Genetic and Agronomic Parameter Estimates of Growth, Yield and Related Traits of Maize (Zea mays L.) under Different Rates of Nitrogen Fertilization

This study evaluated the genetic and agronomic parameter estimates of maize under different nitrogen rates. The trial was established at the Njala Agricultural Research Centre experimental site during 2021 and 2022 in a split block design with three maize varieties (IWCD2, 2009EVDT, and DMR-ESR-Yellow) and seven nitrogen (0, 30, 60, 90, 120, 150 and 180 kg∙N∙ha−1) rates. Findings showed that cob diameter and anthesis silking time (ASI) had intermediate heritability, ASI had high genetic advance, ASI and grain yield had high genotypic coefficient of variation (GCV), while traits with high phenotypic coefficient of variation (PCV) were plant height, ASI, grain yield, number of kernel per cob, number of kernel rows, ear length, and ear height. The PCV values were higher than GCV, indicating the influence of the environment in the studied traits. Nitrogen rates and variety significantly (p < 0.05) influenced grain yield production. Mean grain yields and economic parameter estimates increased with increasing nitrogen rates, with the 30 and 180 kg∙N∙ha−1 plots exhibiting the lowest and highest grain yields of 1238 kg∙ha−1 and 2098 kg∙ha−1, respectively. Variety and nitrogen effects on partial factor productivity (PFPN), agronomic efficiency (AEN), net returns (NR), value cost ratio (VCR) and marginal return (MR) indicated that these parameters were significantly affected (p < 0.05) by these factors. The highest PFPN (41.3 kg grain kg−1∙N) and AEN (29.4 kg grain kg−1∙N) were obtained in the 30 kg∙N∙ha−1 plots, while the highest VCR (2.8) and MR (SLL 1.8 SLL−1 spent on N) were obtained in the 180 kg∙N∙ha−1. The significant influence of variety and nitrogen on traits suggests that increasing yields and maximizing profits require use of appropriate nitrogen fertilization and improved farming practices that could be exploited for increased productivity of maize.

Winter Wheat Yield Estimation Based on Sparrow Search Algorithm Combined with Random Forest:A Case Study in Henan Province,China

Precise and timely prediction of crop yields is crucial for food security and the development of agricultural policies.However,crop yield is influenced by multiple factors within complex growth environments.Previous research has paid relatively little attention to the interference of environmental factors and drought on the growth of winter wheat.Therefore,there is an urgent need for more effective methods to explore the inherent relationship between these factors and crop yield,making precise yield prediction increasingly important.This study was based on four type of indicators including meteorological,crop growth status,environmental,and drought index,from October 2003 to June 2019 in Henan Province as the basic data for predicting winter wheat yield.Using the sparrow search al-gorithm combined with random forest(SSA-RF)under different input indicators,accuracy of winter wheat yield estimation was calcu-lated.The estimation accuracy of SSA-RF was compared with partial least squares regression(PLSR),extreme gradient boosting(XG-Boost),and random forest(RF)models.Finally,the determined optimal yield estimation method was used to predict winter wheat yield in three typical years.Following are the findings:1)the SSA-RF demonstrates superior performance in estimating winter wheat yield compared to other algorithms.The best yield estimation method is achieved by four types indicators’composition with SSA-RF)(R^(2)=0.805,RRMSE=9.9%.2)Crops growth status and environmental indicators play significant roles in wheat yield estimation,accounting for 46%and 22%of the yield importance among all indicators,respectively.3)Selecting indicators from October to April of the follow-ing year yielded the highest accuracy in winter wheat yield estimation,with an R^(2)of 0.826 and an RMSE of 9.0%.Yield estimates can be completed two months before the winter wheat harvest in June.4)The predicted performance will be slightly affected by severe drought.Compared with severe drought year(2011)(R^(2)=0.680)and normal year(2017)(R^(2)=0.790),the SSA-RF model has higher prediction accuracy for wet year(2018)(R^(2)=0.820).This study could provide an innovative approach for remote sensing estimation of winter wheat yield.yield.

