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.

Estimation of soil erosion and sediment yield in Wadi El Hachem watershed(Algeria)using the RUSLE-SDR approach

One of the most common types of soil degradation is water erosion.It reduces soil quality at the erosion site and may cause sedimentation issues at the deposition site.This phenomenon is estimated using a variety of models.The Revised Universal Soil Loss Equation(RUSLE)model is the most often used,due to its consistence and low data requirement.It is useful for estimating annual soil loss at the watershed scale.To investigate the relationship between soil erosion and sediment deposition,the combined RUSLE and Sediment Delivery Ratio(SDR)models are used.The Wadi El Hachem watershed is a coastal and mountainous Mediterranean basin with rugged topography and high degree of climatic aggressiveness.Both of these characteristics can have an immediate effect on soil erosion and sediment yield.This research includes estimating the Average Annual Soil Loss(A)and Sediment Yield(SY)in the Wadi El Hachem watershed,mapping different RUSLE factors as well as A and SY,and studying the influence of rainfall erosivity(R)on A and SY in dry and rainy years.The A results vary from 0 to 410 t·ha^(-1)·yr^(-1)with an annual average of 52 t·ha^(-1)·yr^(-1).The Renfro's SDR model was selected as the best model for estimating SY,with standard error,standard deviation,coefficient of variation,and Nash–Sutcliffe efficiency(NSE)values of 0.38%,0.02,0.07%,and 1.00,respectively.The average SY throughout the whole watershed is around 27 t·ha^(-1)·yr^(-1).The SY map for the entire Wadi El Hachem watershed revealed that sediment production zones are mainly concentrated in the Northeast of the basin,at the basin’s outlet,and in the tributaries of the dam.The simulation results of soil loss and sediment yield in dry and rainy years revealed that R is one of the main factors affecting soil erosion and sediment deposition in the Wadi El Hachem watershed.The mean difference in R factor between dry year and rainy year is 671 MJ·mm·ha^(-1)·h^(-1)·yr^(-1).As a result of this fluctuation,the soil loss and sediment yield have increased by 15 and 8 t·ha^(-1)·yr^(-1),respectively.The results of this research can be used to provide scientific and technical support for conservation and management strategies of the Wadi El Hachem watershed.

Winter wheat yield improvement by genetic gain across different provinces in China

The replacement of winter wheat varieties has contributed significantly to yield improvement worldwide,with remarkable progress in China.Drawing on two sets of data,production yield from the National Bureau of Statistics of China and experimental yield from literature,this study aims to(1)illustrate the increasing patterns of production yield among different provinces from 1978 to 2018 in China,(2)explore the genetic gain in yield and yield relevant traits through the variety replacement based on experimental yield from 1937 to 2016 in China,and(3)compare the yield gap between experimental yield and production yield.The results show that both the production and experimental yields significantly increased along with the variety replacement.The national annual yield increase ratio for the production yield was 1.67%from 1978 to 2018,varying from 0.96%in Sichuan Province to 2.78%in Hebei Province;such ratio for the experimental yield was 1.13%from 1937 to 2016.The yield gap between experimental and production yields decreased from the 1970s to the 2010s.This study reveals significant increases in some yield components consequent to variety replacement,including thousand-grain weight,kernel number per spike,and grain number per square meter;however,no change is shown in spike number per square meter.The biomass and harvest index consistently and significantly increased,whereas the plant height decreased significantly.

The underlying mechanism of variety–water–nitrogen–stubble damage interactions on yield formation in ratoon rice with low stubble height under mechanized harvesting

