Yield Model Research Articles

Construction and Optimization of Integrated Yield Prediction Model Based on Phenotypic Characteristics of Rice Grown in Small–Scale Plantations

An intelligent prediction model for rice yield in small-scale cultivation areas can provide precise forecasting results for farmers, rice planting enterprises, and researchers, holding significant importance for agricultural industries and crop science research within small regions. Although machine learning can handle complex nonlinear problems to enhance prediction accuracy, further improvements in models are still needed to accurately predict rice yields in small areas facing complex planting environments, thereby enhancing model performance. This study employs four rice phenotypic traits, namely, panicle angle, panicle length, total branch length, and grain number, along with seven machine learning methods—multiple linear regression, support vector machine, MLP, random forest, GBR, XGBoost, and LightGBM—to construct a yield prediction model group. Subsequently, the top three models with the best performance in individual model predictions are integrated using voting and stacking ensemble methods to obtain the optimal integrated model. Finally, the impact of different rice phenotypic traits on the performance of the stacked ensemble model is explored. Experimental results indicate that the random forest model performs best after individual machine learning modeling, with RMSE, R2, and MAPE values of 0.2777, 0.9062, and 17.04%, respectively. After model integration, Stacking–3m demonstrates the best performance, with RMSE, R2, and MAPE values of 0.2483, 0.9250, and 6.90%, respectively. Compared to the performance after random forest modeling, the RMSE decreased by 10.58%, R2 increased by 1.88%, and MAPE decreased by 0.76%, indicating improved model performance after stacking ensemble. The Stacking–3m model, which demonstrated the best comprehensive evaluation metrics, was selected for model validation, and the validation results were satisfactory, with MAE, R2, and MAPE values of 8.3384, 0.9285, and 0.2689, respectively. The above research findings demonstrate that this integrated model possesses high practical value and fills a gap in precise yield prediction for small-scale rice cultivation in the Yunnan Plateau region.

Monitoring Yield and Quality of Forages and Grassland in the View of Precision Agriculture Applications—A Review

The potential of precision agriculture (PA) in forage and grassland management should be more extensively exploited to meet the increasing global food demand on a sustainable basis. Monitoring biomass yield and quality traits directly impacts the fertilization and irrigation practises and frequency of utilization (cuts) in grasslands. Therefore, the main goal of the review is to examine the techniques for using PA applications to monitor productivity and quality in forage and grasslands. To achieve this, the authors discuss several monitoring technologies for biomass and plant stand characteristics (including quality) that make it possible to adopt digital farming in forages and grassland management. The review provides an overview about mass flow and impact sensors, moisture sensors, remote sensing-based approaches, near-infrared (NIR) spectroscopy, and mapping field heterogeneity and promotes decision support systems (DSSs) in this field. At a small scale, advanced sensors such as optical, thermal, and radar sensors mountable on drones; LiDAR (Light Detection and Ranging); and hyperspectral imaging techniques can be used for assessing plant and soil characteristics. At a larger scale, we discuss coupling of remote sensing with weather data (synergistic grassland yield modelling), Sentinel-2 data with radiative transfer modelling (RTM), Sentinel-1 backscatter, and Catboost–machine learning methods for digital mapping in terms of precision harvesting and site-specific farming decisions. It is known that the delineation of sward heterogeneity is more difficult in mixed grasslands due to spectral similarity among species. Thanks to Diversity-Interactions models, jointly assessing various species interactions under mixed grasslands is allowed. Further, understanding such complex sward heterogeneity might be feasible by integrating spectral un-mixing techniques such as the super-pixel segmentation technique, multi-level fusion procedure, and combined NIR spectroscopy with neural network models. This review offers a digital option for enhancing yield monitoring systems and implementing PA applications in forages and grassland management. The authors recommend a future research direction for the inclusion of costs and economic returns of digital technologies for precision grasslands and fodder production.

