Crop Conditions Research Articles

Participatory agroecological diagnosis in small and medium-sized producers in Michoacán and Morelos, Mexico

production units by identifying: (i) crop management practices, (ii) types of inputs and technologies used, and (iii) health status of soils and crops. Design/Methodology/Approach: This implemented research was of the type known as "In-depth case study", where the minimum sample size is 6 to 10 cases. Descriptive, correlational, and explanatory aspects corresponding to the selected production units were considered; based on qualitative and quantitative information. Results: For indicators of crop management and soil health, the predominant type of agriculture was transition to agroecology. Crop health indicators were the most agroecological. Study limitations/Implications: The results obtained allowed to establish the current state of the production units; with which further comparisons of the condition of those production units in the future can be made. Findings/Conclusions: The methodology used allowed to evaluate participatively, from an agroecological approach, the sustainability of soils and crops in production units of the municipalities Ario de Rosales, Michoacán and Tetela del Volcán, Morelos. Within the six productive units studied, it was found a productive unit with the appropriate characteristics to be considered as an "agroecological beacon".

Understanding the perceived indicators of food sovereignty and food security for rice growers and rural organizations in Mazandaran Province, Iran

BackgroundFood sovereignty and food security are inseparable from agricultural development policies, particularly regarding how to increase food production and productivity to meet future demand. This study investigates the status and perceptions of food sovereignty and food security of small-scale rice growers’ households in the Mazandaran Province of northern Iran. The study region is one of the most important places for domestic rice production, with nearly 230,000 hectares of rice lands in the country and 45% of total domestic rice production. The role of the Rice Research Institute of Iran (RRII) as an innovative rural institution was highlighted by the rice producers for contributing to food sovereignty and food security. A survey was distributed among 127 rice farmers’ households to obtain indicators of food sovereignty (localization of food systems, values for food providers, concentration of local control of the food system, building knowledge and skills, right to food, working with nature) and food security (availability, accessibility, utility, quality). Principal Component Analysis and Partial Correlation tests were used for finding the relationship between variables and focused indicators.ResultsResults show that food sovereignty in Northern Iran focused on localizing the food system by gaining access to financial assets and local markets, investing in human capital and local training and improving access to the water resources; food security focused on environmental and climate extension, increasing household’s revenues by improving food policies and food quality from rice farmers’ perspectives. In addition, the results of this study demonstrate the desire in Northern Iran for the role of innovative rural organizations as vital linkages between rice farmers and the public sector (i.e., Ministry of Agriculture Jihad).ConclusionsThis research shows that from the perspective of the surveyed rice growers in northern Iran, four main indicators that relate to investment and resources were the most significant: capital, markets, credit, and knowledge. Further studies are required for remote sensing monitoring of rice crop condition and yields, condition of irrigation systems, and geographic relationship of the agricultural infrastructure to food sovereignty and food security in northern Iran.

Justifying the Parameters for an Unmanned Aircraft Flight Missions of Multispectral Aerial Photography

The paper shows the possibility of collecting multispectral and RGB data on the crop condition using an unmanned aircraft. The use of several programs for creating a flight mission is likely to lead to the inefficient use of the unmanned aircraft resources. (Research purpose) To calculate the parameters of aerial photography with multispectral and RGB cameras to enable the improved efficiency of unmanned aircraft resources. (Materials and methods) The paper studies the specifi cations of RGB and multispectral cameras installed on the drone. The research uses the results of scientifi c research on the calculation of aerial photography parameters such as longitudinal and transverse overlap, as well as the flight speed of an unmanned aircraft. (Results and discussion) It is established that the calculated values of the longitudinal and transverse overlap for the Zenmuse X4S RGB camera, as well as the unmanned aircraft fl ight speed during the joint aerial photography with a multispectral camera, make it possible to use standard mobile applications to create a flight mission. (Conclusions) It is determined that the obtained values guarantee the creation of high-quality digital maps. The parameters for the flight mission are calculated ensuring the effi cient exploitation of the unmanned aircraft resources, using simultaneously the Zenmuse X4S RGB camera and six multispectral cameras on board the unmanned aircraft.