Agronomic measures are the key to promote the sustainable development of ratoon rice by reducing the damage from mechanical crushing to the residual stubble of the main crop, thereby mitigating the impact on axillary bud sprouting and yield formation in ratoon rice. This study used widely recommended conventional rice Jiafuzhan and hybrid rice Yongyou 2640 as the test materials to conduct a four-factor block design field experiment in a greenhouse of the experimental farm of Fujian Agricultural and Forestry University, China from 2018 to 2019.The treatments included fertilization and no fertilization, alternate wetting and drying irrigation and continuous water flooding irrigation, and plots with and without artificial crushing damage on the rice stubble. At the same time, a 13C stable isotope in-situ detection technology was used to fertilize the pot experiment. The results showed significant interactions among varieties, water management, nitrogen application and stubble status.Relative to the long-term water flooding treatment, the treatment with sequential application of nitrogen fertilizer coupled with moderate field drought for root-vigor and tiller promotion before and after harvesting of the main crop, significantly improved the effective tillers from low position nodes. This in turn increased the effective panicles per plant and grains per panicle by reducing the influence of artificial crushing damage on rice stubble and achieving a high yield of the regenerated rice. Furthermore, the partitioning of 13C assimilates to the residual stubble and its axillary buds were significantly improved at the mature stage of the main crop, while the translocation rate to roots and rhizosphere soil was reduced at the later growth stage of ratooning season rice. This was triggered by the metabolism of hormones and polyamines at the stem base regulated by the interaction of water and fertilizer at this time. We therefore suggest that to achieve a high yield of ratoon rice with low stubble height under mechanized harvesting, the timely application of nitrogen fertilizer is fundamental,coupled with moderate field drying for root-vigor preservation and tiller promotion before and after the mechanical harvesting of the main crop.

Exogenous melatonin improves cotton yield under drought stress by enhancing root development and reducing root damage

The exogenous application of melatonin by the root drenching method is an effective way to improve crop drought resistance.However,the optimal concentration of melatonin by root drenching and the physiological mechanisms underlying melatonin-induced drought tolerance in cotton(Gossypium hirsutum L.)roots remain elusive.This study determined the optimal concentration of melatonin by root drenching and explored the protective effects of melatonin on cotton roots.The results showed that 50μmol L-1 melatonin was optimal and significantly mitigated the inhibitory effect of drought on cotton seedling growth.Exogenous melatonin promoted root development in drought-stressed cotton plants by remarkably increasing the root length,projected area,surface area,volume,diameter,and biomass.Melatonin also mitigated the drought-weakened photosynthetic capacity of cotton and regulated the endogenous hormone contents by regulating the relative expression levels of hormone-synthesis genes under drought stress.Melatonin-treated cotton seedlings maintained optimal enzymatic and non-enzymatic antioxidant capacities,and produced relatively lower levels of reactive oxygen species and malondialdehyde,thus reducing the drought stress damage to cotton roots(such as mitochondrial damage).Moreover,melatonin alleviated the yield and fiber length declines caused by drought stress.Taken together,these findings show that root drenching with exogenous melatonin increases the cotton yield by enhancing root development and reducing the root damage induced by drought stress.In summary,these results provide a foundation for the application of melatonin in the field by the root drenching method.

Grain yield and N uptake of maize in response to increased plant density under reduced water and nitrogen supply conditions

The development of modern agriculture requires the reduction of water and chemical N fertilizer inputs.Increasing the planting density can maintain higher yields,but also consumes more of these restrictive resources.However,whether an increased maize density can compensate for the negative effects of reduced water and N supply on grain yield and N uptake in the arid irrigated areas remains unknown.This study is part of a long-term positioning trial that started in 2016.A split-split plot field experiment of maize was implemented in the arid irrigated area of northwestern China in 2020 to 2021.The treatments included two irrigation levels:local conventional irrigation reduced by 20%(W1,3,240 m^(3)ha^(-1))and local conventional irrigation(W2,4,050 m^(3)ha^(-1));two N application rates:local conventional N reduced by 25%(N1,270 kg ha^(-1))and local conventional N(360 kg ha^(-1));and three planting densities:local conventional density(D1,75,000 plants ha^(-1)),density increased by 30%(D2,97,500 plants ha-1),and density increased by 60%(D3,120,000 plants ha^(-1)).Our results showed that the grain yield and aboveground N accumulation of maize were lower under the reduced water and N inputs,but increasing the maize density by 30% can compensate for the reductions of grain yield and aboveground N accumulation caused by the reduced water and N supply.When water was reduced while the N application rate remained unchanged,increasing the planting density by 30% enhanced grain yield by 13.9% and aboveground N accumulation by 15.3%.Under reduced water and N inputs,increasing the maize density by 30% enhanced N uptake efficiency and N partial factor productivity,and it also compensated for the N harvest index and N metabolic related enzyme activities.Compared with W2N2D1,the N uptake efficiency and N partial factor productivity increased by 28.6 and 17.6%under W1N1D2.W1N2D2 had 8.4% higher N uptake efficiency and 13.9% higher N partial factor productivity than W2N2D1.W1N2D2 improved urease activity and nitrate reductase activity by 5.4% at the R2(blister)stage and 19.6% at the V6(6th leaf)stage,and increased net income and the benefit:cost ratio by 22.1 and 16.7%,respectively.W1N1D2 and W1N2D2 reduced the nitrate nitrogen and ammoniacal nitrogen contents at the R6 stage in the 40-100 cm soil layer,compared with W2N2D1.In summary,increasing the planting density by 30% can compensate for the loss of grain yield and aboveground N accumulation under reduced water and N inputs.Meanwhile,increasing the maize density by 30% improved grain yield and aboveground N accumulation when water was reduced by 20% while the N application rate remained constant in arid irrigation areas.