Climatic Condition–Based Comparative Study of Deep Learning Models for Yield Forecasting in Smart Agriculture

Climatic Condition–Based Comparative Study of Deep Learning Models for Yield Forecasting in Smart Agriculture

Creep model of bond-degradation in deep granite based on variable radius particle clump

The creep failure of rocks is related to its microstructure, external loading and time. A nonlinear yield model was introduced to describe the variation in the cohesion and friction angle with plastic strain and intergranular stress. The mechanical properties and creep characteristics of deep granite were obtained by indoor tests, and a variable radius particle clump model was constructed based on the particle flow method. The bond-weakening-friction-strengthening model was combined with the parallel bond stress corrosion method to establish the bond-degradation creep model of granite. The creep failure time, creep rate and tension and shear fractures number of the parallel bond stress corrosion model and the bond-degradation creep model were compared and analyzed to verify the applicability of the model. The fracture evolution law of deep roadway surrounding rock was studied based on the bond-degradation creep model. The results show that the rock failure characteristics and tension-compression ratio obtained by the variable radius particle clump modeling method are closer to the actual situation. Compared with the parallel bond stress corrosion model, the creep failure time of the bond-degradation creep model is shorter, more microfractures are generated during the failure process, and the numerical creep curves are more consistent with the test curves. The deep roadway vault shear failure and sidewall plate crack failure characteristics calculated based on the bond-degradation creep model are basically similar to the actual project situation. The bond-degradation creep model can better simulate the creep damage process of rocks under high stress, and is more suitable for analyzing the fracture evolution law of surrounding rock in deep hard rock cavern.

Energy yield framework to simulate thin film CIGS solar cells and analyze limitations of the technology.

This study presents a comprehensive evaluation of Copper Indium Gallium Selenide (CIGS) solar technology, benchmarked against crystalline silicon (c-Si) PERC PV technology. Utilizing a newly developed energy yield model, we analyzed the performance of CIGS in various environmental scenarios, emphasizing its behavior in low-light conditions and under different temperature regimes. The model demonstrated high accuracy with improved error metrics of normalized mean bias error (nMBE) ~1% and normalized root mean square error (nRMSE) of ~8%-20% in simulating rack mounted setup and integrated PV systems. Key findings reveal that the CIGS technology, while slightly underperforming in integrated, low-irradiance setups, shows comparable or superior performance to c-Si PERC technology in high-irradiance and high-temperature conditions. A significant focus of the study was on the low-light performance of CIGS, where it exhibited notable voltage losses. Our research highlights the importance of reducing the diode ideality factor for enhancing CIGS power conversion efficiency, particularly In low-light conditions. These insights provide a pathway for future research and technological improvements, emphasizing defect engineering, passivation strategies to advance the understanding and application of the CIGS technology.

A predictive model for biomass waste pyrolysis yield: Exploring the correlation of proximate analysis and product composition

A predictive model for biomass waste pyrolysis yield: Exploring the correlation of proximate analysis and product composition

Field-level sugarcane yield estimation utilizing Sentinel-2 time-series and machine learning

This work focused on developing a methodology for using machine learning (ML) approaches to establish a pre-harvest yield prediction model for sugarcane at field level by integrating time-series remote sensing imagery data with ML techniques. Ground truth agro data and thirty-one spectral vegetation indices were extracted from Sentinel-2 imagery and were considered for yield modeling. A two-level feature selection technique was used to determine the most significant variables that best correlated with sugarcane yield to predict yield in advance. Seven ML algorithms, including those based on regularization, decision trees, and ensemble methods like boosting, were used to predict yield. The approach achieved the highest R2 score of 0.73 and the lowest root mean squared error (RMSE) of 13.45 t/ha with random forest (RF) among the seven ML models tested. Furthermore, all feature selection procedures identified normalized difference red edge (NDRE), red edge chlorophyll index (RECI), and ratio vegetation index (RVI) as major yield-driving variables. The experiments during feature selection demonstrated the potential of red edge spectral bands in development of a reliable sugarcane-yield prediction approach. The RF model obtained using the proposed methodology outperforms the two baseline models developed using NDVI and GNDVI indices, with an improved RMSE of 16-18%.