Genetic Variation and Heritability Estimates for Bunch Yield, Bunch Components and Vegetative Traits in Oil Palm Interspecific Hybrids

Progeny testing of oil palm interspecific hybrids was conducted in four trials in Kluang, Ulu Paka, Teluk Intan and Carey Island, Malaysia. The interspecific hybrids （O × P） were created according to the North Carolina Model I （NCM I） mating design, using Elaeis oleifera （0） and Elaeis guineensis var. pisifera （P） as the maternal and paternal parents, respectively. Differences among O, P and O-within-P were determined by the performance （bunch yield, components and vegetative traits） of the progenies. There were significant differences among P for fresh fruit bunch （FFB） yield, bunch number （BN） and average bunch weight （ABW） in Kluang, Ulu Paka and Teluk Intan, but not in Carey Island. FFB yield was generally higher on coastal soils （Teluk Intan and Carey Island） than inland soils （Kluang and Ulu Paka）. Heritability was calculated based on the intraclass correlation. Heritability estimates for these three yield components were variable, depending on the breeding material and environment in which the materials were tested. Fruit to bunch （F/B） and oil to bunch （O/B） of parthenocarpic fruits were important in determining the overall O/B of the interspecific hybrids. The O x P hybrids in Kluang showed the lowest height increment with only a mean of 14.0 cm/year, whereas in Ulu Paka and Teluk Intan, the values were higher at 24.0 cm/year and 25.0 cm/year, respectively. The study showed that the FFB yields ofoil palm interspecific hybrids performed better in coastal soils than inland soils.

Comparison on Winter Wheat Yield Estimating Models Based on Radarsat-2 and HJ Satellite in Huaihe River Region

The establishment of crop yield estimating model based on microwave and optical satellite images can conduct the mutual verification of the accuracy of the reported crop yield and the precision of the estimating model. With Shou County and Huaiyuan County of Anhui Province as the experimental fields of winter wheat producing areas, the linear winter wheat yield estimating models were established by adopting backscattering coefficient and Normalized Difference Vegetation Index（NDVI） based on images from the synthetic aperture radar（SAR）—RDARSAT-2 and HJ satellite photographed in mid-April and early May, 2014, and then comparisons were conducted on the accuracy of the yield estimating models. The accuracies of the yield estimating models established using co-polarized（HH） and cross-polarized（HV） modes of SAR in Jiangou Town, Shou County were 68.37% and 74.01%, respectively, while the accuracies in Longkang Town, Huaiyuan County were 63.10%and 69.10%, respectively. Accuracies of yield estimating models established by HJ satellite data were 69.52% and 66.43% in Shou County and Huaiyuan County, respectively. Accuracies of winter yield estimating model based on HJ satellite data and that based on SAR were closed, and the yield difference of winter wheat in the lodging region was analyzed in detail. The model results laid the foundation and accumulated experience for the verification, parameters correction and promotion of the winter wheat yield estimating model.

Loss Rate Estimation of Yield and Output Value of Tobacco Leaf Infected by Tobacco Blown Spot (Alternaria alternata)

[Objective] The paper was to study the effect of tobacco blown spot on the yield and output value of tobacco leaf.[Method]The upper,middle and lower leaves in tobacco plant were selected during the harvest period of tobacco to carry out loss rate estimation of yield and output value of tobacco leaf caused by different disease levels of brown spot.Regression correlation analysis was also conducted.[Result]The disease levels of brown spot had extremely significant strong negative correlation with single leaf weight of tobacco leaf,and it had extremely significant strong positive correlation with the loss rate of single leaf weight.The increase speed of loss rate of single leaf weight of middle and upper leaves was obviously faster than that of lower leaves.The loss rates of single leaf weight of upper,middle and lower leaves were 3.18%-28.95%,3.43%-28.88% and 10.07%-26.90%,respectively.The higher the disease level of blown spot was,the lower the yield and output value of tobacco leaf was,and the corresponding loss rate was also higher.Correlation analysis showed that the disease level of blown spot had extremely significant strong negative correlation with the yield and output value of tobacco leaf,and it had extremely significant strong positive correlation with the loss rate of yield and output value.The negative impact of blown spot on the output value of tobacco leaf was far greater than that on the yield.The highest loss rate of the yield of tobacco leaf was 28.56%,while the highest loss rate of output value reached 89.67%.[Conclusion] The study provided theoretical basis for accurately holding the critical period for the control of blown spot,thus reducing the damage on tobacco leaf and improving the output value of tobacco leaf.