Manure substitution improves maize yield by promoting soil fertility and mediating the microbial community in lime concretion black soil

Synthetic nitrogen(N)fertilizer has made a great contribution to the improvement of soil fertility and productivity,but excessive application of synthetic N fertilizer may cause agroecosystem risks,such as soil acidification,groundwater contamination and biodiversity reduction.Meanwhile,organic substitution has received increasing attention for its ecologically and environmentally friendly and productivity benefits.However,the linkages between manure substitution,crop yield and the underlying microbial mechanisms remain uncertain.To bridge this gap,a three-year field experiment was conducted with five fertilization regimes:i)Control,non-fertilization;CF,conventional synthetic fertilizer application;CF_(1/2)M_(1/2),1/2 N input via synthetic fertilizer and 1/2 N input via manure;CF_(1/4)M_(3/4),1/4 N input synthetic fertilizer and 3/4 N input via manure;M,manure application.All fertilization treatments were designed to have equal N input.Our results showed that all manure substituted treatments achieved high soil fertility indexes(SFI)and productivities by increasing the soil organic carbon(SOC),total N(TN)and available phosphorus(AP)concentrations,and by altering the bacterial community diversity and composition compared with CF.SOC,AP,and the soil C:N ratio were mainly responsible for microbial community variations.The co-occurrence network revealed that SOC and AP had strong positive associations with Rhodospirillales and Burkholderiales,while TN and C:N ratio had positive and negative associations with Micromonosporaceae,respectively.These specific taxa are implicated in soil macroelement turnover.Random Forest analysis predicted that both biotic(bacterial composition and Micromonosporaceae)and abiotic(AP,SOC,SFI,and TN)factors had significant effects on crop yield.The present work strengthens our understanding of the effects of manure substitution on crop yield and provides theoretical support for optimizing fertilization strategies.

Deep learning for joint channel estimation and feedback in massive MIMO systems

The great potentials of massive Multiple-Input Multiple-Output(MIMO)in Frequency Division Duplex(FDD)mode can be fully exploited when the downlink Channel State Information(CSI)is available at base stations.However,the accurate CsI is difficult to obtain due to the large amount of feedback overhead caused by massive antennas.In this paper,we propose a deep learning based joint channel estimation and feedback framework,which comprehensively realizes the estimation,compression,and reconstruction of downlink channels in FDD massive MIMO systems.Two networks are constructed to perform estimation and feedback explicitly and implicitly.The explicit network adopts a multi-Signal-to-Noise-Ratios(SNRs)technique to obtain a single trained channel estimation subnet that works well with different SNRs and employs a deep residual network to reconstruct the channels,while the implicit network directly compresses pilots and sends them back to reduce network parameters.Quantization module is also designed to generate data-bearing bitstreams.Simulation results show that the two proposed networks exhibit excellent performance of reconstruction and are robust to different environments and quantization errors.

A comparative study of data-driven battery capacity estimation based on partial charging curves

With its generality and practicality, the combination of partial charging curves and machine learning(ML) for battery capacity estimation has attracted widespread attention. However, a clear classification,fair comparison, and performance rationalization of these methods are lacking, due to the scattered existing studies. To address these issues, we develop 20 capacity estimation methods from three perspectives:charging sequence construction, input forms, and ML models. 22,582 charging curves are generated from 44 cells with different battery chemistry and operating conditions to validate the performance. Through comprehensive and unbiased comparison, the long short-term memory(LSTM) based neural network exhibits the best accuracy and robustness. Across all 6503 tested samples, the mean absolute percentage error(MAPE) for capacity estimation using LSTM is 0.61%, with a maximum error of only 3.94%. Even with the addition of 3 m V voltage noise or the extension of sampling intervals to 60 s, the average MAPE remains below 2%. Furthermore, the charging sequences are provided with physical explanations related to battery degradation to enhance confidence in their application. Recommendations for using other competitive methods are also presented. This work provides valuable insights and guidance for estimating battery capacity based on partial charging curves.