Algorithms for Calculating the Trajectory of Unmanned Aerial Vehicles for Solving Agricultural Problems

The relevance of using unmanned aerial vehicles (UAV) is substantiated in comparison with other methods of aerospace survey. The paper provides examples of tasks requiring the use of UAVs for aerial photography from different heights. It is shown that the introduction of agricultural robots, including UAVs, increases the speed of fi eld work, allows obtaining unique data necessary for the assessment of agricultural territories, crops processing and plant growth stimulation. It is noted that the problem of constructing the movement trajectories of a multirotor UAV for performing agricultural tasks within a minimum time limit remains unresolved. (Research purpose) To reduce the length of the trajectory covering a given area and reduce the flight time of a multirotor UAV, taking into account the analysis of possible obstacles and land plots that are beyond the task scope. (Materials and methods) Geometric methods have been used to calculate the UAV flight trajectory covering a given section, the trajectory of movement in an environment with obstacles to the designated point. Photogrammetry methods have been used for processing aerial photography images when forming an orthophotoplane and a terrain map. (Results and discussion) The trajectory calculated by the developed algorithm proves to meet all the requirements: it is continuous, has a minimum number of turns, it is smoothed, and feasible for a multirotor UAV. (Conclusions) It was determined that according to the proposed algorithm it takes less than 0.05 seconds to calculate the trajectory covering a rectangular section with the sides of 200 by 30 meters. It was found that the trajectory in the fi rst 10,200-square-meter section decreased by 9 percent, and in the second 950,000-square-meter section it reduced by 6 percent, compared with the length of the trajectory built using standard algorithms. The flight time reduced by 32 and 10 percent, respectively. The paper presents the key advantages of using UAV for video shooting such as: guaranteed high resolution of photographic materials and the ability to shoot at a given time, allowing for the crop condition assessment.

Establishing a critical nitrogen dilution curve for estimating nitrogen nutrition index of potato crop in tropical environments

The critical nitrogen (N) dilution curve (CNDC) ( Nc = A 1 W -A2 ) and the N nutrition index (NNI) have shown to be a robust diagnostic tool to assess crop N status. Several CNDCs were established for potatoes ( Solanum tuberosum L.) in various conditions. However, it is difficult to know if these different CNDCs reflect a true variability across genotype × environment × management (G×E × M) scenarios, or are rather linked to estimation errors. Therefore, the aims of this study were to (i) estimate and compare CNDCs via a Bayesian statistical approach for different G×E × M scenarios in tropical environments and (ii) assess the uncertainty in the relationship between tuber yield and NNI. Field experiments were conducted in three site-years (SYs) in the southeastern region of Brazil. Treatments consisted of two potato cultivars (Agata and Electra) and five N fertilization managements [differing in the N rates (0 −320 kg N ha -1 ) and/or application timings]. Based on 95% credibility intervals, we found no significant differences between CNDCs across G×E × M scenarios assessed and thus derived a single generic CNDC for both cultivars and all SYs defined by Nc = 3.86 W - 0.34 . This global CNDC was used to compute NNI and discriminate tuber yields obtained under N-limiting and non-N-limiting conditions. An uncertainty analysis revealed that critical NNI threshold values for the tuber yield response to N management were not statistically different among G×E × M scenarios. Therefore, these results support the use of a unique CNDC for estimating the N status (NNI) of potato crop in tropical environments. We found no evidence of the need for SY- or cultivar-specific CNDCs. Future studies should evaluate N dilution curves and NNI for potato across different production environments worldwide. • A new critical N curve was derived for potato in tropical environments. • The critical N curve ( Nc = 3.86 W -0.34 ) was obtained using a Bayesian analysis. • No evidence of variability in the critical N nutrition index threshold for yield. • No evidence of the need for cultivar- or site-specific parameters.

GHG emissions of straw treatments in rural China and scenario simulation based on life cycle perspective

The comprehensive utilization of straw plays an important role in GHG emissions reduction. The main breakthrough point of promoting straw resource utilization in China is on staple crops. In this study, the current straw utilization and treatment status of staple crops in rural China were studied based on data from the 2019 China Rural Development Survey, which included five provinces, 100 villages, and 1015 households. A life cycle assessment was conducted to identify the GHG emissions of the various crop straw utilization and treatment methods. The GHG emissions per unit area from the crop and household perspectives were also observed. Furthermore, the trend in GHG emissions from crop straw utilization and treatment was predicted through scenario simulation. The results showed that the comprehensive utilization rates of rice straw and wheat straw in rural China were 85.30% and 98.41%, respectively, in 2018, whereas the comprehensive utilization rate of maize straw was only 40.25%. Rice straw has the highest GHG emission level of 11771.90 kgCO2-eq/ha from a long-term perspective. On a national scale, the average GHG emissions per unit area of household is 1229.56 kgCO2-eq/ha, with significant differences between provinces, from a maximum of 12033.77 kgCO2-eq/ha in Jiangsu to a minimum of −3599.77 kgCO2-eq/ha in Jilin. According to the results of the scenario simulation, if the ambitious measures in reducing the straw incorporated into paddy soil are not taken, future GHG emissions of staple crop straw may persist and even rise rapidly in rural China.

