposed negative effect of increasing thermal resources decreased wheat yield by 1.92,4.08 and 5.24%for the three terms of future climate under RCP4.5 scenario,and 3.64,5.87 and 5.81%for the three terms of future climate under RCP8.5 scenario with clearly larger decreases in RCP8.5.Counterintuitively,the impacts in southern sub-regions were positive,but they were all negative in the remaining sub-regions.Our analysis demonstrated that in the 3H Plain,which is a part of the mid-high latitude region,the effects of increasing thermal resources were counteracted by the aggravated water deficits caused by the increase in temperature.