Efficient Unsupervised Image Stitching Using Attention Mechanism with Deep Homography Estimation

Traditional feature-based image stitching techniques often encounter obstacles when dealing with images lackingunique attributes or suffering from quality degradation. The scarcity of annotated datasets in real-life scenesseverely undermines the reliability of supervised learning methods in image stitching. Furthermore, existing deeplearning architectures designed for image stitching are often too bulky to be deployed on mobile and peripheralcomputing devices. To address these challenges, this study proposes a novel unsupervised image stitching methodbased on the YOLOv8 (You Only Look Once version 8) framework that introduces deep homography networksand attentionmechanisms. Themethodology is partitioned into three distinct stages. The initial stage combines theattention mechanism with a pooling pyramid model to enhance the detection and recognition of compact objectsin images, the task of the deep homography networks module is to estimate the global homography of the inputimages consideringmultiple viewpoints. The second stage involves preliminary stitching of the masks generated inthe initial stage and further enhancement through weighted computation to eliminate common stitching artifacts.The final stage is characterized by adaptive reconstruction and careful refinement of the initial stitching results.Comprehensive experiments acrossmultiple datasets are executed tometiculously assess the proposed model. Ourmethod’s Peak Signal-to-Noise Ratio (PSNR) and Structure Similarity Index Measure (SSIM) improved by 10.6%and 6%. These experimental results confirm the efficacy and utility of the presented model in this paper.

Yields,growth and water use under chemical topping in relations to row configuration and plant density in drip-irrigated cotton

Background Water deficit is an important problem in agricultural production in arid regions.With the advent of wholly mechanized technology for cotton planting in Xinjiang,it is important to determine which planting mode could achieve high yield,fiber quality and water use efficiency(WUE).This study aimed to explore if chemical topping affected cotton yield,quality and water use in relation to row configuration and plant densities.Results Experiments were carried out in Xinjiang China,in 2020 and 2021 with two topping method,manual topping and chemical topping,two plant densities,low and high,and two row configurations,i.e.,76 cm equal rows and 10+66 cm narrow-wide rows,which were commonly applied in matching harvest machine.Chemical topping increased seed cotton yield,but did not affect cotton fiber quality comparing to traditional manual topping.Under equal row spacing,the WUE in higher density was 62.4%higher than in the lower one.However,under narrow-wide row spacing,the WUE in lower density was 53.3%higher than in higher one(farmers’practice).For machine-harvest cotton in Xinjiang,the optimal row configuration and plant density for chemical topping was narrow-wide rows with 15 plants m-2 or equal rows with 18 plants m-2.Conclusion The plant density recommended in narrow-wide rows was less than farmers’practice and the density in equal rows was moderate with local practice.Our results provide new knowledge on optimizing agronomic managements of machine-harvested cotton for both high yield and water efficient.

Cascaded ELM-Based Joint Frame Synchronization and Channel Estimation over Rician Fading Channel with Hardware Imperfections

Due to the interdependency of frame synchronization(FS)and channel estimation(CE),joint FS and CE(JFSCE)schemes are proposed to enhance their functionalities and therefore boost the overall performance of wireless communication systems.Although traditional JFSCE schemes alleviate the influence between FS and CE,they show deficiencies in dealing with hardware imperfection(HI)and deterministic line-of-sight(LOS)path.To tackle this challenge,we proposed a cascaded ELM-based JFSCE to alleviate the influence of HI in the scenario of the Rician fading channel.Specifically,the conventional JFSCE method is first employed to extract the initial features,and thus forms the non-Neural Network(NN)solutions for FS and CE,respectively.Then,the ELMbased networks,named FS-NET and CE-NET,are cascaded to capture the NN solutions of FS and CE.Simulation and analysis results show that,compared with the conventional JFSCE methods,the proposed cascaded ELM-based JFSCE significantly reduces the error probability of FS and the normalized mean square error(NMSE)of CE,even against the impacts of parameter variations.

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.