Perspectivas futuras de la biofortificación de alimentos: la asociación con microorganismos del suelo

The problem of food insufficiency and insecurity affects close to 1 billion people worldwide, frequently due to the deficiency of essential mineral elements present in food, such as iron, copper, iodine, selenium and zinc. In addition, the low fertility of soils affects the mobility of nutrients in the soil and the dynamics of plant absorption, directly influencing their production, crop quality and the inadequate nutritional balance of the population. A viable strategy to face this problem is crop biofortification, which aims to improve the nutritional status and value of crops by producing foods with high nutritional value in mineral elements that are deficient in the diet of the world's population. This review article is an analysis of what biofortification implies, its current context and the initiatives of international programs, institutions and policies for the improvement of nutrition, food security and public health. The future perspectives of biofortification explored in this review revolve around the enrichment of natural compounds in vegetables and fruits, as well as the feasibility of biofortification of alternative crops due to their profitability, nutritional value and their good acceptance in the market for human consumption. The analysis of future perspectives and recent advances in this lines of research, focused on obtaining strategies that increase mineral absorption and bioavailability in the plant to achieve greater benefit, the practice of applying soil microorganisms (arbuscular mycorrhizae and plant growth promoting bacteria) is emerging as the strategy with greater accessibility to the farmer at all levels of production, cost reduction and increased yields, while improving various nutritional characteristics of food.

Reducing and Delaying Nitrogen Recommended by Leaf Critical SPAD Value Was More Suitable for Nitrogen Utilization of Spring Wheat under a New Type of Drip-Irrigated System

Timely and accurate judgment of the nitrogen nutritional status of crops is the key to develop an optimal nitrogen application strategy. However, the evaluation criteria of nitrogen nutrition and nitrogen application strategies at each growth stage of wheat are not clear for the new type of drip-irrigated spring wheat system, TR6S (where one drip tube serves six rows of wheat, with a row spacing (RS) of 10 cm, inter-block space (IBS) of 25 cm and the lateral spacing (LS) of 80 cm, which achieved a lower drip-tube input and higher profit compared with the traditional planting system in Xinjiang). Therefore, we studied the recommendation mechanism of nitrogen fertilizer in different growth stages of wheat based on the critical SPAD values of leaves under TR6S. We set four nitrogen treatments (N1 (300 kg ha−1), N2 (270 kg ha−1), N3 (240 kg ha−1) and N4 (0 kg ha−1)) during two spring wheat growth seasons. The results revealed that the correlation coefficient (r2) between SPAD (soil plant analysis development) value and plant nitrogen content in the middle of first top leaf (L1-M) of wheat was higher than that in other leaf types and leaf positions under TR6S. A quadratic function relationship existed between a SPAD value of L1-M and grain yield. The critical SPAD values at the jointing, booting, anthesis, early milk, and late milk stages were 37.34, 39.40, 42.25, 45.57, and 35.91, respectively. In addition, through the establishment of the nitrogen application recommendation model for various wheat growth stages based on the critical SPAD value, the recommended optimal nitrogen application rates at jointing, booting, anthesis, early milk, and late milk stages were observed to be 69.4, 80.0, 90.8, 44.0, and 6.0 kg ha−1, respectively. This recommended nitrogen application strategy exhibited a better parallel relationship with the nitrogen nutrition index (NNI) of each growth period than the conventional nitrogen application strategy. Therefore, it was more in line with the actual absorption and utilization of nitrogen in wheat of TR6S. In conclusion, the SPAD values of L1-M could be relatively more accurate to evaluate the nitrogen nutrition status of wheat. Compared to traditional nitrogen application strategy, reducing and delaying nitrogen application, recommended based on the leaf SPAD model, was more suitable for nitrogen utilization under TR6S. The results can be applied in other arid and semiarid regions.

