Productivity Model Research Articles

Modelling of energy productivity prediction systems of shallots classification growth phase system using convolutional neural network

The energy productivity of agriculture is the effectiveness of energy consumed to carry out agricultural processes. In that case, energy prediction is needed in the future to achieve optimal agricultural yields for the farmers, make a strategy of the environmental health based on the energy field, especially non-renewable energy like fuel and consider the impact on the environment like pesticides, fertilizer and irrigation. This study used function modelling to predict energy productivity during shallot cultivation in Hutajulu, Sumatera Utara. The closest model equation is the Cobb-Douglas Model, a general model for predicting the production yield of a farm. Furthermore, a mathematical model was developed with Convolutional Neural Network (CNN). The system classified the image data based on the phases of shallot growth using CNN to process data in images. The model prediction used Ordinary Least Square (OLS) Regression to develop the Cobb-Douglas Mathematics model. It then continued with CNN modelling stages: Data Setup, Load Data, Preprocess Image, Model Development, Train Model, Model Evaluation, Test Model and Save Model. At the image classification stage, based on the growth phase, CNN was used with a sequential model with three hidden layers. The activation function used was ReLU and sigmoid to divide the class of growth phase into two based on binary digits (0.1). The energy productivity (Yi, kg MJ-1) model was lnYI = 4.177 - 0.0152lnX1 - 0.05lnX2 + 0.02lnX3 + 0.098lnX3 - 0.882lnX3 - 0.027 lnXer with an accuracy of 100% using Jupyter Notebook. The CNN model produces a percentage accuracy for the image classification of 95.3% with 100 epochs.

Innovative Design and Execution Model for Improving Productivity of Interior Prefabricated Commercial Wall Assemblies

Field productivity of building trades is the focus of prefabricated construction practitioners as a path to greater profitability and competitiveness in the marketplace. Construction firms are struggling to meet the demand of the marketplace due to shortages of skilled workers and flat to declining productivity. Human capital and productivity challenges are affecting the ability to both acquire new work and complete the work under contract. This study focuses on the development of an innovative model that defines a process for the design, project site preconstruction planning phase, and fabrication of interior prefabricated wall components that improves onsite productivity. The developed model was tested and implemented in a case study of a single project comprised of four identical buildings located on a singular jobsite while utilizing both traditional and model approaches. The results verify that the productivity model developed in this study is capable of reducing on-site labor hours and, therefore, improving field productivity compared to traditional methods. The application of the model saved between 7–23% man-hours compared to the traditional methods and beat the estimate by 17%. Practitioners and researchers are both incentivized to explore, develop, and implement novel methodologies to address the human capital shortage that is facing the construction industry.

Meteorological Data Fusion Approach for Modeling Crop Water Productivity Based on Ensemble Machine Learning

Crop water productivity modeling is an increasingly popular rapid decision making tool to optimize water resource management in agriculture for the decision makers. This work aimed to model, predict, and simulate the crop water productivity (CWP) for grain yields of both wheat and maize. Climate datasets were collected over the period from 1969 to 2019, including: mean temperature (Tmean), maximum temperature (Tmax), minimum temperature (Tmin), relative humidity (H), solar radiation (SR), sunshine hours (Ssh), wind speed (WS), and day length (DL). Five machine learning (ML) methods were applied, including random forest (RF), support vector regression (SVM), bagged trees (BT), boosted trees (BoT), and matern 5/2 Gaussian process (MG). Models implemented by MG, including Tmean, SR, WS, and DL (Model 3); Tmax, Tmin, Tmean, SR, Ssh, WS, H, and DL (Model 8); Tmean, and SR (Model 9), were found optimal (r2 = 0.85) for forecasting CWP for wheat. Moreover, results of CWP for maize showed that the BT model, a combination of SR, WS, H, and Tmin data, achieved a high correlation coefficient of 0.82 compared to others. The outcomes demonstrated several high performance ML-based alternative CWP estimation methods in case of limited climatic data supporting decision making for designers, developers, and managers of water resources.