High-Precision Doppler Frequency Estimation Based Positioning Using OTFS Modulations by Red and Blue Frequency Shift Discriminator

Orthogonal Time Frequency and Space(OTFS) modulation is expected to provide high-speed and ultra-reliable communications for emerging mobile applications, including low-orbit satellite communications. Using the Doppler frequency for positioning is a promising research direction on communication and navigation integration. To tackle the high Doppler frequency and low signal-to-noise ratio(SNR) in satellite communication, this paper proposes a Red and Blue Frequency Shift Discriminator(RBFSD) based on the pseudo-noise(PN) sequence.The paper derives that the cross-correlation function on the Doppler domain exhibits the characteristic of a Sinc function. Therefore, it applies modulation onto the Delay-Doppler domain using PN sequence and adjusts Doppler frequency estimation by red-shifting or blue-shifting. Simulation results show that the performance of Doppler frequency estimation is close to the Cramér-Rao Lower Bound when the SNR is greater than -15dB. The proposed algorithm is about 1/D times less complex than the existing PN pilot sequence algorithm, where D is the resolution of the fractional Doppler.

Effect of Inorganic and Organic Fertilizers on Growth and Yield of Two Industrial Potato Varieties (Asterix and Courage) in Bangladesh

Potato is one of the most important vegetable crops, which contributes more than half of the total vegetable production in Bangladesh. Four field experiments were conducted in two different locations in Bangladesh to develop integrated nutrient management practices to produce quality potato seed in industrial processing varieties Asterix and Courage. For the inorganic trial, Factorial Randomized Complete Block Design (RCBD) including 2 potato varieties, 5 treatments with 4 replications, and in the organic fertilizer trial, Factorial RCBD including 2 potato varieties, 6 treatments with 4 replications were used. In the inorganic fertilizer trail, the highest yield was obtained in the variety Asterix due to Nitrogen, Phosphorus, Potassium, and Sulfur (NPKS) plus Magnesium treated plot in Domar BADC farm and due to NPKS plus Boron, Zinc, and Magnesium treated plots in Kashimpur Farm. In the case of variety Courage, the highest yield was found in the treatment of NPKS plus Zinc in Domar BADC farm while in Kashimpur farm, NPKS plus Mg treated plots had the highest yield of potato variety-Courage. In the organic fertilizer trail, the highest tuber yield per hill was obtained by applying the government-approved commercial brand Northern organic fertilizer in variety Asterix and by organic fertilizer brand Chook Chook in variety Courage. Parameters such as days to tuber initiation, number of stems per hill, plant height, and number of tubers per hill were found statistically different among the treatments and between the two varieties. Treatments namely Northern organic fertilizer and Cowdung combined with mustard oil cake performed better considering standard grade tuber yield (grade A and B) compared to other treatments. Hence, the combination of NPKS MgZn and either Northern organic or Chook Chook or Cowdung plus mustard oil cake could be used to grow the varieties Asterix and Courage.

Effect of Planting Date on Yield and Yield Components of Grain Sorghum Hybrids

In Kansas, productivity of grain sorghum [Sorghum bicolor (L.) Moench] is affected by weather conditions at planting and during pollination. Planting date management and selection of hybrid maturity group can help to avoid severe environmental stresses during these sensitive stages. The hypothesis of the study was that late May planting improves grain sorghum yield and yield components compared with late June planting. The objectives of this research were to investigate the influence of planting dates yield and yield components of different grain sorghum hybrids, and to determine the optimal planting date and hybrid combination for maximum biomass and grains production. Three sorghum hybrids (early, medium, and late maturing) were planted in late May and late June without irrigation in Kansas at Manhattan/Ashland Bottom Research Station, and Hutchinson in 2010;and at Manhattan/North Farm and Hutchinson in 2011. Data on dry matter production, yield and yield components were collected. Grain yield and yield components were influenced by planting date depending on environmental conditions. At Manhattan (2010), greater grain yield, number of heads per plant, were obtained with late-June planting compared with late May planting, while at Hutchinson (2010) greater yield was obtained with late May planting for all hybrids. The yield component most affected at Hutchinson was the number of kernels∙panicle−1 and plant density. Late-May planting was favorable for late maturing hybrid (P84G62) in all locations. However, the yield of early maturing hybrid (DKS 28-05) and medium maturing hybrid (DKS 37-07) was less affected by delayed planting. The effects of planting dates on yield and yield components of grain sorghum hybrids were found to be variable among hybrid maturity groups and locations.