Optimal Storage Conditions of Commercial Cannabis Crops

Optimal Storage Conditions of Commercial Cannabis Crops

Sentinel-2 estimation of CNC and LAI in rice cropping system through hybrid approach modelling

ABSTRACT Earth observation techniques represent a reliable and faster alternative to in-situ measurements by providing spatio-temporal information on crop status. In this framework, a study was conducted to assess the performance of hybrid approaches, either standard (HYB) or exploiting an active learning optimisation strategy (HYB-AL), to estimate leaf area index (LAI) and canopy nitrogen content (CNC) from Sentinel–2 (S2) data, in rice crops. To achieve this, the PROSAIL-PRO Radiative Transfer Model (RTM) was tested. Results demonstrate that a wide range of rice spectra, simulated according to realistic crop parameters, are reliable when appropriate field background conditions are considered. Simulations were used to train a Gaussian Process Regression (GPR) algorithm. Both cross-validation and validation results showed that HYB-AL approach resulted the best performing retrieval schema. LAI estimation achieved good performance (R2=0.86; RMSE=0.54) and resulted very promising for model application in operational monitoring systems. CNC estimations showed moderate performance (R2=0.63; RMSE=0.89) due to a saturation behaviour limiting the retrieval accuracy for moderate/high CNC values, approximately above 4 [g m−2]. S2 maps of LAI and CNC provided spatio-temporal information in agreement with crop growth, nutritional status and agro-practices applied to the study area, resulting in an important contribution to precision farming applications.

Plant-based extracts for cotton pest management in Sub-Saharan Africa: a review

ABSTRACT Cotton crops are hampered by a complex of arthropod species, insects and mites that attack all plant organs. In sub-Saharan Africa, crop pest control mainly involves synthetic pesticide applications based on a treatment calendar consisting of 5–6 applications at 14-day intervals. Organic cotton production chains have however been set up to supply a niche market, with botanical extracts used for crop protection. This review presents published results of experiments conducted in sub-Saharan African countries. A total of 37 plant species were laboratory tested in research stations (24 trials) and village fields in Central and West African countries. Neem (Azadirachta indica) was the plant most commonly used in the form of seed-pressed oil. In Mali, this compound was combined with Carapa procera oil in organic cotton cropping conditions. Cassia nigricans is another species reported to be used in Mali, Burkina Faso and Benin. Aromatic species such as Ocimum gratissimum, Cymbopogon citratus, Citrus sp. were also tested. Further research could focus on species such as Anacardium occidentale and Hyptis suaveolens, which seem to be locally available. The use of essential oils does not appear to take priority over aqueous extracts, which could be tested in crop protection programmes involving partial substitution of synthetic insecticides.

Forming scientific-methodical principles of satellite agroecological monitoring in Ukraine

The article presents the historical aspects of developing remote agroecological monitoring. It is shown that in the process of improving satellite sensors, opening access to foreign satellite data, the possibilities and effectiveness of using the results of remote sensing in scientific and environmental protection activities, in the production of safe agricultural products have expanded. The stages of forming scientific and technical program of satellite agro-ecological monitoring «Agrokosmos», the main fundamentals, tasks and results of this research direction are analyzed. The effectiveness of satellite information for determining the structure of agrolandscapes and land use systems, climate change, its impact on the condition of crops and their moisture supply, productivity of grain crops is shown. Methodical approaches to identifying and mapping manifestations of desertification processes, erosive degradation of agricultural land and associated negative trends are considered. It is noted that modern satellite information is an important tool not only for improving the agroecological monitoring system, but also for developing and implementing measures to achieve a neutral level of soil degradation, prevent the development of desertification processes, and adapt to climate change. It has been proven that the satellite agroecological monitoring system needs improvement in terms of more detailed classification of individual elements of agrolandscapes, as well as improvement of algorithms and image processing methods to expand the possibilities of timely using these data to provide management recommendations and prompt adjustment of agricultural technologies. The main directions of further development of the «Agrokosmos» program are presented. An important issue remains the strengthening of the coordination of research works with the scientific institutions of the National Academy of Sciences, the State Space Agency, the development of cooperation with foreign partners within the framework of international projects. The urgent task of improving the information potential of the use of Earth remote sensing data is the creation of a national network of sub-satellite terrestrial agricultural test sites. The effective use of satellite data in management, production and scientific activities in agriculture requires creating an interdepartmental informative and analytical center «Agrokosmos» within the framework of the National Academy of Sciences and the Ministry of Agricultural Policy and Food.