Predictive Modelling of Local Currency Sovereign Debt yields using Machine Learning

This paper explores applying Statistical Machine Learning (ML) models to forecast local currency 10-year bond yields in 17 Emerging Markets (EM) economies, using Economic & Financial Indicators as feature parameters. The models considered include Random Forest (RF), Gradient Boosting (GB), Lasso Regression, Ridge Regression, and Linear Regression (LR). The performance is assessed using three key metrics: Mean Squared Error (MSE), R2 on the testing set, and R2 on Cross-Validation (CV). The results show that RF and GB models generally outperform traditional linear models, showing lower MSE and higher R2 values, indicating better accuracy in their predictions. By exploring the practical application of those models in the context of EM economies, this paper contributes valuable insights into the predictive modelling of sovereign debt yields, which is crucial for investors and policymakers. The findings suggest that ML models, with their ability to capture non-linear relationships, offer a more reliable approach to predicting 10-year bond yields in EM economies.

A New Yield Surface for Cemented Paste Backfill Based on the Modified Structured Cam-Clay

Cemented paste backfill (CPB) is a cemented void filling method gaining popularity over traditional hydraulic or rockfill methods. As mining depth increases, CPB-filled stopes are subjected to higher confining pressures. Due to the soil triaxial apparatus limitations, as the conventional method of triaxial testing on CPB, no confining pressures higher than 5 MPa can be applied to CPB over a range of curing time. This lack of data introduces uncertainty in predicting CPB behavior, potentially leading to an overestimation of the required strength. To address this, this study introduces a new testing method that allows for higher confinement beyond traditional limitations by modifying the Hoek triaxial cell to accommodate low-strength materials. This study then investigates the coupled influence of confining pressure and curing time (hydration) on CPB characteristics, specifically examining the impacts of different curing times and confining pressures on the mechanical and rheological properties of CPB. A total of 75 triaxial tests were conducted using 42 mm cylinder shape samples at five various curing times from 7 to 96 days, and applied at low and high confinement condition levels (0.5 to 30 MPa). The results reveal that hydration and confinement positively impact the CPB strength. The modified structured Cam-Clay model was selected to predict the behavior, and its yield surface was updated using the experimental results. The proposed yield model can be utilized to describe CPB material subjected to various curing and pressure conditions underground.

СРАВНИТЕЛЬНАЯ ОЦЕНКА НОВЫХ СИСТЕМ ЗАЩИТЫ И ПРОГНОЗИРОВАНИЯ УРОЖАЙНОСТИ ЗЕРНОВЫХ КУЛЬТУР НА ОСНОВЕ ДАННЫХ NDVI

The purpose of the study is to evaluate the effectiveness of the use of a new biopesticide and develop a predictive model for crop yields based on Earth remote sensing data. Tasks – acquisition, processing and analysis of retrospective data and current seasonal NDVI values for spring barley and winter wheat; comparative evaluation of the action of a new biological plant protection product (PPP) of the protective and stimulating action "Nigor ++"; numerical assessment of the long-term correlation relationship between the maximum values of NDVI and crop yields and the conclusion about the possibility of using the vegetation index as an indicator of crop yields with the development of an adequate regression equation under the conditions of the experimental production farm of the Orel State Agrarian University. Field studies for the 2021 season were carried out at the pilot production sites of the Orel State Agrarian University. Soil type is gray forest. Crops are winter wheat Moskovskaya 39 (crop area 48.1 ha), spring barley Raushan (crop area – 17.4 ha). Sowing of winter wheat was carried out on 09/10/2020, spring barley – on 05/07/2021 with seeds treated with the Scarlet fungicide. The dynamics of changes in the NDVI index for crops treated with a new biological product is 10–15 days ahead of the control, up to the heading phase, and the maximum values of the vegetation index of the experimental crops are 5–6 % higher than the control plots. The average long-term correlation coefficients between yield and peak NDVI values are 0.79 for winter wheat and 0.75 for spring barley, which confirms the reliability of using the maximum seasonal values of the vegetation index as a controlled variable in the predictive yield model. The error in assessing the yield of crops, made using the proposed predictive models, relative to the actual yield values does not exceed 6.7 %. This gives the right to recommend the obtained models for planning production and economic tasks in the experimental production facility of the university.