Model-driven full system dynamics estimation of PMSM-driven chain shell magazine

Based on the system dynamic model, a full system dynamics estimation method is proposed for a chain shell magazine driven by a permanent magnet synchronous motor(PMSM). An adaptive extended state observer(AESO) is proposed to estimate the unmeasured states and disturbance, in which the model parameters are adjusted in real time. Theoretical analysis shows that the estimation errors of the disturbances and unmeasured states converge exponentially to zero, and the parameter estimation error can be obtained from the extended state. Then, based on the extended state of the AESO, a novel parameter estimation law is designed. Due to the convergence of AESO, the novel parameter estimation law is insensitive to controllers and excitation signal. Under persistent excitation(PE) condition, the estimated parameters will converge to a compact set around the actual parameter value. Without PE signal, the estimated parameters will converge to zero for the extended state. Simulation and experimental results show that the proposed method can accurately estimate the unmeasured states and disturbance of the chain shell magazine, and the estimated parameters will converge to the actual value without strictly continuous PE signals.

The Yield and Diseases-Pest Performance of Breeding Materials for Tomatoes (Lycopersicon esculentum) Sourced from Different Exotic Sources

A study was conducted with 36 tomato germplasm [PGRC (19), The Netherlands (7), Japan (2), and Bangladesh (9)] with 3 check varieties in the research field of the Olericulture Division of Horticulture Research Centre (HRC), Bangladesh Agricultural Research Institute (BARI), Gazipur during the winter season of 2022-23 to identify promising tomato breeding germplasm. All the germplasm showed differences in most of the parameters studied. Considerable variation was observed for fruit yield per hectare varied from 39 to 144 tons, while the highest fruit yield per hectare was harvested from the germplasm viz., BD 7759B, BD 7757, BD 7751, BD 7301, QM, BD 7759A, BD 7762, QF, BD 7753, BD 8886, BD 10351, NL-1A, SLA-19. The number of locules, total soluble solids (TSS) and plant height at last harvest varied from 2 to 6.6, 2.6 to 5.9%, and 66 to 154cm, respectively. The TYLCV infection (%) and leaf-sucking pest infestation (%) were observed with a 1 to 20% range in the field condition in both cases. So, we can select that germplasm has zero per cent infestation. Considering the plant growth habit, earliness, different yield contributing horticultural traits, virus and pest infestation thirteen germplasm viz., BD 7759B, BD 7757, BD 7751, BD 7301, QM, BD 7759A, BD 7762, QF, BD 7753, BD 8886, BD 10351, NL-1A, SLA-19 were selected as breeding materials for further tomato improvement program.

Salinity Stress Deteriorates Grain Yield and Increases 2-Acetyl-1-Pyrroline Content in Rice

Salinity stress greatly impacts rice grain yield and quality, as well as the 2-acetyl-1-pyrroline(2-AP) content in grains. The present study was conducted with Nanjing 9108(NJ9108, conventional japonica rice) and Wenliangyou 669(WLY669, indica hybrid rice) in the fields with non-salinity(NS), low salinity(LS), and high salinity(HS) stresses in 2021 and 2022.

Yield and Nutritive Values of Semi- and Non-Fall Dormant Alfalfa Cultivars under Late-Cutting Schedule in California’s Central Valley

California is one of the major alfalfa (Medicago sativa L) forage-producing states in the U.S, but its production area has decreased significantly in the last couple of decades. Selection of cultivars with high yield and nutritive value under late-cutting schedule strategy may help identify cultivars that growers can use to maximize yield while maintaining area for sustainable alfalfa production, but there is little information on this strategy. A field study was conducted to determine cumulative dry matter (DM) and nutritive values of 20 semi- and non-fall dormant (FD) ratings (FD 7 and FD 8 - 10, respectively) cultivars under 35-day cut in California’s Central Valley in 2020-2022. Seasonal cumulative DM yields ranged from 6.8 in 2020 to 37.0 Mg·ha−1 in 2021. Four FD 8 - 9 cultivars were the highest yielding with 3-yrs avg. DM greater than the lowest yielding lines by 46%. FD 7 cultivar “715RR” produced the highest crude protein (CP: 240 g·Kg−1) while FD 8 cultivar “HVX840RR” resulted in the highest neutral detergent fiber digestibility (NDFD: 484 g·Kg−1, 7% greater than the top yielding cultivars) but with DM yield intermediate. Yields and NDFD correlated positively but weakly indicating some semi- and non-FD cultivars performing similarly. These results suggest that selecting high yielding cultivars under 35-day cutting schedule strategy can be used as a tool to help growers to maximize yield while achieving good quality forages for sustainable alfalfa production in California’s Central Valley.