Hyperspectral Vegetation Indices to Assess Water and Nitrogen Status of Sweet Maize Crop

The deployment of novel technologies in the field of precision farming has risen to the top of global agendas in response to the impact of climate change and the possible shortage of resources such as water and fertilizers. The present research addresses the performance of water and nitrogen-sensitive narrow-band vegetation indices to evaluate the response of sweet maize (Zea mays var. saccharata L.) to different irrigation and nitrogen regimes. The experiment was carried out in Valenzano, Bari (Southern Italy), during the 2020 growing season. Three irrigation regimes (full irrigation, deficit irrigation, and rainfed) and two nitrogen levels (300 and 50 kg ha−1) were tested. During the growing season, a Field Spec Handheld 2 spectroradiometer operating in the range of 325–1075 nm was utilized to capture spectral data regularly. In addition, soil water content, biometric parameters, and physiological parameters were measured. The DATT index, based on near-infrared and red-edge wavelengths, performed better than other indices in explaining the variation in chlorophyll content, whereas the double difference index (DD) showed the greatest correlation with the leaf–gas exchange. The modified normalized difference vegetation index (NNDVI) and the ratio of water band index to normalized difference vegetation index (WBI/NDVI) showed the highest capacity to distinguish the interaction of irrigation x nitrogen, while the best discriminating capability of these indices was under a low nitrogen level. Moreover, red-edge-based indices had higher sensitivity to nitrogen levels compared to the structural and water band indices. Our study highlighted that it is critical to choose proper narrow-band vegetation indices to monitor the plant eco-physiological response to water and nitrogen stresses.

Main levers of environmental risk management in agrocenoses

The article considers the existing approaches to the management of environmental risks in agricultural production. It is noted that in order to minimize environmental risks in agrocenoses and improve environmental safety in agroecosystems, it is necessary to assess the consequences of the impact of agricultural activities on agrocenoses. Therefore, the purpose of the study is to summarize the main environmental risks in agrocenoses and determine levers of environmental risks managent to minimize their negative impact in agricultural production and improve the safety of agroecosystems. It is shown that in order to control the consequences of environmental risks, it is important to develop an algorithm for the process of managing environmental risks in agrocenoses throughout the crop cycle. The fundamental principles of this process include studying the main factors of environmental risks for growing crops and developing a list of environmental risks in agrocenoses; identification of the main levers of environmental risk management in agrocenoses, which include recommendations for the use of optimal measures in agricultural production to prevent or minimize environmental risks. The list of main environmental risks in agrocenoses is proposed, which summarizes the sources of their occurrence and the consequences for agrocenosis. The main environmental risks in agrocenoses include climate change, soil degradation, anthropogenic soil pollution, changing of the optimal ratio of land used in agriculture, unsatisfactory phytosanitary condition of crops. The main levers of environmental risk management in agrocenoses are identified and priority measures are given to prevent the negative consequences of the effects of biotic and abiotic factors on agrocenoses. It is noted that the development of new methodological approaches to the management of environmental risks will minimize their impact on agrocenoses and improve the safety of agroecosystems.

Monitoring the Growth Status of Corn Crop from UAV Images Based on Dense Convolutional Neural Network

Monitoring corn crop growth status is of great significance to crop production, breeding, and seed production. The Unmanned Aerial Vehicles’ (UAVs) technology makes it possible to use computer vision technology to identify corn growth stage intelligently. A model customized for corn growth status monitoring based on a dense convolutional neural network (CM-CNN) was proposed, including a two-way dense module and a new activation function ELU. The two-way dense module enlarges the receptive field, while the ELU alleviates gradient disappearance and speeds up learning in deep neural networks. Dense architecture concatenates all the previous layer features to enhance feature reuse. The proposed CM-CNN performs well in classifying corn growth stages. Experimental results show that CM-CNN is a state-of-the-art method, with an accuracy of its relevant data up to 99.3%. Compared with other CNN models, viz. AlexNet, ZFNet, VGG, InceptionV3, Xception and ResNet, fewer parameters are in CM-CNN.

