Agricultural Production Planning Research Articles

Simultaneous Localization and Mapping System for Agricultural Yield Estimation Based on Improved VINS-RGBD: A Case Study of a Strawberry Field

Crop yield estimation plays a crucial role in agricultural production planning and risk management. Utilizing simultaneous localization and mapping (SLAM) technology for the three-dimensional reconstruction of crops allows for an intuitive understanding of their growth status and facilitates yield estimation. Therefore, this paper proposes a VINS-RGBD system incorporating a semantic segmentation module to enrich the information representation of a 3D reconstruction map. Additionally, image matching using L_SuperPoint feature points is employed to achieve higher localization accuracy and obtain better map quality. Moreover, Voxblox is proposed for storing and representing the maps, which facilitates the storage of large-scale maps. Furthermore, yield estimation is conducted using conditional filtering and RANSAC spherical fitting. The results show that the proposed system achieves an average relative error of 10.87% in yield estimation. The semantic segmentation accuracy of the system reaches 73.2% mIoU, and it can save an average of 96.91% memory for point cloud map storage. Localization accuracy tests on public datasets demonstrate that, compared to Shi–Tomasi corner points, using L_SuperPoint feature points reduces the average ATE by 1.933 and the average RPE by 0.042. Through field experiments and evaluations in a strawberry field, the proposed system demonstrates reliability in yield estimation, providing guidance and support for agricultural production planning and risk management.

An Analysis of the Rice-Cultivation Dynamics in the Lower Utcubamba River Basin Using SAR and Optical Imagery in Google Earth Engine (GEE)

One of the world’s major agricultural crops is rice (Oryza sativa), a staple food for more than half of the global population. In this research, synthetic aperture radar (SAR) and optical images are used to analyze the monthly dynamics of this crop in the lower Utcubamba river basin, Peru. In addition, this study addresses the need to obtain accurate and timely information on the areas under cultivation in order to calculate their agricultural production. To achieve this, SAR sensor and Sentinel-2 optical remote sensing images were integrated using computer technology, and the monthly dynamics of the rice crops were analyzed through mapping and geometric calculation of the surveyed areas. An algorithm was developed on the Google Earth Engine (GEE) virtual platform for the classification of the Sentinel-1 and Sentinel-2 images and a combination of both, the result of which was improved in ArcGIS Pro software version 3.0.1 using a spatial filter to reduce the “salt and pepper” effect. A total of 168 SAR images and 96 optical images were obtained, corrected, and classified using machine learning algorithms, achieving a monthly average accuracy of 96.4% and 0.951 with respect to the overall accuracy (OA) and Kappa Index (KI), respectively, in the year 2019. For the year 2020, the monthly averages were 94.4% for the OA and 0.922 for the KI. Thus, optical and SAR data offer excellent integration to address the information gaps between them, are of great importance to obtaining more robust products, and can be applied to improving agricultural production planning and management.

Comparative analysis of habitat suitability for a crop and its primary insect herbivore: providing insights for crop planting and pest management strategies

Habitat suitability analysis using species distribution models (SDMs) presents a valuable approach for determining appropriate land utilization strategies and crop planting decisions, which are vital to achieve optimal agricultural productivity and ensure environmental sustainability in the context of rapid global change. Aside abiotic conditions, pests emerge as the most important biological factor affecting crop growth. It can be an effective avenue to comprehensively assess land use suitability by building and integrating SDMs for both crops and their pests. Using the MaxEnt model and a bivariate map, we predicted and integrated the habitat suitability of tea (Camellia sinensis) and its primary insect herbivore (Empoasca onukii) in China. Our study explicitly revealed spatial heterogeneity in suitability between the two species, despite finding that they shared common environmental predictors (i.e., temperature, elevation, and soil pH). Results support South China and Southwest China as the most favorable areas for tea cultivation, while highlighting the adverse climatic challenges on tea growth in the middle and lower reaches of the Yangtze River and in the northern regions of the Yangtze River. Meanwhile, analyses emphasize the necessity of sustainable leafhopper management strategies in the middle and lower reaches of the Yangtze River and in regions along the southeast coast. These findings demonstrate the significance of comparative analysis on the habitat suitability of both crops and insect herbivores, providing valuable insights for agricultural production planning and sustainable pest management. Furthermore, the methods employed in this study hold potential for the application in diverse context, ranging from the control of invasive species to the conservation of endangered biodiversity.