Suspended sediment yield estimation using geomorphologic instantaneous unit sedimentgraph: a case study from the Southern Caspian Sea, Iran

ABSTRACT This study develops and validates a Geomorphological-based Instantaneous Unit Sediment Graph (GIUS) model to estimate the event-based sediment yield of the Talar catchment, a significant sediment source in the southern Caspian Sea. Delivering approximately 11,190 tons of sediment annually, which directly impacts coastal dynamics and port operations, necessitates accurate sediment load estimation to the Caspian Sea. The catchment was divided into three sub-catchments, where the GIUS model was applied. Model calibration and verification utilized paired data of daily rainfall, streamflow, and suspended sediment concentration for 475 events. After separating direct runoff from streamflow, the Soil Conservation Service Curve Number (SCS-CN) model was employed to estimate the excess rainfall, serving as the primary input for the GIUS model. The erosion rate, a critical parameter in the GIUS model, was calibrated as a power function of excess rainfall depth. The results indicate that the sediment delivery ratio for the channel networks ranges from 28% to 44%. The strong correlation (CE = 0.64−0.85, SE = 0.75−1.96, R 2 = 0.85–0.92) between observed and simulated sediment yield values underscores the reliability of the developed model. This research presents a repeatable approach for event scale sediment yield modeling, essential for sustainable water resources management and soil erosion control in the region.

Hydrological Modelling of Sediment Yield in the Upper Basin Region of Kirkuk City Watershed

Mountain sedimentation and undue erosion provide significant challenges for dams since materials accumulate within reservoirs that hold water, reducing the capacity for storing living water, which is the most important goal of dam building. Iraq is one of the nations that will have a big problem getting sufficient water because its water requirement continues to climb, and the supplying countries provide less water to Iraq. The present research focuses on the upper basin of the Kirkuk City watershed in northeastern Iraq. The largest watershed region, spanning approximately 420 km2 within Al-Sulaymaniyah and Kirkuk Provinces. The objective is to quantify and foresee the sediment yield in this basin using 43 years of daily environment information and various rainstorm events with various intensities. The model was adjusted and confirmed by comparing it to the monthly mean surface flow and sediment readings obtained at the Kirkuk gauging station. The Soil and Water Assessment Tool (SWAT) model was utilized to simulate the upper basin region of Kirkuk City. The aforementioned model uses the geographic information system (GIS) software to analyse necessary information from GIS layers of the electronic contour modelling kind of soil, using of land, and coverage by integrating it with the appropriate climatic information. The kinematic erosion (KINEROS) and runoff model can simulate intricate watershed behaviour by precisely adjusting for the geographical variation of soils, distribution of rainfall sequences, and vegetation. The hydrological features of the Kirkuk city basin reveal that the subdivisions with the highest erosion rates cannot transport the debris or deposits to the reservoir, primarily due to propagation damages, percolating, and other smaller barriers. The optimal curve number (CN) was determined to be between 84 and 88, while the land cover factor (C) ranged from 0.005 to 0.04. The model validation results indicate that the Kirkuk Dam reservoir received a total volume of water estimated at 118.7 million cubic metres (MCM) and a total silt yield of 0.85 million tonnes. The “KINEROS” simulation modelling for the sediment yield closely matches the upper basin of Kirkuk watershed's deformation and sedimentation behaviour during singular rainfall events; this is particularly true for parts of watersheds located near the watershed area entrance.