Segmentation and Stratification Methods of Field Maize Terrestrial LiDAR Point Cloud

Three-dimensional (3D) laser point cloud technology is an important research method in the field of agricultural remote sensing research. The collection and processing technology of terrestrial light detection and ranging (LiDAR) point cloud of crops has greatly promoted the integration of agricultural informatization and intelligence. In a smart farmland based on 3D modern agriculture, the manager can efficiently and conveniently achieve the growth status of crops through the point cloud collection system and processing model integrated in the smart agricultural system. To this end, we took field maize as the research object in this study and processed four sets of field maize point clouds, named Maize-01, Maize-02, Maize-03, and Maize-04, respectively. In this research, we established a field individual maize segmentation model with the density-based clustering algorithm (DBSCAN) as the core, and four groups of field maize were used as research objects. Among them, the value of the overall accuracy (OA) index, which was used to evaluate the comprehensive performance of the model, were 0.98, 0.97, 0.95, and 0.94. Secondly, the multi-condition identification method was used to separate different maize organ point clouds from the individual maize point cloud. In addition, the organ stratification model of field maize was established. In this organ stratification study, we take Maize-04 as the research object and obtained the recognition accuracy rates of four maize organs: tassel, stalk, ear, and leaf at 96.55%, 100%, 100%, and 99.12%, respectively. We also finely segmented the leaf organ obtained from the above-mentioned maize organ stratification model into each leaf individual again. We verified the accuracy of the leaf segmentation method with the leaf length as the representative. In the linear analysis of predicted values of leaf length, R2 was 0.73, RMSE was 0.12 m, and MAE was 0.07 m. In this study, we examined the segmentation of individual crop fields and established 3D information interpretations for crops in the field as well as for crop organs. Results visualized the real scene of the field, which is conducive to analyzing the response mechanism of crop growth and development to various complex environmental factors.

Odor Profile of Four Cultivated and Freeze-Dried Edible Mushrooms by Using Sensory Panel, Electronic Nose and GC-MS.

Cultivated mushrooms are well-known nutrient inputs for an equilibrated diet. Some species are broadly appreciated due to their medicinal properties. Lately, a number of novel foods and nutraceuticals based on dehydrated and freeze-dried powder obtained from cultivated mushrooms has been reaching the market. The food industry requires fast and reliable tools to prevent fraud. In this, work we have cultivated Agaricus bisporus sp. bisporus (AB) (white button mushroom), Agaricus bisporus sp. brunnescens (ABP) (portobello), Lentinula edodes (LE) (shiitake) and Grifola frondosa (GF) (maitake) using tailor-made substrates for the different species and standardized cropping conditions, which were individually freeze-dried to obtain the samples under evaluation. The aim of this article was to validate the use of two different methodologies, namely, electronic nose and sensory panel, to discriminate the olfactory profile of nutritional products based on freeze-dried mushrooms from the different cultivated species. Additionally, GC-MS was used to detect and quantify the most abundant volatile organic compounds (VOCs) in the samples. The multivariate analysis performed proved the utility of electronic nose as an analytical tool, which was similar to the classical sensory panel but faster in distinguishing among the different species, with one limitation it being unable to differentiate between the same species. GC-MS analysis showed the chemical volatile formulation of the samples, also showing significant differences between different samples but high similarities between varieties of the same cultivated species. The techniques employed can be used to prevent fraud and have the potential to evaluate further medicinal mushroom species and build solid and trustful connections between these novel food products and potential consumers.

Study of Agricultural Commodities Production and Price Movement in Gujarat Region: Demand and Sustainable Livelihoods

Since ancient times, agriculture has been one of the chief factors in the advancement of many nations. Also, it has been a huge contributor to the GDP (Gross Domestic Product). But the farmers and the small-scale retailers are still unaware of the demand and supply of the commodities in mandis. Ultimately, they are compelled to use the repetitive pattern of crops. Through knowledge of the intra-mandi prices for seasonal crops is necessary to gain the desired profit. Price trends in several Gujarat markets were investigated for Ajwain and Isabgol (Psyllium Husk) in order to forecast price volatility and problems that produce mandi-level projections, enhancing its usefulness. Given that medicinal crops have various medical and culinary uses, boosting their productivity could increase farmers' profits in comparison to typical pattern crops. So, the aim of this study is to evaluate the optimum preferred crop conditions for important commodities like Isabgol, Ajwain, Fenugreek, Mint and Raw turmeric with respect to profit. A data from the past decade was screened to observe the variation in prices of the crops along with production analysis. Also, it was validated statistically by various statical measures like Standard Deviation, Mean and Coefficient of Variation. We examined intra-mandi prices and found that prices fluctuate over the whole peak season, with notable fluctuations in modal prices among several mandis. To demonstrate the variations in pricing reported for certain mandis on specific days, we've given a few instances. A low standard deviation implies that the commodities price is clustered around the mean, whereas a large standard deviation shows that the commodities price is more dispersed. Price volatility is the discrepancy between what is anticipated and what occurs.

