Quantitative Performance Metrics Research Articles

FDA-led consortium studies advance quality control of targeted next generation sequencing assays for precision oncology.

Cancer is the second leading cause of mortality worldwide despite tremendous advances in treatment. The promise of precision oncology depends on accurate characterization of tumor mutations and subsequent therapy selection. The lack of tumor reference samples along with the associated next generation sequencing (NGS) technical assessments has hindered the development of NGS assays and the realization of benefits for precision oncology. The summarized results and recommendations of several seminal SEQC2 studies along with a vision of the changing landscape of precision oncology and anticipated next steps by the SEQC2 consortium are reported. Importantly, these studies utilized a new robust reference sample material which was developed and constructed to support multiple DNA and RNA-based NGS assay studies. These studies focused on a wide variety of precision oncology assay scenarios and provided guidelines for standardized analyses and best practice recommendations. The evolving landscape of precision oncology requires insights into critical factors supporting the sensitivity and reproducibility of clinical NGS assays for continued improvement in patient outcomes. Persistent development of robust reference materials, quantitative performance metrics, and actionable data analysis recommendations are needed. This series of SEQC2 studies serve to advance NGS-based assays for precision oncology and support regulatory science endeavors.

Deep Learning Based Automated Orthotopic Lung Tumor Segmentation in Whole-Body Mouse CT-Scans.

Simple SummaryThe development of more translatable orthotopic mouse models is essential in order to study lung cancer more realistically. However, a major challenge in these orthotopic mouse models is the monitoring of tumor take, tumor growth and the detection of therapeutic effects. Therefore, the aim of this study was to train and validate a deep learning algorithm for fully automatic lung tumor quantification in whole-body mouse µCBCT scans. This deep learning application enables highly accurate longitudinal evaluation of tumor volume changes in mice with minimal operator involvement in the data analysis. In addition to longitudinal quantification of tumor development, the algorithm can also be deployed to optimize the randomization and 3R animal welfare aspects of the experimental design in preclinical studies.Lung cancer is the leading cause of cancer related deaths worldwide. The development of orthotopic mouse models of lung cancer, which recapitulates the disease more realistically compared to the widely used subcutaneous tumor models, is expected to critically aid the development of novel therapies to battle lung cancer or related comorbidities such as cachexia. However, follow-up of tumor take, tumor growth and detection of therapeutic effects is difficult, time consuming and requires a vast number of animals in orthotopic models. Here, we describe a solution for the fully automatic segmentation and quantification of orthotopic lung tumor volume and mass in whole-body mouse computed tomography (CT) scans. The goal is to drastically enhance the efficiency of the research process by replacing time-consuming manual procedures with fast, automated ones. A deep learning algorithm was trained on 60 unique manually delineated lung tumors and evaluated by four-fold cross validation. Quantitative performance metrics demonstrated high accuracy and robustness of the deep learning algorithm for automated tumor volume analyses (mean dice similarity coefficient of 0.80), and superior processing time (69 times faster) compared to manual segmentation. Moreover, manual delineations of the tumor volume by three independent annotators was sensitive to bias in human interpretation while the algorithm was less vulnerable to bias. In addition, we showed that besides longitudinal quantification of tumor development, the deep learning algorithm can also be used in parallel with the previously published method for muscle mass quantification and to optimize the experimental design reducing the number of animals needed in preclinical studies. In conclusion, we implemented a method for fast and highly accurate tumor quantification with minimal operator involvement in data analysis. This deep learning algorithm provides a helpful tool for the noninvasive detection and analysis of tumor take, tumor growth and therapeutic effects in mouse orthotopic lung cancer models.

Longitudinal observational study of boxing therapy in Parkinson\u2019s disease, including adverse impacts of the COVID-19 lockdown

BackgroundParkinson’s Disease (PD) is a highly prevalent neurodegenerative disease whose incidence is increasing with an aging population. One of the most serious manifestations of PD is gait instability, leading to falls and subsequent complications that can be debilitating, even fatal. Boxing therapy (BT) uses gait and balance exercises to improve ambulation in people with PD, though its efficacy has not yet been fully proven.MethodsIn the current longitudinal observational study, 98 participants with idiopathic PD underwent twice-weekly BT sessions. Primary outcome was self-reported falls per month; secondary outcomes were quantitative and semi-quantitative gait and balance performance evaluations. Statistical methods included segmented generalized estimating equation with an independent correlation structure, binomial distribution, and log link.ResultsThe average number of self-reported falls per month per participant decreased by 87%, from 0.86 ± 3.58 prior to BT, to 0.11 ± 0.26 during BT. During the lockdown imposed by COVID-19, this increased to 0.26 ± 0.48 falls per month. Females and those > 65 years old reported the greatest increase in falls during the lockdown period. Post-lockdown resumption of BT resulted in another decline in falls, to 0.14 ± 0.33. Quantitative performance metrics, including standing from a seated position and standing on one leg, largely mirrored the pattern of falls pre-and post-lockdown.ConclusionsBT may be an effective option for many PD patients.