Comparison of the Application of Different Process-Based Models in the Study of Spatio-Temporal Patterns of Ecosystem Service Value

Quantitative assessments of ecosystem service value (ESV) are of great significance for rational allocation of environmental resources and making regional ecological protection decisions. The method of equivalence factor per unit area is widely used for this purpose because of its simple algorithm. However, ESV is also affected by biotic and environmental factors (e.g., net ecosystem productivity (NEP) or precipitation), which are difficult to obtain at the regional scale, leading to uncertainty in ESV estimations. In this study, according to the equivalent factor modified by precipitation and NEP from four state-of-the-art process-based productivity models, i.e., CLM4.0, LPJDGVM, LPJGUESS and ORCHIDEE, we explored the temporal and spatial patterns of ESV of 15 administrative regions in northern China. The results show that the simulation accuracy of different models varied among four representative ecosystem types, i.e., typical steppe in northern China, alpine steppe in northwest China, farmland ecosystem in central China, and forest in northeast China, implying that model-based ESV estimates are ecosystem-specific. The ESV tends to decline from northeast to southwest in northern China. Regions with dense vegetation usually had high ESV due to better hydrological and thermal conditions. Low vegetation coverage areas, such as Qinghai and Xinjiang, had higher ESV because of their large geographical areas. The central and eastern developed regions without abundant natural resources had lower ESV due to their lower NEP. For different categories of ecosystem services, the regulation services (mainly water flow regulation services and climate regulation services) contributed the most to ESV. For the temporal dynamics, the total ESV of the 15 provinces, autonomous regions and municipalities showed an insignificant downward trend over the years. The regions with increasing trends of ESV were distributed in northwestern China, while pixels with decreasing trends of ESV were concentrated in northeastern China. Land use cover change may be the most important factor controlling the temporal dynamics of ESV. Our results can provide support for the enaction of reasonable strategies for ecological protection and economic development in northern China.

Novel Yield Model of Pinus patula Schltdl. & Cham. Growth near the Ecological Limit in Northwestern Peruvian Andes

Forest plantations with exotic species in the northwestern Peruvian Andes have brought different ecosystem benefits. The wood productivity in this Páramo region is directly related to the great availability of water from abundant rainfall compared to other regions of the semi-arid Andes. To address the lack of information on forest inventories of plantations in the Páramo region, this study used annual growth rings (dendrochronology) to build new models of tree growth and wood productivity and compared 22-year-old Pinus patula plots with thinning and unthinning treatments. Our results show that late thinning, removing 63% of stem density in the 15th year, does not have significant effects on the diameter increase or stand-level productivity. For these plantations, we propose a management rotation of 21 years with a first thinning treatment (35%) at 5 years and a second thinning treatment (50%) at 12 years. Production at 21 years is expected to be between 194.6 m3 ha−1 and 504.6 m3 ha−1 for stands with low and high wood productivity, respectively. Tree-ring studies are potentially useful for monitoring forest plantations and provide an alternative method for forest managers who use allometric equations to predict silvicultural treatments and to propose management guides for plantations.

Drowsiness Detection in the Era of Industry 4.0: Are We Ready?

Interconnectivity and smart automation of Internet of Things in recent times have led to the concept of Industry 4.0. Together with the improvement in productivity and new business models, employment conditions should take advantage of these new technologies. Safety in the workplace is one of the most sensitive topics on matters that needs targeted and accurate solutions. The safety can be guaranteed by investigating the attention states of the workers, and in particular, their drowsiness levels. Several technologies have faced this problem by using biometrics, but how many of them are applicable in a real-case-use scenario of Industry 4.0? This article aims to answer this question by discussing available data and methods that can be used in specific workplaces. We highlight their limitations and accuracy to sketch out the recent literature that may contribute to worker safety in Industry 4.0. Finally, we point out a gap that needs to be filled in order to implement these strategies on a large scale.