Dynamics of Spring Snow Cover Variability over Northeast China

Spring snow cover variability over Northeast China (NEC) has a profound influence on the local grain yield and even the food security of the country, but its drivers remain unclear. In the present study, we investigated the spatiotemporal features and the underlying mechanisms of spring snow cover variability over NEC during 1983–2018 based on the satellite-derived snow cover data and atmospheric reanalysis products. The empirical orthogonal function (EOF) analysis showed that the first EOF mode (EOF1) explains about 50% of the total variances and characterizes a coherent snow cover variability pattern over NEC. Further analyses suggested that the formation of the EOF1 mode is jointly affected by the atmospheric internal variability and the sea surface temperature (SST) anomaly at the interannual timescale. Specifically, following a negative phase of the atmospheric teleconnection of the Polar–Eurasian pattern, a prominent cyclonic circulation appears over NEC, which increases the snowfall over the east of NEC by enhancing the water vapor transport and decreases the air temperature through reducing the solar radiation and intensifying the cold advection. As a result, the snow cover has increased over NEC. Additionally, the tripole structure of the North Atlantic spring SST anomaly could excite a wave-train-type anomalous circulation propagating to NEC that further regulates the snow cover variability by altering the atmospheric dynamic and thermodynamic conditions and the resultant air temperature and snowfall. Our results have important implications on the understanding of the spring snow cover anomaly over NEC and the formulation of the local agricultural production plan.

Prediction Model of Greenhouse Tomato Yield Using Data Based on Different Soil Fertility Conditions

Tomato yield prediction plays an important role in agricultural production planning and management, market supply and demand balance, and agricultural risk management. To solve the problems of low accuracy and high uncertainty of tomato yield prediction methods in solar greenhouses, based on experimental data for water and fertilizer consumption by greenhouse tomatoes in different regions over many years, this paper investigated the prediction models of greenhouse tomato yields under three different soil fertility conditions (low, medium, and high). Under these three different soil fertility conditions, greenhouse tomato yields were predicted using the neural network prediction model (NN), the neural network prediction model based on particle swarm optimization (PSO–NN), the neural network prediction model based on an adaptive inertia weight particle swarm optimization algorithm (AIWPSO–NN), and the neural network prediction model based on the improved particle swarm optimization algorithm (IPSO–NN). The experimental results demonstrate that the evaluation indexes (mean square error, mean absolute error, and R2) of the IPSO–NN prediction model proposed in this paper were superior to the other three prediction models (i.e., NN prediction model, AIWPSO–NN prediction model, and IPSO–NN prediction model) under three different soil fertility conditions. Among them, compared with the NN prediction model, the MSE of the other three prediction models under high soil fertility decreased to 0.0082, 0.0041, and 0.0036; MAE decreased to 0.0759, 0.0511, and 0.0489; R2 decreased to 0.8641, 0.9323, and 0.9408. These results indicated that the IPSO–NN prediction model had a higher predictive ability for greenhouse tomato yields under three different soil fertility conditions. In view of the important role of tomato yield prediction in greenhouses, this technology may be beneficial to agricultural management and decision support.

Attention Mechanism-Combined LSTM for Grain Yield Prediction in China Using Multi-Source Satellite Imagery

Grain yield prediction affects policy making in various aspects such as agricultural production planning, food security assurance, and adjustment of foreign trade. Accurately predicting grain yield is of great significance in ensuring global food security. This paper is based on the MODIS remote sensing image data products from 2010 to 2020, and adds band information such as vegetation index and temperature to form composite remote sensing data as a dataset. Aiming at the lack of models for large-scale forecasting and the need for human intervention in traditional models, this paper proposes a grain production estimation model based on deep learning. First, image cropping and yield mapping techniques are used to process the data to generate training samples. Then the channel and spatial attention mechanism (convolutional block attention module, CBAM) is added to extract spatial information in different remote sensing bands to improve the efficiency of the model. Long short-term memory (LSTM) neural networks are added to obtain feature information in the time dimension. Finally, a national-scale grain yield prediction model is constructed. After the study, it was found that the LSTM model using a combination of multi-source satellite images and an attention mechanism can effectively predict grain yield in China. Furthermore, the proposed model was tested on data from 2018 to 2020 showing an average R2 of 0.940 and an average RMSE of 80,020 tons, indicating that it can predict Chinese grain yield better. The model proposed in this paper extracts grain yield information directly from the composite remote sensing data, and solves the problem of small-scale research and imprecise yield prediction in an end-to-end manner.