RadSimPE - a Radiology Workstation Simulator for Perceptual Education.

Recent studies have demonstrated the effectiveness of simulation in radiology perceptual education. While current software exists for perceptual research, these software packages are not optimized for inclusion of educational materials and do not have full integration for presentation of educational materials. To address this need, we created a user-friendly software application, RadSimPE. RadSimPE simulates a radiology workstation, displays radiology cases for quantitative assessment, and incorporates educational materials in one seamless software package. RadSimPE provides simple customizability for a variety of educational scenarios and saves results to quantitatively document changes in performance. We performed two perceptual education studies involving evaluation of central venous catheters: one using RadSimPE and the second using conventional software. Subjects in each study were divided into control and experimental groups. Performance before and after perceptual education was compared. Improved ability to classify a catheter as adequately positioned was demonstrated only in the RadSimPE experimental group. Additional quantitative performance metrics were similar for both the group using conventional software and the group using RadSimPE. The study proctors felt that it was qualitatively easier to run the RadSimPE session due to integration of educational material into the simulation software. In summary, we created a user-friendly and customizable simulated radiology workstation software package for perceptual education. Our pilot test using the software for central venous catheter assessment was a success and demonstrated effectiveness of our software in improving trainee performance.

Impression management tactics in the CEO statements of Turkish sustainability reports

AbstractIn this study, we construct a classificatory scheme of impression management tactics and apply it to a sample of sustainability report and annual report CEO statements issued by large Turkish companies. We find a wide range of impression management tactics applied in both types of CEO statements. However, impression management in sustainability report CEO statements emerges as more limited and uniform, revealing clearer prioritizations of impression management tactics when compared to annual report CEO statements. Sustainability report CEO statements emphasize tactics that allow the company to highlight carefully selected, isolated aspects of their sustainability engagement. At the same time, they rarely employ tactics that are geared towards more systematic (quantitative) performance evaluation. We attribute these differences to the way in which corporate sustainability performance is commonly conceptualized and evaluated, emphasising qualitative performance dimensions rather than the reporting of quantitative performance metrics. This results in the application of a different impression management tactics mix and limits the usefulness of sustainability reports as an accountability mechanism when compared to corporate annual reports. In addition, we provide tentative evidence for the context‐specific nature of impression management, with the choice of impression management tactics reflecting the institutional environment a company is based in.

Science needs more external evaluation, not less

When science is evaluated by bureaucrats and administrators, it is usually done by quantified performance metrics, for the purpose of economic productivity. Olof Hallonsten criticizes both the means (quantification) and purpose (economization) of such external evaluation. I share the concern that such neoliberal performance metrics are shallow, over-simplified and inaccurate, but differ in how best to oppose this reductionism. Hallonsten proposes to replace quantitative performance metrics with qualitative in-depth evaluation of science, which would keep evaluation internal to scientific communities. I argue that such qualitative internal evaluation will not be enough to challenge current external evaluation since it does little to counteract neoliberal politics, and fails to provide the accountability that science owes the public. To assure that the many worthy purposes of science (i.e. truth, democracy, well-being, justice) are valued and pursued, I argue science needs more and more diverse external evaluation. The diversification of science evaluation can go in many directions: towards both quantified performance metrics and qualitative internal assessments and beyond economic productivity to value science’s broader societal contributions. In addition to administrators and public servants, science evaluators must also include diverse counterpublics of scientists: civil society, journalists, interested lay public and scientists themselves. More diverse external evaluation is perhaps no more accurate than neoliberal quantified metrics, but by valuing the myriad contributions of science and the diversity of its producers and users, it is hopefully less partial and perhaps more just.

Secure Air Traffic Control at the Hub of Multiplexing on the Centrifugo-Pneumatic Lab-on-a-Disc Platform.