Optimization analysis of production capacity on trimming process for passenger vehicle with a learning curve approach (case study: car os)

The primary goal of this research to optimizing the production process capacity is expected to answer basic problems of how steps taken by the manufacturing industry to produce effective output. This study used a technique Research Design case study, namely in the automotive manufacturing industry in the process of assembling a car OS in the Trimming Process PT. XYZ. This study refers to the technique of quantitative research methods of data collection by interview and sampling cycle time by using stopwach. There are 5 models used in this research is, a) Learning Curve Approach, b) Time study c) The model of productivity to measure productivity Actual and after the application of the standard time Productivity Model, d) Efficiency and Efectivity Model. Results of this study found a) Learning Curve Effect on the curve for each station, the end effect of learning curve for operators learning process average in the assembly process units to 11 – 16 with learning rate between 88% - 94% for each stations, b) Standard time for assembly process OS is 215.17 minutes/units with the effective capacity 5.18 units / hour, c) Estimate the productivity level obtained when applying the standard time is 5.18 higher than the actual productivity is 2.45, d) the level of efficiency that can be achieved in the application of standard time is 4.7%, e) Identification of factors that affect the speed of the operator skill then found five factors that according to the operator (the respondents) that affect their work, the machine does not automatically chosen 95% of respondents, many elements of the work carried out by manual as much as 85% and 80% respectively of respondents choose the layout, engine support and salaries / wages are still not standard than other same industry.

Employee Productivity Model at Mineral, Coal and Geothermal Resources Center

This study intends to determine the influence of Organizational Culture and Work Environment on Work Productivity, either partially or simultaneously, this research was carried out at the Center for Mineral, Coal and Geothermal Resources. The population in this study amounted to 161 employees at the Center for Mineral, Coal and Geothermal Resources. Sampling was used in taking samples in this study using a census with a total sample of 62 respondents. Based on the variables studied, the types of research used are descriptive and verification. The analytical method used in this research is multiple linear regression, correlation coefficient, determination analysis coefficient, partial and simultaneous hypothesis testing. This study obtained the results that organizational culture has a significant influence on work productivity, and the work environment does not have a significant effect on work productivity. The results of the research show that both orientations can be applied simultaneously in determining work productivity in the Center for Mineral, Coal and Geothermal Resources. The results of this study are expected to help managers in the company to increase the work productivity of their employees by paying attention to Organizational Culture and Work Environment on Work Productivity.

How Well Can Matching High Spatial Resolution Landsat Data with Flux Tower Footprints Improve Estimates of Vegetation Gross Primary Production

Eddy-covariance (EC) measurements are widely used to optimize the terrestrial vegetation gross primary productivity (GPP) model because they provide standardized and high-quality flux data within their footprint areas. However, the extent of flux data taken from a tower site within the EC footprint, represented by the satellite-based grid cell between Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS), and the performance of the model derived from the Normalized Difference Vegetation Index (NDVI) within the EC footprint at different spatial resolutions (e.g., Landsat and MODIS) remain unclear. Here, we first calculated the Landsat-footprint NDVI and MODIS-footprint NDVI and assessed their spatial representativeness at 78 FLUXNET sites at 30 m and 500 m scale, respectively. We then optimized the parameters of the revised Eddy Covariance-Light Use Efficiency (EC-LUE) model using NDVI within the EC-tower footprints that were calculated from the Landsat and MODIS sensor. Finally, we evaluated the performance of the optimized model at 30 m and 500 m scale. Our results showed that matching Landsat data with the flux tower footprint was able to improve the performance of the revised EC-LUE model by 18% for savannas, 14% for croplands, 9% for wetlands. The outperformance of the Landsat-footprint NDVI in driving model relied on the spatial heterogeneity of the flux sites. Our study assessed the advantages of remote sensing data with high spatial resolution in simulating GPP, especially for areas with high heterogeneity of landscapes. This could facilitate a more accurate estimation of global ecosystem carbon sink and a better understanding of plant productivity and carbon climate feedbacks.