Yield Estimation Model of Citrus Based on Spectral Data and Agronomic Parameters

With the development of precision agriculture, the research that applies Remote Sensing technology, especially hyperspectral remote sensing, to realize crop management, monitoring and yield estimation, has been concerned. Nowadays, the growth-monitoring and yield-estimating methods in rice, wheat and other annual crops develop rapidly with some achievements having already been put into service. But the yield estimation research on perennial economic crops is few. Taking peren- nial citrus trees as the research object, using ASD spectrometer to collect citrus canopy spectral, this article studied and analyzed the citrus of veget＆tion index and its relationship on yield, synthetically considered the influence of the agriculture pa- rameters on crop yield, and finally constructed the citrus yield estimation model based on the spectral data and agronomic parameters. Through the Significance Test and Samples＇ Test, olutained that the model＇s fitting degree was R=0.631, F= 13.201, P〈0.01 and the error rate of estimating accuracy was controlled in the range 3%-16%, proving that the model has statistical signification and reliability. It concluded that hyperspectral acquired from citrus canopy has substantial potential for citrus yield estimation. This study is an application and exploration of Hyperspectral Remote Sensing technology in the citrus yield estimation.

Estimating Rice Yield by HJ-1A Satellite Images

As illustrated by the case of Xuyi County, Jinhu County and Hongze County in Jiangsu Province, China, monitoring and forecasting of rice production were carried out by using HJ-1A satellite remote sensing images. The handhold GPS machines were used to measure the geographical position and some other information of these samples such as area shape. The GPS data and the interpretation marks were used to correct H J-1 image, assist human-computer interactive interpretation, and other operations. The test data had been participated in the whole classification process. The accuracy of interpreted information on rice planting area was more than 90% By using the leaf area index from the normalized difference vegetation index inversion, the biomass from the ratio vegetation index inversion, and combined with the rice yield estimation model, the rice yield was estimated. Further, the thematic map of rice production classification was made based on the rice yield data. According to the comparison results between measured and fitted values of yields and areas of sampling sites, the accuracy of the yield estimation was more than 85%. The results suggest that HJ-A/B images could basically meet the demand of rice growth monitoring and yield forecasting, and could be widely applied to rice production monitoring.

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.

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.

Regionalization for Rice Yield Estimation by Remote Sensing in Zhejiang Province

In order to provide a scientific basis for rice yield estimation and improve the accuracy of yield estimation in Zhejiang Province, regionalization indices for rice yield estimation by remote sensing (RS) in the province were determined by considering the special features of yield estimation by RS, and based on analysis of the natural conditions of Zhejiang Province. The indices determined included rice cropping system, agroclimate, landform, surface feature structure and rice yield level, where rice planting system was considered as the main one. Then regionalization for rice yield estimation by RS was completed by spatial neighboring analysis with the Geographical information System (GIS) technology combined with using of tree algorithm. The province was divided into two regions, i. e., the single-cropping rice region which was subdivided into 3 regions including those in mountains of northwest Zhejiang, water network area of north Zhejiang and mountains of south Zhejiang, and double-cropping rice region which was subdivided into 5 regions including those on plain of north Zhejiang, coastal plains and hills of southeast Zhejiang, Jin-Qu Basin of middle Zhejiang, hills of east Zhejiang, and hills and mountains of northwest Zhejiang. This regionalization took the county borders as the region boundaries, kept the regions connective and made the administrative regions integrity and, then, could meet the requirements of rice yield estimation by RS, showing that the results were quite satisfying.

Yield estimation of metallic layers in integrated circuits

In the existing models of estimating the yield and critical area, the defect outline is usually assumed to be circular, but the observed real defect outlines are irregular in shape. In this paper, estimation of the yield and critical area is made using the Monte Carlo technique and the relationship between the errors of yield estimated by circular defect and the rectangle degree of the defect is analysed. The rectangular model of a real defect is presented, and the yield model is provided correspondingly. The models take into account an outline similar to that of an original defect, the characteristics of two-dimensional distribution of defects, the feature of a layout routing, and the character of yield estimation. In order to make the models practicable, the critical area computations related to rectangular defect and regular （vertical or horizontal） routing are discussed. The critical areas associated with rectangular defect and non- regular routing are developed also, based on the mathematical morphology. The experimental results show that the new yield model may predict the yield caused by real defects more accurately than the circular model. It is significant that the yield is accurately estimated using the proposed model for IC metals.

REMOTE SENSING BASED ESTIMATION SYSTEM FOR WINTER WHEAT YIELD IN NORTH CHINA PLAIN

This paper presents the applications of Landsat Thematic Mapper (TM) data and Advanced Very High Resolution Radiometer (AVHRR) time series data for winter wheat production estimation in North China Plain. The keytechniques are described systematically about winter wheat yield estimation system, including automatically extractingwheat area, simulating and monitoring wheat growth situation, building wheat unit yield model of large area and forecasting wheat production. Pattern recognition technique was applied to extract sown area using TM data. Temporal NDVI(Normal Division Vegetation Index) profiles were produced from 8 - 12 times AVHRR data during wheat growth dynamically. A remote sensing yield model for large area was developed based on greenness accumulation, temperature andgreenness change rate. On the basis of the solution of key problems, an operational system for winter wheat yield estimation in North China Plain using remotely sensed data was established and has operated since 1993, which consists of 4 subsystems, namely databases management, image processing, models bank management and production prediction system.The accuracy of wheat production prediction exceeded 96 per cent compared with on the spot measurement.