Fluidic larger-scale integration (LSI) resides at the heart of comprehensive sample-to-answer automation and parallelization of assay panels for frequent and ubiquitous bioanalytical testing in decentralized point-of-use/point-of-care settings. This paper develops a novel “digital twin” strategy with an emphasis on rotational, centrifugo-pneumatic flow control. The underlying model systematically connects retention rates of rotationally actuated valves as a key element of LSI to experimental input parameters; for the first time, the concept of band widths in frequency space as the decisive quantity characterizing operational robustness is introduced, a set of quantitative performance metrics guiding algorithmic optimization of disc layouts is defined, and the engineering principles of advanced, logical flow control and timing are elucidated. Overall, the digital twin enables efficient design for automating multiplexed bioassay protocols on such “Lab-on-a-Disc” (LoaD) systems featuring high packing density, reliability, configurability, modularity, and manufacturability to eventually minimize cost, time, and risk of development and production.

Business Jet Fly-by-Wire Control Laws Handling Qualities Flight Test Assessment

Fly-by-wire control laws for a business jet were developed and a handling qualities assessment flight test was conducted on the Calspan Variable Stability System Learjet-25. The control laws, which provide a blended normal acceleration and speed () command response type in the longitudinal axis and a stability axes roll rate and sideslip () command response type in the lateral/directional axes, were optimized to meet Level 1 requirements for a comprehensive set of stability, handling qualities, and performance specifications. The control laws were evaluated in flight by U.S. Air Force Test Pilot School and Textron Aviation test pilots using a series of handling qualities demonstration maneuvers, including pitch and roll capture and tracking tasks and an offset landing task. Quantitative performance metrics were collected, in addition to pilot handling qualities ratings and comments. Several modifications were made to the control laws based on initial pilot comments and ratings. The final results show that the optimized fly-by-wire control laws provided assigned Level 1 handling qualities for discrete tracking and offset landing tasks.

On the Coercive Nature of Research Impact Metrics: The Case Study of Altmetrics and Science Communication

ABSTRACT This article grasps the coercive character often associated to research impact metrics, in the wake of the ever-growing use of quantitative indicators for the evaluation of the academic performance. It does so by taking a Marxian perspective which underscores what are the historically determined attributes of academic labour that the functioning of impact metrics embodies, unfolding thereby what ‘impact’ really means concerning said social attributes of the scientific enterprise. Science communication via social media, and the array of metrics and indicators surrounding it, known as alternative metrics or altmetrics, are taken as a case study. This study concludes that the coercive nature of research impact metrics is grounded on the private and independent character taken by social labour in capitalist production, an historically determined form of the social reproduction that asks for academic production to be organised and regulated in an indirect fashion, behind the backs of producers themselves – in the particular case of science, by means of quantitative indicators and performance metrics. As research impact metrics bear the power to determine how and to what extent academic labourers contribute to the general productive process, their will and consciousness become thus subsumed to such reified social subject.

X-Ray Scatter Estimation Using Deep Splines.

X-ray scatter compensation is a very desirable technique in flat-panel X-ray imaging and cone-beam computed tomography. State-of-the-art U-net based scatter removal approaches yielded promising results. However, as there are no physics' constraints applied to the output of the U-Net, it cannot be ruled out that it yields spurious results. Unfortunately, in the context of medical imaging, those may be misleading and could lead to wrong conclusions. To overcome this problem, we propose to embed B-splines as a known operator into neural networks. This inherently constrains their predictions to well-behaved and smooth functions. In a study using synthetic head and thorax data as well as real thorax phantom data, we found that our approach performed on par with U-net when comparing both algorithms based on quantitative performance metrics. However, our approach not only reduces runtime and parameter complexity, but we also found it much more robust to unseen noise levels. While the U-net responded with visible artifacts, the proposed approach preserved the X-ray signal's frequency characteristics.