Приближенные цифровые модели роста и продуктивности растений (обзор)

The article presents a brief review and analysis of existing mathematical models for calculating the growth and productivity of agricultural plants. Much attention is paid to the analysis of the first models of plant growth by T.B. Robertson, F.F. Blackman, E.A. Mitcherlich and other famous researchers. Important theoretical works by I.A. Poletaeva, T.I. Eman, Yu.I. Gilderman, G.P. Kareva et al., who established and for the first time took into account the competition of plants for the amount of energy received during pho-tosynthesis on the mechanism of plant growth and their productivity. In total, more than 30 mathematical models of famous authors are indicated. The article describes a new approximate digital model of growth and productivity of agricultural plants, taking into account the main provisions and results of the cellular theory of the structure and growth of plants, obtained in the fundamental works of D.A. Sabinina, R.O. Erickson, V.B. Ivanov and other researchers. For the first time in a new growth model, it is taken into account that the relative length of a plant cell and the relative increase in this length are proportional to the difference between the maximum length and the actual value of the cell length, and the relative number of plant cells and the relative increase in this number of cells are proportional to the difference between the maximum number of cells and the actual number of cells. For the first time a mathematical model of plant growth under the combined action of light, mineral nutrition and respiration was described. Examples of calculations are considered that show a good agreement between the actual and calculated values of plant height, productivity of different crops for different climatic and soil conditions.

Assessment of Labour Productivity and the Factors of Its Increase in European Union 27 and Ukrainian Economies

The article is devoted to identifying the impact of factors on the increasing labour productivity in the economies of the European Union-27 (EU-27) and Ukraine. The system of influencing factors that must be taken into account in the assessment of the labour productivity indicator was substantiated. The factors are based on the most significant indicators of innovative development (innovative activity; formation and the use of personnel; the state of use of fixed capital; the composition of the payroll budget; investment activity; and the use of working time). Based on the use of the method of linearization of the labour productivity model, the rating coefficients of the influence of factors for the economies of the EU-27 and Ukraine were determined. It has been proven that the following factors have a significant positive impact on labour productivity: an increase in the costs of scientific research and development; the growth of enterprises expenditures on research and development (R&D) in high-tech sectors; the increase in the share of scientific research personnel and researchers in the total number of the employed population; the growth of costs for the innovation of industrial enterprises; an introduction to the production of new technological processes and innovative types of products; the purchase of machinery, equipment, and software; an increase in the share of the employed population that has a basic higher education (bachelor’s degree); the growth in the share of the employed population that has a full higher education (master’s degree); the increase in the share of enterprises providing training; the increase in the capital–labour ratio; the technological equipment of labour; the machine equipment of labour; the renewal of fixed capital; and the increase in the level of intellectualization of fixed capital. The available reserves for increasing the labour productivity in EU-27 economies and Ukraine were clarified, and recommendations for managing the labour productivity in the conditions of innovative development have been provided. This study gains relevance when Ukraine has assumed the official status of an EU candidate country and the advantages and potential difficulties in the membership process should be evaluated. Labour productivity will be one of the key factors in the post-conflict recovery of the economy.

Photorespiration in eelgrass (Zostera marina L.): A photoprotection mechanism for survival in a CO2-limited world.