Optimizing machine learning models for wheat yield estimation using a comprehensive UAV dataset

Timely and accurate wheat yield forecasts using Unmanned Aircraft Vehicles (UAV) are crucial for crop management decisions, food security, and ensuring the sustainability of agriculture worldwide. While traditional machine learning algorithms have already been used in crop yield modelling, previous research used machine learning algorithms with default parameters and did not take into account the complex, non-linear relationships between model variables. Especially, the combination of vegetation indices, soil properties, solar radiation, and wheat height at the field estimation has not been deeply analysed in scientific literature. We present a machine learning based wheat yield estimation model using comprehensive UAV datasets with the implementation of hyperparameter tuning to improve model performance. The performance of the models before and after optimisations was measured using the metrics RMSE, MAE and R2, and the results showed that the models improved after tuning. Furthermore, we find that the Random Forest (RF) and Extreme Gradient Boosting (XGBoost) models outperformed other examined models. Furthermore, a non-parametric Friedman test with a Nemenyi post-hoc test indicates that the best-performing algorithms for wheat yield estimation and prediction are RF and XGBoost models. In the final step, we utilised a SHapley Additive exPlanations approach to identify the direct impact of each input variable on the yield estimation model. Among the input variables, only the Red-Edge Chlorophyll Index, the Normalised Difference Red-Edge Index and wheat height were found to be of high explanatory power in predicting wheat yield. The optimised model is 7–12% more accurate in estimating wheat yields than traditional linear models.

Development of Integrated Fertilizer Prescription Based on STCR- IPNS for Cotton through Inductive Cum Targeted Yield Model in Thirunallar Soil Series of Karaikal, Puducherry, India

The Soil Test Crop Response (STCR) approach within the Integrated Plant Nutrient System (IPNS) is a targeted yield-based strategy that balances immediate crop nutrient needs with long-term soil health, aiming to optimize yield, fertilizer efficiency, and sustainability. Field trials on Typic Haplusterts soils in Karaikal used an inductive cum targeted yield model to develop fertilizer prescription equations (FPE) based on the nutrient requirement (NR) of crops and the contributions from soil (Cs), fertilizers (Cf), and farmyard manure (Cfym). For cotton, desired yield targets of 31, 33, and 35 q ha⁻¹ were set, and nomograms were created to determine precise fertilizer applications based on soil test values. When FYM was applied at 12.5 t ha⁻¹, it contributed 52 kg of nitrogen (N), 30 kg of phosphorus (P2O5), and 48 kg of potassium (K2O), which complemented the calculated NPK fertilizer needs. These values allowed for precise adjustments of nutrient inputs, aligning with both crop demands and the soil’s nutrient-supplying capacity, and improving efficiency while maintaining soil health. This approach provides a practical framework for farmers, enhancing yields and profitability while promoting the sustainable use of resources by balancing "fertilizing the crop" with "fertilizing the soil."

Assessment of dynamic water yield using multi scenario of LULC in Cisadane Watershed West Java, Indonesia

Uncontrolled economic development often leads to land degradation, a decline in ecosystem services, and negative impacts on community welfare. This study employs water yield (WY) modeling as a method for environmental management, aiming to provide a comprehensive understanding of the relationship between Land Use Land Cover (LULC), Land Use Intensity (LUI), and WY to support sustainable natural resource management in the Cisadane Watershed, Indonesia. The objectives include: (1) analyzing changes in WY for 2010, 2015, and 2021; (2) predicting WY for 2030 and 2050 under two scenarios—Business as Usual (BAU) and Protected Forest Area (PFA); (3) assessing the impacts of LULC and climate change on WY; and (4) exploring the relationship between LUI and WY. The Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model calculates actual and predicted WY conditions, while the Coupling Coordination Degree (CCD) analyzes the LULC-WY relationship. Results indicate that the annual WY in 2021 was 215.8 × 108 m³, reflecting a 30.42% increase from 2010. Predictions show an increasing trend in WY under both scenarios for 2030 and 2050 with different magnitudes. Rainfall contributes 88.99% more dominantly to WY than LULC. Additionally, around 50% of districts exhibited unbalanced coordination between LUI and WY in 2010 and 2020. This study reveals the importance of ESs in sustainable watershed management amidst increasing demand for natural resources due to population growth.