Learning regularized representations of categorically labelled surface EMG enables simultaneous and proportional myoelectric control

BackgroundProcessing the surface electromyogram (sEMG) to decode movement intent is a promising approach for natural control of upper extremity prostheses. To this end, this paper introduces and evaluates a new framework which allows for simultaneous and proportional myoelectric control over multiple degrees of freedom (DoFs) in real-time. The framework uses multitask neural networks and domain-informed regularization in order to automatically find nonlinear mappings from the forearm sEMG envelope to multivariate and continuous encodings of concurrent hand- and wrist kinematics, despite only requiring categorical movement instruction stimuli signals for calibration.MethodsForearm sEMG with 8 channels was collected from healthy human subjects (N = 20) and used to calibrate two myoelectric control interfaces, each with two output DoFs. The interfaces were built from (I) the proposed framework, termed Myoelectric Representation Learning (MRL), and, to allow for comparisons, from (II) a standard pattern recognition framework based on Linear Discriminant Analysis (LDA). The online performances of both interfaces were assessed with a Fitts’s law type test generating 5 quantitative performance metrics. The temporal stabilities of the interfaces were evaluated by conducting identical tests without recalibration 7 days after the initial experiment session.ResultsMetric-wise two-way repeated measures ANOVA with factors method (MRL vs LDA) and session (day 1 vs day 7) revealed a significant (p<0.05) advantage for MRL over LDA in 5 out of 5 performance metrics, with metric-wise effect sizes (Cohen’s d) separating MRL from LDA ranging from left|dright|=0.62 to left|dright|=1.13. No significant effect on any metric was detected for neither session nor interaction between method and session, indicating that none of the methods deteriorated significantly in control efficacy during one week of intermission.ConclusionsThe results suggest that MRL is able to successfully generate stable mappings from EMG to kinematics, thereby enabling myoelectric control with real-time performance superior to that of the current commercial standard for pattern recognition (as represented by LDA). It is thus postulated that the presented MRL approach can be of practical utility for muscle-computer interfaces.

Examining municipal guidelines for users of shared E-Scooters in the United States

The emergence of shared electric scooters (E-Scooters) has drawn the significant attention of local governments in many urban areas. Despite the fast growth in the number of trips, current guidelines for using E-Scooters have consistently experienced lags in development. Existing guidelines, in some cities, are rather vague and vary drastically across different areas. This paper aims to analyze current municipal requirements for the use of E-Scooters in the U.S., and to discuss more gaps for improvement. Specifically, E-Scooter user guidelines of 156 cities were explored. A multifaceted analysis was conducted to characterize the distinct features of E-Scooter user guidelines. A total of sixteen key attributes were identified and two categorizing procedures were implemented in the analysis. The comparative results show the completeness of information and similarities between cities. We conclude that municipalities should introduce more actionable guidelines driven by quantitative performance metrics.

Water Feature Extraction, Enhancement and Change Detection of Multi-Temporal Satellite Images using MNDWI2-PCA

Water feature extraction is a challenging task in remote sensing. In this research work, a new water index is implemented for easy identification of water pixels. The Area of interest is extracted with desired shape file. Here water bodies from kalahasti region are extracted, which is in Chittoor district. The Water indices are used to identify water pixels from Landsat-8 image, which has high spectral resolution. This image is multi-spectral image comprising of eleven bands. Interactive supervised classification is implemented for segmenting the satellite image. The image is classified into two categories i.e. water bodies and non-water bodies. Then MNDWI2-PC (Normalized Difference Water Index2-Principal Component) is applied to LANDSAT-8 image. Then this image is segmented into water bodies and non-water bodies. Finally accuracy assessment is carried out by confusion or error matrix. Quantitative parameters such as Overall Accuracy (OA), Kappa Coefficient (KC), Overall Kappa Coefficient (OKC) User’s accuracy (UA), Producer’s Accuracy (PA), and F1 score (F-Measure) are calculated for this multi-spectral satellite imagery. The algorithm reduced misclassification of water pixels with urban pixels, vegetation and other land covers. The algorithm outperforms in terms of quantitative performance metrics.

Using numerical methods to screen magnetocaloric materials in an active magnetic regenerative cycle

• A parametric convective heat transfer term is used to assess t span & cooling power. • Four major magnetocaloric materials are screened. • Optimal fluid velocity depends on the magnetic transition temperature. A 2-dimensional numerical model of a room temperature operating Active Magnetic Regenerator (AMR) that comprises of a regenerator, hot and cold heat exchangers, heat transfer fluid is developed. The regenerator is made of a magnetocaloric material (MCM) which heats up upon applying a magnetic field, H, and cools down when the field is removed; thus, making it the most essential part of an AMR. The model takes experimentally measured ∆T ad (H,T) and the C p (H,T) data as input and provides quantitative performance metrics of the magnetic cooling system, such as ∆T span and the cooling load, as output. With this model, it is possible to assess a wide range of MCMs in an AMR. 4 different MCMs were investigated using this model in terms of their ∆T span and the cooling loads—LaFe 10.96 Co 0.97 Si 1.07 , MnFeP (1-x) As (x) and AlFe 2 B 2 and Gd. During the screening of MCMs, all the important operating conditions of the device were fixed such as the fluid flow rate, ambient temperature, cycle duration, magnetic field strength. Our results indicate that Gd exhibits the maximum ∆T span with respectable cooling load, while AlFe 2 B 2 generates the lowest ∆T span . Even though LaFeCoSi compound did not perform as well as Gd, it could be the MCM choice for the realization of magnetic refrigeration on a global scale due to its much lower cost.