Photorespiration, commonly viewed as a loss in photosynthetic productivity of C3 plants, is expected to decline with increasing atmospheric CO2, even though photorespiration plays an important role in the oxidative stress responses. This study aimed to quantify the role of photorespiration and alternative photoprotection mechanisms in Zostera marina L. (eelgrass), a carbon-limited marine C3 plant, in response to ocean acidification. Plants were grown in controlled outdoor aquaria at different [CO2]aq ranging from ~55 (ambient) to ~2121 μM for 13 months and compared for differences in leaf photochemistry by simultaneous measurements of O2 flux and variable fluorescence. At ambient [CO2], photosynthesis was carbon limited and the excess photon absorption was diverted both to photorespiration and non-photochemical quenching (NPQ). The dynamic range of NPQ regulation in ambient grown plants, in response to instantaneous changes in [CO2]aq, suggested considerable tolerance for fluctuating environmental conditions. However, 60 to 80% of maximum photosynthetic capacity of ambient plants was diverted to photorespiration resulting in limited carbon fixation. The photosynthesis to respiration ratio (P E : R D) of ambient grown plants increased 6-fold when measured under high CO2 because photorespiration was virtually suppressed. Plants acclimated to high CO2 maintained 4-fold higher P E : R D than ambient grown plants as a result of a 60% reduction in photorespiration. The O2 production efficiency per unit chlorophyll was not affected by the CO2 environment in which the plants were grown. Yet, CO2 enrichment decreased the light level to initiate NPQ activity and downregulated the biomass specific pigment content by 50% and area specific pigment content by 30%. Thus, phenotypic acclimation to ocean carbonation in eelgrass, indicating the coupling between the regulation of photosynthetic structure and metabolic carbon demands, involved the downregulation of light harvesting by the photosynthetic apparatus, a reduction in the role of photorespiration and an increase in the role of NPQ in photoprotection. The quasi-mechanistic model developed in this study permits integration of photosynthetic and morphological acclimation to ocean carbonation into seagrass productivity models, by adjusting the limits of the photosynthetic parameters based on substrate availability and physiological capacity.

Drivers of site productivity for oak in Poland

The site index (SI) is the most commonly used and representative measure of the phytocentric approach; it evaluates the site productivity based on the stand height and age. In the case of mixed stands with complex structures, phytocentric methods are very limited, while in non-forest areas, they are not ap­plicable. In situations where the applicability of phytocentric methods is limited, the site productivity is de­termined by geocentric methods. Geocentric methods allow direct modelling of site productivity, expressed by SI predicted from various environmental variables. The aim of this study was to develop a geocentric model for oak. Site productivity expressed by SI was described by the environmental variables and stand characteristics. To develop the SI model, we used the data from 2490 NFI plots with dominant oak species (Quercus sessilis and Quercus robur). A generalized additive model was used in modelling site productivity. We documented a significant relationship between SI and the environmental variables, age of stands and stand density. Furthermore, the site productivity for oak is shaped by climate factors, soil type, geology, and altitude. The model developed based on the geocentric method, explained 55.1% of the variation of SI

Photosynthetic Carbon Uptake Correlates with Cell Protein Content during Lipid Accumulation in the Microalga Chlorella vulgaris NIES 227

Large-scale microalgae cultivation for biofuel production is currently limited by the possibility of maintaining high microalgae yield and high lipid content, concomitantly. In this study, the physiological changes of Chlorella vulgaris NIES 227 during lipid accumulation under nutrient limitation was monitored in parallel with the photosynthetic capacity of the microalgae to fix carbon from the proxy of oxygen productivity. In the exponential growth phase, as the biomass composition did not vary significantly (approx. 53.6 ± 7.8% protein, 6.64 ± 3.73% total lipids, and 26.0 ± 9.2% total carbohydrates of the total biomass dry-weight), the growth capacity of the microalgae was preserved (with net O2 productivity remaining above (4.44 ± 0.93) × 10−7 g O2·µmol PAR−1). Under nutrient limitation, protein content decreased (minimum of approx. 18.6 ± 6.0%), and lipid content increased (lipid content up to 56.0 ± 0.8%). The physiological change of the microalgae was associated with a loss of photosynthetic activity, down to a minimum (1.27 ± 0.26) × 10−7 g O2·µmol PAR−1. The decrease in photosynthetic O2 productivity was evidenced to correlate to the cell internal-protein content (R2 = 0.632, p = 2.04 × 10−6, N = 25). This approach could serve to develop productivity models, with the aim of optimizing industrial processes.