Soft Sensor Modeling of Acrylic Acid Yield Based on Autoencoder Long Short‐Term Memory Neural Network of Savitzky–Golay and ReliefF Algorithm

ABSTRACTAcrylic acid yield (AAY) is a key quality index in production process of acrylic acid. Meanwhile, AAY has been considered as direct characterization of productivity. Aiming at the difficulty of online measurement of AAY in acrylic acid process, a soft sensing model of AAY based on autoencoder long short‐term memory neural network (AE LSTM NN) applying Savitzky–Golay and ReliefF method is presented in this paper. Firstly, Savitzky–Golay method with denoising effect is adopted to remove industrial noise in measurement. Then, ReliefF algorithm is developed to compress characteristic variables from the result of denoising. Finally, AE LSTM is employed to predict the AAY in acrylic acid process. In contrast to LSTM, support vector machine, and artificial neural network, the root mean square error (RMSE) of the provided method is 0.0954, mean absolute error (MAE) is 0.0757, mean absolute percent error (MAPE) is 0.09%, and maximum absolute error (MaxAE) is 0.3236, which shows validity and superiority.

On crop yield modelling, predicting, and forecasting and addressing the common issues in published studies

There has been a recent surge in the number of studies that aim to model crop yield using data-driven approaches. This has largely come about due to the increasing amounts of remote sensing (e.g. satellite imagery) and precision agriculture data available (e.g. high-resolution crop yield monitor data), as well as the abundance of machine learning modelling approaches. However, there are several common issues in published studies in the field of precision agriculture (PA) that must be addressed. This includes the terminology used in relation to crop yield modelling, predicting, forecasting, and interpolating, as well as the way that models are calibrated and validated. As a typical example, many studies will take a crop yield map or several plots within a field from a single season, build a model with satellite or Unmanned Aerial Vehicle (UAV) imagery, validate using data-splitting or some kind of cross-validation (e.g. k-fold), and say that it is a ‘prediction’ or ‘forecast’ of crop yield. However, this poses a problem as the approach is not testing the forecasting ability of the model, as it is built on the same season that it is then validating with, thus giving a substantial overestimation of the value for decision-making, such as an application of fertiliser in-season. This is an all-too-common flaw in the logic construct of many published studies. Moving forward, it is essential that clear definitions and guidelines for data-driven yield modelling and validation are outlined so that there is a greater connection between the goal of the study, and the actual study outputs/outcomes. To demonstrate this, the current study uses a case study dataset from a collection of large neighbouring farms in New South Wales, Australia. The dataset includes 160 yield maps of winter wheat (Triticum aestivum) covering 26,400 hectares over a 10-year period (2014–2023). Machine learning crop yield models are built at 30 m spatial resolution with a suite of predictor data layers that relate to crop yield. This includes datasets that represent soil variation, terrain, weather, and satellite imagery of the crop. Predictions are made at both the within-field (30 m), and field resolution. Crop yield predictions are useful for an array of applications, so four different experiments were set up to reflect different scenarios. This included Experiment 1: forecasting yield mid-season (e.g. for mid-season fertilisation), Experiment 2: forecasting yield late-season (e.g. for late-season logistics/forward selling), Experiment 3: predicting yield in a previous season for a field with no yield data in a season, and Experiment 4: predicting yield in a previous season for a field with some yield data (e.g. two combine harvesters, but only one was fitted with a yield monitor). This study showcases how different model calibration and validation approaches clearly impact prediction quality, and therefore how they should be interpreted in data-driven crop yield modelling studies. This is key for ensuring that the wealth of data-driven crop yield modelling studies not only contribute to the science, but also deliver actual value to growers, industry, and governments.