A Viable New Strategy for the Discovery of Peptide Proteolytic Cleavage Products in Plant-Microbe Interactions.

Small peptides that are proteolytic cleavage products (PCPs) of less than 100 amino acids are emerging as key signaling molecules that mediate cell-to-cell communication and biological processes that occur between and within plants, fungi, and bacteria. Yet, the discovery and characterization of these molecules is largely overlooked. Today, selective enrichment and subsequent characterization by mass spectrometry-based sequencing offers the greatest potential for their comprehensive characterization, however qualitative and quantitative performance metrics are rarely captured. Herein, we addressed this need by benchmarking the performance of an enrichment strategy, optimized specifically for small PCPs, using state-of-the-art de novo-assisted peptide sequencing. As a case study, we implemented this approach to identify PCPs from different root and foliar tissues of the hybrid poplar Populus × canescens 717-1B4 in interaction with the ectomycorrhizal basidiomycete Laccaria bicolor. In total, we identified 1,660 and 2,870 Populus and L. bicolor unique PCPs, respectively. Qualitative results supported the identification of well-known PCPs, like the mature form of the photosystem II complex 5-kDa protein (approximately 3 kDa). A total of 157 PCPs were determined to be significantly more abundant in root tips with established ectomycorrhiza when compared with root tips without established ectomycorrhiza and extramatrical mycelium of L. bicolor. These PCPs mapped to 64 Populus proteins and 69 L. bicolor proteins in our database, with several of them previously implicated in biologically relevant associations between plant and fungus.

Adaptive Interval Type-2 Fuzzy Logic Control of a Three Degree-of-Freedom Helicopter

This paper combines interval type-2 fuzzy logic with adaptive control theory for the control of a three degree-of-freedom (DOF) helicopter. This strategy yields robustness to various kinds of uncertainties and guaranteed stability of the closed-loop control system. Thus, precise trajectory tracking is maintained under various operational conditions with the presence of various types of uncertainties. Unlike other controllers, the proposed controller approximates the helicopter’s inverse dynamic model and assumes no a priori knowledge of the helicopter’s dynamics or parameters. The proposed controller is applied to a 3-DOF helicopter model and compared against three other controllers, i.e., PID control, adaptive control, and adaptive sliding-mode control. Numerical results show its high performance and robustness under the presence of uncertainties. To better assess the performance of the control system, two quantitative tracking performance metrics are introduced, i.e., the integral of the tracking errors and the integral of the control signals. Comparative numerical results reveal the superiority of the proposed method by achieving the highest tracking accuracy with the lowest control effort.

Clinical deployment of AI for prostate cancer diagnosis

Clinical deployment of AI for prostate cancer diagnosis

Fréchet PDF based Matched Filter Approach for Retinal Blood Vessels Segmentation

Background and ObjectiveRetinal pathology diseases such as glaucoma, obesity, diabetes, hypertension etc. have deadliest impact on life of human being today. Retinal blood vessels consist of various significant information which are helpful in detection and treatment of these diseases. Therefore, it is essential to segment these retinal vessels. Various matched filter approaches for segmentation of retinal blood vessels are reported in the literature but their kernel templates are not appropriate to vessel profile resulting poor performance. To overcome this, a novel matched filter approach based on Fréchet probability distribution function has been proposed. MethodsImage processing operations which we have used in the proposed approach are basically divided into three major stages viz; pre processing, Fréchet matched filter and post processing. In pre processing, principle component analysis (PCA) method is used to convert color image into grayscale image thereafter contrast limited adaptive histogram equalization (CLAHE) is applied on obtained grayscale to get enhanced grayscale image. In Fréchet matched filter, exhaustive experimental tests are conducted to choose optimal values for both Fréchet function parameters and matched filter parameters to design new matched filter. In post processing, entropy based optimal thresholding technique is applied on obtained MFR image to get binary image followed by length filtering and masking methods are applied to generate to a clear and whole vascular tree. ResultsFor evaluation of the proposed approach, quantitative performance metrics such as average specificity, average sensitivity and average accuracy and root mean square deviation (RMSD) are computed in the literature. We found the average specificity 97.24%, average sensitivity 72.78%, average accuracy 95.09% for STARE dataset while average specificity 97.61%, average sensitivity 73.07%, average accuracy 95.44% for DRIVE dataset. Average RMSD values are found 0.07 and 0.04 for STARE and DRIVE databases respectively. ConclusionsFrom experimental results, it can be observed that our proposed approach outperforms over latest and prominent works reported in the literature. The cause of improved performance is due to better matching between vessel profile and Fréchet template.