Prediction of truck productivity at mine sites using tree-based ensemble models combined with Gaussian mixture modelling

ABSTRACT In the past decade, machine learning (ML) algorithms have been widely applied to build prediction models for various mining applications. However, no research has been reported that forecasts truck productivity using ML algorithms. In this study, two tree-based ensemble learning algorithms, including random forest (RF) and gradient boosting regression (GBR), were proposed in combination with Gaussian mixture modelling (GMM) to train prediction models of truck productivity. GMM was adopted as a clustering technique to extract a latent variable from the training dataset. Multiple linear regression (MLR) and decision tree (DT) as single learning algorithms were used to construct prediction models to be compared with the tree-based ensemble models. The results showed that the tree-based ensemble models performed better than single models in predicting truck productivity with and without GMM clustering. Furthermore, GMM significantly increased the predictability of truck productivity prediction models by considering the latent variable. From the relative importance analysis, haul distance was the most influential factor among the observed input variables. Finally, the GMM-RF and GMM-GBR models with high accuracy were the proposed models for predicting truck productivity at mine sites.

Using the SCOPE model for potato growth, productivity and yield monitoring under different levels of nitrogen fertilization

Most applications of remote sensing in agricultural crop monitoring use multispectral imaging techniques, but with upcoming hyperspectral missions, the opportunity arises to better estimate pigment absorption and crop structure by exploiting the full solar reflective spectrum. In this study, we demonstrate how hyperspectral time series can be used with the Soil Canopy Observation of Photochemistry and Energy fluxes (SCOPE) model to estimate crop yield variability among fields, crop varieties and nitrogen treatments generically, i.e. without a calibration with in situ , data. Field experiments were conducted in two potato fields in the Netherlands between May and September 2019. The fields were planted with five varieties of potato, under three nitrogen fertilization treatments. By fitting the model to the full VNIR-SWIR spectrum of measured hyperspectral reflectance, we retrieved the model input parameters of Leaf Area Index (LAI), leaf chlorophyll content (Cab) and leaf water content (Cw) and simulated the photosynthesis throughout the season using data of local Automatic Weather Stations (AWS). Statistical analysis of measured and retrieved traits of LAI, Cab and canopy water content showed that two fields responded differently to the treatments, exhibiting fewer classes than were expected based on the experimental design. Potato yield, which was estimated as the sum of photosynthesis flux multiplied by the harvest index of 0.64, correlated with the measured tuber dry weight with R 2 0.36 and RMSE 2.5 t ha −1 . This study demonstrates that even in the absence of crop or variety specific information, hyperspectral reflectance and local weather data ingested into SCOPE can explain a substantial part of the observed variability in yield among fields. • Time series of hyperspectral canopy reflectance were recorded in two potato fields. • The variation in traits was created by nitrogen fertilization treatments. • SCOPE model retrieved plant traits contained information about treatments. • SCOPE-simulated accumulated photosynthesis correlated with the final dry yield. • SCOPE model is more generic than dimensionality reduction methods.

Quantity and Quality in Scientific Productivity: The Tilted Funnel Goes Bayesian.

The equal odds baseline model of creative scientific productivity proposes that the number of high-quality works depends linearly on the number of total works. In addition, the equal odds baseline implies that the percentage of high-quality works and total number of works are uncorrelated. The tilted funnel hypothesis proposes that the linear regression implied by the equal odds baseline is heteroscedastic with residual variance in the quality of work increasing as a function of quantity. The aim of the current research is to leverage Bayesian statistical modeling of the equal odds baseline. Previous work has examined the tilted funnel by means of frequentist quantile regression, but Bayesian quantile regression based on the asymmetric Laplace model allows for only one conditional quantile at a time. Hence, we propose additional Bayesian methods, including Poisson modeling to study conditional variance as a function of quantity. We use a classical small sample of eminent neurosurgeons, as well as the brms Bayesian R package, to accomplish this work. In addition, we provide open code and data to allow interested researchers to extend our work and utilize the proposed modeling alternatives.