Winter wheat yield estimation based on assimilated Sentinel-2 images with the CERES-Wheat model

Assimilating Sentinel-2 images with the CERES-Wheat model can improve the precision of winter wheat yield estimates at a regional scale. To verify this method, we applied the ensemble Kalman filter(EnKF) to assimilate the leaf area index(LAI) derived from Sentinel-2 data and simulated by the CERES-Wheat model. From this, we obtained the assimilated daily LAI during the growth stage of winter wheat across three counties located in the southeast of the Loess Plateau in China: Xiangfen, Xinjiang, and Wenxi. We assigned LAI weights at different growth stages by comparing the improved analytic hierarchy method, the entropy method, and the normalized combination weighting method, and constructed a yield estimation model with the measurements to accurately estimate the yield of winter wheat. We found that the changes of assimilated LAI during the growth stage of winter wheat strongly agreed with the simulated LAI. With the correction of the derived LAI from the Sentinel-2 images, the LAI from the green-up stage to the heading–filling stage was enhanced, while the LAI decrease from the milking stage was slowed down, which was more in line with the actual changes of LAI for winter wheat. We also compared the simulated and derived LAI and found the assimilated LAI had reduced the root mean square error(RMSE) by 0.43 and 0.29 m^(2) m^(–2), respectively, based on the measured LAI. The assimilation improved the estimation accuracy of the LAI time series. The highest determination coefficient(R2) was 0.8627 and the lowest RMSE was 472.92 kg ha^(–1) in the regression of the yields estimated by the normalized weighted assimilated LAI method and measurements. The relative error of the estimated yield of winter wheat in the study counties was less than 1%, suggesting that Sentinel-2 data with high spatial-temporal resolution can be assimilated with the CERES-Wheat model to obtain more accurate regional yield estimates.

STUDY ON MODEL FOR REMOTE SENSING ESTIMATION OF MAIZE YIELD

Through analysis of perpendicular vegetation index (PVI) from combination of visible and nearinfrared spectrums reflecting the feature of crop reflectance, we come to the conclusion that the index can better indicate crop instantaneous photosynthesis whereas people generally regard it as the representation of crop leaf area index(LAI). Exploration of crop photosynthesis within a day and its period of duration leads to production of photosynthetic vegetation index (PST) that can reflect the whole crop accumulated photosynthesis, which means the total biomass produced by crop, moreover the method simulating PST is put forward by employment of multitemporal spectrum parameters. On the basis of the achievements mentioned above, a new comprehensive model for remote sensing estimation of maize yield is established, which can comprehensively show major physiological actions of maize and the course of its yield formation, organically integrate various effective ways of crop yield estimation. It lays a solid foundation for carrying out remote sensing estimation of maize yield on a large scale.

REGIONALIZATION FOR LARGE AREA CROP ESTIMATES BY REMOTE SENSING——A Case Study of Chinese Wheat

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STUDY ON GIS FOR YIELD ESTIMATION BY REMOTE SENSING IN JILIN MAIZE BELT

The integration of remote sensing and geographical information system (GIS) technology is an optimal method for maize yield estimation, because it needs the support of various data including remote sensing information and others.This paper introduces the objective, components, data acquisition and implementing way of maize yield estimation information system, and uses it in the study on maize acreage calculation, growth vigour monitoring, regional soil moisture content assessment and final yield forecast.

A Method for Estimation of Wheat Yield Loss Caused by Drought in Northwestern China

To develop a suitable method for monitoring wheat yield loss caused by drought for dry farming areas in northwestern China, daily ET0 and ETC were calculated using KC and FAO- PM from 1961 to 2000, and wheat evapotranspiration with an interval of 10 days was estimated with soil water balance equation for the mountainous areas in southern Ningxia, China. Actual water consumption and water requirements of wheat during growing season was calculated using soil water balance equation by correcting leakage of soil water and run-off of precipitation every year. A model for estimation of yield loss by drought was established based on crop growth-water consumption function and yield potential. The results show that it is an effective method for monitoring drought and estimating yield loss. This method is suitable for monitoring drought and estimating yield loss of wheat in dry farming areas in northwestern China.