Shear Induced Non-Linear Elasticity Imaging: Elastography for Compound Deformations.

The goal of non-linear ultrasound elastography is to characterize tissue mechanical properties under finite deformations. Existing methods produce high contrast non-linear elastograms under conditions of pure uni-axial compression, but exhibit bias errors of 10-50% when the applied deformation deviates from the uni-axial condition. Since freehand transducer motion generally does not produce pure uniaxial compression, a motion-agnostic non-linearity estimator is desirable for clinical translation. Here we derive an expression for measurement of the Non-Linear Shear Modulus (NLSM) of tissue subject to combined shear and axial deformations. This method gives consistent nonlinear elasticity estimates irrespective of the type of applied deformation, with a reduced bias in NLSM values to 6-13%. The method combines quasi-static strain imaging with Single-Track Location-Shear Wave Elastography (STL-SWEI) to generate local estimates of axial strain, shear strain, and Shear Wave Speed (SWS). These local values were registered and non-linear elastograms reconstructed with a novel nonlinear shear modulus estimation scheme for general deformations. Results on tissue mimicking phantoms were validated with mechanical measurements and multiphysics simulations for all deformation types with an error in NLSM of 6-13%. Quantitative performance metrics with the new compound-motion tracking strategy reveal a 10-15 dB improvement in Signal-to-Noise Ratio (SNR) for simple shear versus pure compressive deformation for NLSM elastograms of homogeneous phantoms. Similarly, the Contrast-to-Noise Ratio (CNR) of NLSM elastograms of inclusion phantoms improved by 25-30% for simple shear over pure uni-axial compression. Our results show that high fidelity NLSM estimates may be obtained at ~30% lower strain under conditions of shear deformation as opposed axial compression. The reduction in strain required could reduce sonographer effort and improve scan safety.

Продуктивність короткоротаційної сівозміни з різними бобовими культурами на чорноземі типовому

Goal. To analyze results of long-term research in productivity of different short field crop rotations in conditions of left bank Forest-steppe of Ukraine. Methods. General scientific and special: long-term field — to determine quantitative performance metrics of short crop rotations, and analytical. Results. They determined the yield of crops in crop rotations, as well as the calculated productivity of different 4-filed rotations. The positive role is emphasized by leguminous predecessors for leading grain crop of Ukraine — winter wheat. It is revealed that the use of pea, lentil and vetch-oat mixture in the short crop rotation increases the yields of winter wheat and root crops of the 3rd culture of crop rotation — sugar beet. The influence of crop rotations on crop yield in the 4th year of the rotation (spring barley) is somewhat reduced. The role of fallow in the crop rotation is known. Numerous studies have established that in the conditions of insufficient and unstable moistening the best precursor for winter wheat is clean fallow. The fallow makes it possible to prepare the soil, retain enough moisture and nutrients in the form available to plants practically at any weather conditions. This helps to obtain high yields of winter wheat, but the productivity of the 4-field crop rotations with this precursor is greatly reduced. This primarily happens due to the lack of products in the field of clean fallow. Conclusions. In the conditions of unstable moistening of the left bank Forest-steppe of Ukraine on typical chernozem, the higher output of food-protein units was got in the crop rotations with soybean, pea, lentil and vetch-oat mixture. The worse indices were got in the rotation with Lathyrus sativus L. The low productivity had short crop rotations with silage maize and beans. Studies revealed a significant reduction in the productivity of crop rotation with a clean fallow. It provided minimal productivity due to the lack of production in the first field.