Remote sensing-based vegetation indices for monitoring rice crop phenology and productivity in cikakak sub-district, sukabumi regency

Paddy is the main food crop consumed by most of the Indonesian daily. It is supported by the fact that rice consumption reached up to 1.55 tons in 2018, and Sukabumi Regency is among the five largest rice producer in Indonesia. The goal of this study is to determine the rice crop phenology and estimate rice productivity in one year of the planting season. The rice crop phenology was analyzed by comparing vegetation indices such as NDVI, ARVI, and MSAVI in different temporal situations. Vegetation indices derived from Sentinel-2 imageries via Google Earth Engine. A rice crop productivity model developed from the statistical relationship between in-situ-based productivity data and vegetation indices applied to estimate productivity per each paddy field at a sub-district level. Also, the estimation will be associated with elevation data. The results of this study are the pattern of the rice crop phenology and the number of harvesting time in one year-planting season. The rice productivity in the Cikakak sub-district estimates at the range between 6.50 to 8.87 tonnes per hectare. Estimation models utilizing NDVI and MSAVI are showing similar results, which averagely at 8.89 and 8.87 tonnes per hectare, respectively. Rice fields with high productivity are mostly located at 250 to 500 meters above sea level.

Productivity Model of Labour on Construction Projects in Indonesia

Productivity Model of Labour on Construction Projects in Indonesia

Performance of Diversified Legume Entailing Ultra High Intensity Rice based Cropping System Models for Higher Productivity, Profitability and Sustainability

Background: An intensive cropping system involving legume and vegetable crops not only highly productive and profitable but should also be stable over time and maintains soil health in present scenario. So, keeping this point in view the present investigation was carried out to evaluate the productivity, profitability and sustainability of legume entailing different ultra high intensity rice based cropping system models under irrigated sub-tropics. Methods: The field experiment was conducted at Research Farm, SKUAST-Jammu, Main Campus, Chatha for two consecutive years from kharif 2019 to summer season of 2021. The experiment consisting of five legume entailing rice based cropping system models of cropping intensity varying from 300-600% viz. T1: Rice (Basmati-370) - Wheat (HD-3086) - Cowpea (Lobia Super-60) having 300% cropping intensity, T2: Rice (Basmati-564) - Potato (Kufri Badshah) - Wheat (Raj-3765) - Mixed fodder (Maize+Cowpea+Charri in 2:2:1 ratio) having 400% cropping intensity, T3: Rice (SJR-129) - Knol khol (G-40) - Potato (Kufri Sindhuri) - Green gram (IPM 02-3) having 400% cropping intensity, T4: Rice (Pusa-1121) - Radish (CR-45) - Green onion (Nasik Red) - French bean (Anupama) - Okra (Seli special) having 500% cropping intensity in relay cropping from fourth crop onwards and T5: Rice (IET-1410) - Fenugreek (JF-07) - Knol khol (G-40) - Green onion (Nasik Red) - Dry onion (Selection-1) - Black gram (Pant U-19) having 600% cropping intensity in relay cropping from third crop onwards was laid out in a randomized block design with four replications. Result: The results obtained under two years research study revealed that treatment T4 recorded significantly highest basmati-370 rice equivalent system productivity followed by treatment T5. Further, treatment T4 also recorded highest system cost of cultivation, system gross returns and system net returns. System soil fertility status in terms of available NPK after completion of second crop cycle was significantly affected. Among different cropping systems, significantly higher available nitrogen and phosphorus were recorded in treatment T5 system which was at par with treatment T4.