Spatiotemporal characteristics of high winds in Jilin Province during 1982–2021

AbstractHigh winds are a type of catastrophic weather characterized by wind speeds over 10.8 m·s−1, occurring in all seasons, and have become more frequent, impacting crop growth, agricultural production and quality of life. It is crucial to understand the spatial and temporal distribution patterns of high winds, as well as their speed and other characteristics, for logical planning of agricultural production and the creation of disaster resilience measures. This study applied the climate tendency, accumulative anomalies, theory of run, Mann–Kendall trend and Theil–Sen median to analyse the spatial and temporal distribution characteristics of the average wind speed and high wind days in Jilin Province, the probability of occurrence of high winds of various magnitudes in different eras, and the gale concentration degree (GCD) throughout Jilin Province for a total of 40 years from 1982 to 2021. The average wind speed and number of high wind days in Jilin Province have decreased over the past 40 years, and this trend has slowed over time. The average GCD in Jilin Province has shown an increasing trend over the last 40 years, with the average GCD being highest in Liouhe and lowest in Linjiang. In general, level six high winds were the main wind hazard level in Jilin Province, with the highest frequency occurring in different seasons and decades. In addition, the occurrence of high wind days is affected by global climate change and different underlying surfaces, which leads to maize and yield reduction. Research on the spatiotemporal distribution of high winds can provide important suggestions for the prevention and management of high wind disasters.

Improving the spatial and temporal estimation of ecosystem respiration using multi-source data and machine learning methods in a rainfed winter wheat cropland

The investigation of ecosystem respiration (RE) and its vital influential factors along with the timely and accurate detection of spatiotemporal variations in RE are essential for guiding agricultural production planning. RE observation in the plot region is primarily based on the laborious chamber method. However, upscaling the spatial-temporal estimates of RE at the canopy scale is still challenging. The present study conducted a field experiment to determine RE using the chamber method. A multi-rotor unmanned aerial vehicle (UAV) equipped with a multispectral camera was employed to acquire the canopy spectral data of wheat during each RE test experiment. Moreover, the agronomic indicators of aboveground plant biomass, leaf area index, leaf dry mass as well as agrometeorological and soil data were measured simultaneously. The study analyzed the potential of multi-information for estimating RE at the field scale and proposed two strategies for RE estimation. In addition, a semiempirical, yet Lloyd and Taylor-based, remote sensing model (LT1-NIRV) was developed for estimating RE observed across different growth stages with a small margin of error (coefficient of determination [R2] = 0.60–0.64, root-mean-square error [RMSE] = 285.98–316.19 mg m−2 h−1). Further, five machine learning (ML) algorithms were utilized to independently estimate RE using two different datasets. The rigorous analyses, which included statistical comparison and cross-validation for estimating RE, confirmed that the XGBoost model, with the highest R2 and lowest RMSE (R2 = 0.88 and RMSE = 172.70 mg m−2 h−1), performed the best among the evaluated ML models. The LT1-NIRV model was less effective in estimating RE compared with the other ML models. Based on this comprehensive comparison analysis, the ML model can successfully estimate variations in wheat field RE using high-resolution UAV multispectral images and environmental factors from the wheat cropland system, thereby providing a valuable reference for monitoring and upscaling RE observations.

Artificial intelligence models for prediction of monthly rainfall without climatic data for meteorological stations in Ethiopia

Global climate change is affecting water resources and other aspects of life in many countries. Rainfall is the most significant climate element affecting the livelihood and well-being of the majority of Ethiopians. Rainfall variability has a great impact on agricultural production, water supply, transportation, the environment, and urban planning. Because all agricultural activities and subsequent national crop production hinge on the amount and distribution of rainfall, accurate monthly and seasonal predictions of this rainfall are vital for agricultural planning. Rainfall prediction is also useful for governmental, non-governmental, and private agencies in making long-term decisions and planning in numerous areas such as farming, early warning of potential hazards, drought mitigation, disaster prevention, and insurance policy. Artificial Intelligence (AI) has been widely used in almost every area, and rainfall prediction is one of them. In this study, we attempt to investigate the use of AI-based models to predict monthly rainfall at 92 Ethiopian meteorological stations. The applicability of Artificial Neural Networks (ANNs) and Adaptive Neuro-Fuzzy Inference System (ANFIS) models in predicting long-term monthly precipitation was investigated using geographical and periodicity component (longitude, latitude, and altitude) data collected from 2011 to 2021. The experimental results reveal that the ANFIS model outperforms the ANN model in all assessment criteria across all testing stations. The Nash–Sutcliffe efficiency coefficients were 0.995 for ANFIS and 0.935 for ANN over testing stations.