Estimation of Drought Change Trends in Northeast China in 2020-2050 and Its Impact on Maize Yield

Based on the results of nine Coupled Model Intercomparison Project Phase 5 (CMIP5) coupling models, the temperature and precipitation data of 114 stations in Northeast China were compared and analyzed. The simulation effect of CMIP5 model on precipitation and temperature in Northeast China was evaluated. The research shows that the Geophysical Fluid Dynamics Laboratory Earth System Models (GGFDL-ESM2G) have the best simulation effect on precipitation and temperature in Northeast China. Based on the SPEI index, the relationship between the drought trend of maize growing season and the yield change rate of maize in Northeast China was analyzed, and the future drought (2020-2050) and corn yield in Northeast China were estimated. The cumulative Standardized Precipitation Evapotranspiration Index (SPEI) analysis of the northeast maize growing season (May-September) shows that the drought in the northeastern region showed an intensifying trend from 1980 to 2010, especially in the first ten years of the 21st century. The cumulative SPEI index has a significant positive correlation with the yield of maize in Northeast China, and has a certain indicator effect on the yield of maize in Northeast China. The three scenarios of GFDL-ESM2G model show that under the three scenarios of Representative Concentration Pathways (RCP), the warming in Northeast China is significant;under the RCP4.5 scenario, the precipitation in Northeast China is increasing;in the RCP2.6 and RCP8.5 climate scenarios, precipitation is presented and reduces the trend of drought. Estimates of drought trends in Northeast China show that under the RCP4.5 climate scenario, the drought in Northeast China showed a slowing trend from 2020 to 2050. Under the RCP2.6 and RCP8.5 climate scenarios, the drought in Northeast China showed an increasing trend. Under the RCP2.6 and RCP8.5 climate scenarios, the yield change rate of maize in Northeast China showed a downward trend, indicating that climate warming caused the drought in Northeast China to increase, which had a negative impact on corn yield increase. In severe drought years, drought may cause northeast corn production seriously reduced. However, under the RCP4.5 scenario, drought has little effect on corn yield.

Crop Yield Estimation with Farmers＇ Appraisal on Weather Condition

Crop yield is mainly affected by weather condition, inputs, and agriculture policies. In the crop yield estimation, farmers＇ perception on weather conditions lead to the assessment of how well yield would be compared to the previous seasons. This paper applies Bayesian estimation method to estimate crop yield with farmers＇ appraisal on weather condition. The paper shows that crop yield estimation with farmers＇ appraisal on weather condition takes into account risk proportionally to climate change. In light of the United Nations efforts aimed to build a consolidated agriculture statistical system across countries, the statistical model developed here should provide an important tool both for the crop yield estimation and food price analysis.

利用区域地震资料监测朝鲜地下核试验的研究进展

根据朝鲜6次地下核试验在中国东北及邻近地区产生的宽频带区域地震资料,中国科学院地质与地球物理研究所地震学学科组发展了一套用于监测地下核试验的地震学方法,主要包括震级测量和当量估计、高精度震中定位以及爆炸与天然地震事件识别等.在全球范围内,存在多个用于震级-当量转换的经验公式,确定何种公式适合于朝鲜半岛地区至关重要.通过搜集一组用于地震测深的化学爆炸的当量和埋深,并以这些已知爆炸源作为量规事件,能够标定朝鲜地下核试验场的震级-当量关系,从而实现对朝鲜地下核爆进行当量估计.在精确定位方面,发展了利用多事件之间波形互相关方法精确测定它们之间的走时差从而完成高精度相对震中定位.通过进一步利用Pn和Pg等多震相数据还实现了对核爆深度的相对定位.在爆炸震源识别方面,利用统计处理方法分析研究了不同类型事件所产生地震波的特征,包括6次地下核试验、4次天然地震和3次化学爆炸,以及一些矿区塌陷等.结果发现P和S类型波的振幅比,例如Pn/Lg、Pg/Lg和Pn/Sn等,在2 Hz以上能够较好区分各类震源.其中爆炸震源产生的振幅比显著高于天然地震的值.特别是,通过计算谱振幅比的台网平均值能够显著提高识别的可靠性.利用中国东北和附近地区地震台网所提供的数据可以准确地将朝鲜地下核试验从周边天然地震事件中识别出来.该方法同时得到化学爆炸的P/S谱振幅比值介于核爆炸和天然地震的群组之间.