A multi-temporal phenology based classification approach for Crop Monitoring in Kenya

The SBAM (Satellite Based Agricultural Monitoring) project, funded by the Italian Space Agency aims at: developing a validated satellite imagery based method for estimating and updating the agricultural areas in the region of Central-Africa; implementing an automated process chain capable of providing periodical agricultural land cover maps of the area of interest and, possibly, an estimate of the crop yield. The project aims at filling the gap existing in the availability of high spatial resolution maps of the agricultural areas of Kenya. A high spatial resolution land cover map of Central-Eastern Africa including Kenya was compiled in the year 2000 in the framework of the Africover project using Landsat images acquired, mostly, in 1995. We investigated the use of phenological information in supporting the use of remotely sensed images for crop classification and monitoring based on Landsat 8 and, in the near future, Sentinel 2 imagery. Phenological information on crop condition was collected using time series of NDVI (Normalized Difference Vegetation Index) based on Landsat 8 images. Kenyan countryside is mainly characterized by a high number of fragmented small and medium size farmlands that dramatically increase the difficulty in classification; 30 m spatial resolution images are not enough for a proper classification of such areas. So, a pan-sharpening FIHS (Fast Intensity Hue Saturation) technique was implemented to increase image resolution from 30 m to 15 m. Ground test sites were selected, searching for agricultural vegetated areas from which phenological information was extracted. Therefore, the classification of agricultural areas is based on crop phenology, vegetation index behaviour retrieved from a time series of satellite images and on AEZ (Agro Ecological Zones) information made available by FAO (FAO, 1996) for the area of interest. This paper presents the results of the proposed classification procedure in comparison with land cover maps produced in the past years by other projects. The results refer to the Nakuru County and they were validated using field campaigns data. It showed a satisfactory overall accuracy of 92.66 % which is a significant improvement with respect to previous land cover maps.

Integrating Sentinel-1 SAR and Sentinel-2 optical imagery with a crop structure dynamics model to track crop condition

ABSTRACT Sentinel-1 and Sentinel-2 satellites are delivering data at the spatial resolutions and geographies needed for operational crop monitoring. Of particular interest, Sentinel-1 Synthetic Aperture Radar (SAR) data in Single Look Complex (SLC) format can be processed to provide polarimetric parameters sensitive to crop development. This study created a Sentinel-1 based SAR vegetation index for canola, calibrated to Normalized Difference Vegetation Index (NDVI) (SARcal-NDVI) values calculated from Sentinel-2. A Random Forest Regressor (RFR) modelled the SARcal-NDVI, from four selected polarimetric features (degree of linear polarization (DoLP), normalized Shannon entropy (SE), the second eigenvalue of the coherency matrix (l2) and the ellipticity angle (. The coefficient of determination (R2) between the Sentinel-2 NDVI and the SARcal-NDVI was 0.89. RFR-generated SARcal-NDVI estimates, based on the four SLC generated polarimetric parameters, were then used with a Canopy Structure Dynamics Model (CSDM) and with Growing Degree Days (GDD) to estimate the condition of canola at a daily time step. The SARcal--NDVI time series estimated from the CSDM model was correlated to the ground measured biomass. During the rapid accumulation of biomass from early to mid-season, correlations of the SARcal-NDVI to wet biomass were strong (R2 of 0.88). Correlations were still significant albeit weaker during pod development and throughout the period of canola senescence (R2 of 0.42). As climate variability drives uncertainty in the agricultural sector, sensors like the Sentinels can be leveraged to track changes in crop acreages and crop productivity. The next step in this research is to extend to other economically important crops such as corn, soybeans and wheat and test the ability of a Sentinel-1-based vegetation index to inform crop yield estimates.