Characteristics of land and climate indicators as the basis for agricultural production planning in the municipality of Vlasotince

This study explores the characteristics of land and climate indicators as a basis for planning agricultural production in the municipality of Vlasotince. Aim of the research was to provide concise information about agricultural land, relief, pedological characteristics, and basic climate indicators in this area. Data were collected from relevant strategic documents and scientific papers that address similar topics. In the field of agricultural land, the analysis encompassed the scope and structure of agricultural land use, agricultural land used by agricultural households, land consolidation, and agricultural land in state ownership within the territory of the municipality of Vlasotince. The relief analysis included the identification of key relief characteristics in municipality, which have a significant impact on microclimate conditions and agricultural production possibilities in mentioned area. The results of the pedological characteristics of the land indicate the occurance of various types of soil, such as humus-silicate soils (rankers), eutric brown soils (cambisol), alluvial or fluviatile soils, and pseudogleys. The obtained results offer insight into soil fertility and its capability to support growing of various crops. Analysis of basic climate indicators in the municipality of Vlasotince includes temperature, precipitation, and the length of the vegetation period. According to research results and discussion, this study provides a comprehensive overview of the characteristics of land and climate indicators in observed municipality that are of essential importance for agricultural production planning.

Developing a New Methodology for the Use of GIS and AHP in Determining Suitable Areas for Wheat Plants in the Lower Kelkit Basin

In agricultural production planning; compiling the data correctly, and using and interpreting the data precisely have strategic importance. This study aims, it is aimed to develop a model that can evaluate the suitability of the Lower Kelkit Basin for wheat farming by using the Analytical Hierarchy Process (AHP) and current GIS technologies. In the study, weight values of seven different criteria of topography (slope, aspect) and soil (texture, organic matter (OM), CaCO3, EC, pH) were calculated with AHP. These weight values and standardized criteria maps were combined within the ArcGIS Weighted Overlay tool and the result maps were created according to the FAO suitability index. According to these maps, 54% of the Lower Kelkit Basin was modeled as unsuitable (N) for wheat, 22% as moderately suitable (S2), and 24% as highly suitable (S1). In addition to all these, an editable and updatable ArcGIS model tool was also produced as a result of the study. Our results indicated that AHP and GIS are powerful and effective tools that can be used in land suitability modeling.

Effects of global climate change on the hydrological cycle and crop growth under heavily irrigated management – A comparison between CMIP5 and CMIP6

Quantifying the impact of global climate change on the water cycle and crop production is essential for water resource management and agricultural production planning. This study compared multiple GCM (General Circulation Model) projections of CMIP5 (Coupled Model Intercomparison Project 5) and CMIP6 and further used the selected GCMs to quantify the impact of future climate change on hydrology and crop production under intensive irrigation management in the North China Plain using an improved SWAT model (SWAT-MAD). Taylor’s skill score was used firstly to screen out six groups of GCMs with better simulation performance from 10 pairs of homologous GCMs of CMIP5 and CMIP6. The selected GCMs of CMIP5 and CMIP6 were further used to drive SWAT-MAD for a robust evaluation of climate change impacts. Results showed that during winter wheat growing season, average actual evapotranspiration (ETa) increased by 3%, 3%, 4%, and 5%, respectively, under 2041–2070 RCP4.5, 2041–2070 SSP2-4.5, 2041–2100 RCP4.5, and 2071–2100 SSP2-4.5 scenarios, compared to the historical period (1971–2000). During summer maize growing season, those changes in ETa were 4%, 2%, −0.2%, and −3%. Predicted future precipitation, air temperatures, and surface runoff could increase, while irrigation could decrease as precipitation increased. The dynamic patterns of leaf area index of winter wheat and summer maize indicated that there is a tendency for early emergence and maturity of both crops in the future, and the daily total biomass elevated with a corresponding increase in final yields. The highest increases in yields of winter wheat and summer maize were 18.9% and 16.7%. The findings not only contribute to enhancing the confidence of future projections using CMIP6 but also facilitate our understanding of the relative uncertainty of GCMs. This study provides technical and data support for pre-selections of GCMs and decision making in best management practices for groundwater conservation and agricultural production.