Mathematical Models for Yield Prediction of Bioenergy Crop

Purpose. To determine mathematical models suitable for yield prediction of bioenergy crops and to establish the patterns of the crop health monitoring. Methods. Field studies were conducted in the zone of unstable soil moisture (Experimental Field of the Institute of Bioenergy Crops and Sugar Beet NAAS of Ukraine, Ksaverivka Druha, Kyiv region) and sufficient soil moisture (Yaltushkiv Experimental Breeding Station of the IBCSB NAAS of Ukraine, Chereshneve, Vinnytsia region) in the Right-Bank Forest Steppe of Ukraine in 2022−2024. Results. Frosts can destroy new giant miscanthus sprouts and reduce crop yield by 15.5% in the sufficient soil moisture zone and by 22.4% in the unstable soil moisture zone. Additionally, under the conditions of unstable soil moisture, yield formation of giant miscanthus is limited by insufficient soil moisture, as miscanthus plants can efficiently use only 20 kg/ha of nitrogen fertiliser. In this case, the yield of giant miscanthus can be predicted using the equation: y = 15.19 + 0.29X – 0.005X². Meanwhile, under the conditions of sufficient soil moisture, small doses of fertilisers do not limit the linear biomass growth function, and the equation for yield prediction is: y = 18.44 + 0.25X. Conclusions. For yield prediction in the specific region, it is advisable to use the regression model proposed by Vossen, which takes into account the average yield, linear time trend, and linear regression function. For a specific agroclimatic region, the potential yield of bioenergy crops could be determined by a formula that considers the total land area of the region, the share of bioenergy plantations, and the predicted yield for the specific conditions of the region. Yield prediction model for bioenergy crop consists of the following levels: (1) determining the areas of bioenergy plantations in the GIS map of the region, specifying the plantation age and the varieties used; (2) refining environmental conditions (soil type, moisture regime, and mineral nutrition); (3) weather observation; (4) vegetation index observation; and (5) yield modelling.

Calibration and validation of InfoCrop model for yield and yield attributing characters of different cultivars of mustard at Anand

Calibration and validation of InfoCrop model for yield and yield attributing characters of different cultivars of mustard at Anand

Solar Wind Ion Sputtering from Airless Planetary Bodies: New Insights into the Surface Binding Energies for Elements in Plagioclase Feldspars

Our understanding of the ion-sputtering contribution to the formation of exospheres on airless bodies has been hindered by the lack of accurate surface binding energies (SBEs) of the elements in the various mineral and amorphous compounds expected to be on the surfaces of these bodies. The SBE for a given element controls the predicted sputtering yield and energy distribution of the ejecta. Here, we use molecular dynamics computations to provide SBE data for the range of elements sputtered from plagioclase feldspar crystalline end members, albite and anorthite, which are expected to be important mineral components on the surfaces of the Moon and Mercury. Results show that the SBE is dependent on the crystal orientation and the element’s coordination, meaning multiple SBEs are possible for a given element. Variation in the SBEs among the different surface positions has a significant effect on the predicted yield and energy distribution of the ejecta. We then consider sputtering by H, He, and a solar wind mixture of 96% H and 4% He. For each of these cases, we derive best-fit elemental SBE values to predict the ejecta energy distribution from each of the (001), (010), and (011) cleavage planes. We demonstrate that the He contribution to the sputtering yield cannot be accounted for by multiplying the 100% H results by some factor. Lastly, we average our results over all three possible lattice orientations and provide best-fit elemental SBE values that can be easily incorporated into sputtering yield models.