Rainfall Probability Analysis for Crop Planning of Unakoti and West Tripura District of Tripura, India

Rainfall is one of the most important climatic variables that determine the spatial and temporal patterns of climate variability of a region, which provides useful information for the planning of agricultural production. For the purpose of crop planning, rainfall data of two districts of Tripura viz., Unakoti and West Tripura for aperiod of 20 years (2001-2020) were collected and used to analyze probability of weekly rainfall. The incomplete Gamma distribution probability analysis using the method suggested by Victor (2000) was used to find and analyze weekly rainfall probability for the studied districts. The investigation revealed that total annual rainfall of Unakoti at 90%, 75% and 50% probability is predicted to be 1640 mm, 1686 mm and 1722 mm respectively. The total annual rainfall of the West Tripura at 90%, 75% and 50% probability is likely to be 1631 mm, 1847 mm and 2108.4 mm respectively. Crops found suitable for kharif season are blackgram, greengram, aman rice. The crops which can be grown in rabi season include cole crops, tomato, chilli, potato, pea, toria etc. Based on the study, crops suited to summer season include short duration greengram, aush rice, sesame, jute in all land situations.

Prediction of arabica coffee production using artificial neural network and multiple linear regression techniques

Crop yield and its prediction are crucial in agricultural production planning. This study investigates and predicts arabica coffee yield in order to match the market demand, using artificial neural networks (ANN) and multiple linear regression (MLR). Data of six variables, including areas, productivity zones, rainfalls, relative humidity, and minimum and maximum temperature, were collected for the recent 180 months between 2004 and 2018. The predicted yield of the cherry coffee crop continuously increases each year. From the dataset, it was found that the prediction accuracy of the R2 and RMSE from ANN was 0.9524 and 0.0784 tons, respectively. The ANN model showed potential in determining the cherry coffee yields.

Systematic analysis of retrospective crop yields time series based on their structure identification

Agricultural production and crop production, as its basic branch, is characterized by a significant dependence on solar activity (the Shvabe cycle) and hydrometeorological conditions of a particular year, especially in areas of insufficient moisture, which are mainly characteristic of the regions of the Lower Volga region of Russia. For the planning and management of agricultural production, a reliable forecast of the yield of cultivated crops is important. Previously published results show that the empirical distribution of retrospective TS yields, including the example of grain crops, is statistically significantly different from the normal one. This justifies the use of more subtle research methods, such as singular value analysis (SSA). The algorithm of the SSA method includes several stages, the main of which is the singular decomposition of the trajectory matrix into the sum of elementary matrices, each of which is given by a set of eigenvalues. At the final stage, diagonal averaging is performed, according to which the elementary matrices within each set are summed. The singular decomposition of the data on the dynamics of the BP yield was carried out according to the data for 1950-2018 on the example of grain crops in arid conditions of the Volgograd region. For the practical implementation of the SSA method, a computer program in Python was developed, using specialized mathematical libraries. The program provided a singular decomposition of the trajectory matrix, diagonal averaging of the resulting matrices, as well as the restoration of the set of series, taking into account their contribution to the analyzed TS. The use of the SSA method allowed us to identify the main natural and economic cycles, including a cycle with a period of about 14 years, which plays a major role in the dynamics of the formation of grain yields. Pronounced cycles of a different duration, among those identified by the SSA method, have not been established. There is a strong negative autocorrelation of the levels of interannual variability of grain yield values, which is present on the graphs of many restored series.

MONITORING OF SECURITY OF AGRICULTURAL CROPS WITH MINERAL MATERIALS BY REMOTE DETECTION

The need to produce organic food and increase yields plays a significant role in the planning of agricultural production and the economy of the state in general. Monitoring and modeling of all stages of production implies the establishment of smart agriculture concepts based on the use of remote sensing results. This work procedure implies abandoning the classic homogenization in the approach to the cultivation of agricultural land and provides the possibility of anticipating problems and timely action, which should provide an increase in yield with environmental production conditions.

Hyperspectral Data Classification Algorithm considering Spatial Texture Features

As a cutting-edge technology, hyperspectral remote sensing has been widely applied in many fields, including agricultural production, mineral identification, target detection, disaster warning, military reconnaissance, and urban planning. The collected hyperspectral data have high spectral resolution and spatial resolution and are characterized by a large amount of information, redundancy, and high dimension. At the same time, there is a strong correlation between the bands. Therefore, hyperspectral data not only provides rich information but also brings great challenges for subsequent processing. Hyperspectral image classification is a hot issue in remote sensing information processing. Traditional hyperspectral remote sensing image classification methods only use the spectral features of the image without considering the spatial features of each pixel in the hyperspectral remote sensing image. In this paper, a hyperspectral image classification method is proposed not only considering spectral features but also considering texture features. This method jointly considers both these features. Firstly, six texture features contributing a lot to each pixel of hyperspectral remote sensing image are extracted by using a gray level cooccurrence matrix, and then, the spectral features of each pixel in neighbor are combined to form the texture-spectral features. Finally, the classification experiment of the Indian Pines and Pavia University scene is carried out based on a support vector machine and extreme random tree algorithm, and the obtained results show that the proposed method achieves higher classification performance than the traditional method.

Mapping the Northern Limit of Double Cropping Using a Phenology-Based Algorithm and Google Earth Engine

Double cropping is an important cropping system in China, with more than half of China’s cropland adopting the practice. Under the background of global climate change, agricultural policies, and changing farming practices, double-cropping area has changed substantially. However, the spatial-temporal dynamics of double cropping is poorly understood. A better understanding of these dynamics is necessary for the northern limit of double cropping (NLDC) to ensure food security in China and the world and to achieve zero hunger, the second Sustainable Development Goal (SDG). Here, we developed a phenology-based algorithm to identify double-cropping fields by analyzing time-series Moderate Resolution Imaging Spectroradiometer (MODIS) images during the period 2000–2020 using the Google Earth Engine (GEE) platform. We then extracted the NLDC using the kernel density of pixels with double cropping and analyzed the spatial-temporal dynamics of NLDC using the Fishnet method. We found that our algorithm accurately extracted double-cropping fields, with overall, user, and producer accuracies and Kappa coefficients of 95.97%, 96.58%, 92.21%, and 0.91, respectively. Over the past 20 years, the NLDC generally trended southward (the largest movement was 66.60 km) and eastward (the largest movement was 109.52 km). Our findings provide the scientific basis for further development and planning of agricultural production in China.

Knowledge base creation for nitrogen fertilization towards crops

The use of intelligent technologies for the systematization of agrotechnological knowledge, by creating a specialized software system for storing and presenting knowledge in the field of crop production, represents one of the most promising areas for improving the efficiency of agricultural production planning. The paper deals with the relevant issues of creating a knowledge base for information support system of the operator’s activities on the mineral fertilizer application. The increased requirements for modern agriculture to be resource-saving and environmentally friendly stem from the development of this direction. The software LogicGem, an easy-to-use tool for creating, editing, checking and compiling decision table logic, was used to build a knowledge base.

Stochastic Model of Rural Agribusiness Supply Chain: A Case Study of Gatsibo District

The current economy of most developing countries is based on agriculture and its related operations that serve as the main entrance into the emerging industrial transformation. The lookout focuses on the integration of rural areas as drivers of a meaningful part of the land output. The concern has been to curiously follow the changes in the agricultural production environment from a global perspective of the market economy and its integration in the current economic revolution/transformation. The branches of deepening the understanding of this issue enroot in levelling the optimization of the benefit at all extremes of the processes involved in decentralized agribusiness functions. This study is essentially targeting to explain the current agriculture supply chain formula in rural parts of Rwanda for a more competitive equilibrium of the agricultural production planning and inventory and the distribution as the rural market nodes. The methodological intention was to drive a stochastic view of the operational transactions through the flow of the financial means (income from job for consumers and revenues from sales for rice farmers) and the likelihood to switch (respond to one’s reactions or actions) to one of the nodes, if any. The results show that the financial security on both parties is not a driver to quicken the transactions or sustain any change in the supply chain and it calls for exogenous factors, in short term, to reverse the trends.