Losses For Farmers Research Articles

PLANTS UNDER THE RISK DROUGHT AND DESERTIFICATION IN SOUTHEASTERN ANATOLIA (TURKEY)

This study examines the impact of climate change on the semiarid region of Southeastern Anatolia, Turkey, which borders the northern Arabian deserts. The region faces significant risks of desertification due to the expansion of arid zones, affecting precipitation, evaporation, water resources, and vegetation. The loss of biodiversity is already a critical issue, exacerbated by drought conditions. Agricultural productivity is threatened as drought reduces water availability, leading to lower yields and quality of crops. Economic consequences include increased food prices, farmer losses, and potential migration. The study aims to identify at-risk agricultural products and propose measures to combat drought and ensure food security. KEY WORDS: Climate change, drought, desertification, soil, evaporation, endangered species

Analysis of Drought Index with Theory of Run Statistical Method in Dompu Regency

Indonesia has in recent years experienced severe drought in some areas. Climate change causes temperatures in Indonesia to become hotter and makes rainfall patterns erratic or El-Nino. Indonesia is an agricultural country that makes the agricultural sector as one of the livelihoods for many people, but droughts that occur in several regions in Indonesia result in losses for farmers because the agricultural crops planted have failed harvests, resulting in reduced community income. Dompu Regency is one of the areas experiencing drought. Analysis of the drought index in Manggalewa District using the Theory of Run method with the aim of determining the prediction of the duration of rain for a period of 10 years. The results showed that in the period 2003-2022, the longest drought duration was 11 events that occurred from March 2014 to January 2015 with a deficit value of 430.05 mm from the average normal rain, while the duration of wet months was 12 events that occurred from March 2021 to February 2022. Meanwhile, in the 2023-2032 period, the longest drought duration is 8 months which occurs in February-September 2027, while the worst deficit value occurs in December 2030 to January 2031 of 235.93 mm from the average normal rain, while the duration of wet months is 6 events that occur in August 2025 to January 2026.

Komparasi Metode Isolasi DNA dalam Mendeteksi Gen toxR Bakteri Vibrio parahaemolyticus pada Udang Vaname (Litopenaeus vannamei)

Vibrio parahaemolyticus bacteria is one of the dangerous pathogens in vaname shrimp farming (Litopenaeus vannamei). Infection with this bacterium can cause mass mortality in shrimp and considerable losses for farmers. This bacterium also has a specific gene, the toxR gene, which is used to detect its presence. The successful detection of this bacterium determines the success of handling the disease it causes. The purpose of the study was to determine the optimal DNA isolation method in detecting the toxR gene of Vibrio parahaemolyticus bacteria in vaname shrimp. The research was conducted by means of molecular biology-based identification using two DNA isolation protocols, namely Chloroform and Boilling lysis buffer. Visualization results showed that toxR gene DNA appeared in samples isolated with Boiling Lysis Buffer in Tryptic Soy Broth (TSB) media culture preparation. This means that the boiling lysis buffer method is a suitable DNA isolation method for detecting the toxR gene of the Vibrio parahaemolyticus bacteria in white shrimp.

Law Enforcement Against Business Actors Who Cause Water Pollution

This research aims to examine law enforcement against business actors who cause water pollution in Cianjur Regency. Today's rapid development has a negative impact on the environment. Liquid waste from industry and domestic is the main cause of water pollution, which threatens the quality of water used as a source of daily needs. This research uses a normative juridical approach and descriptive analysis method. Data is obtained through literature studies by searching for primary materials and supported by secondary materials. The impacts of water pollution by business actors include: a decrease in the quality of water resources, public health problems, and losses for farmers. The implementation of law enforcement against business actors is faced with several obstacles, including the lack of awareness of the importance of the environment and the lack of effective criminal sanctions. To maintain the right to a good and healthy environment, serious environmental protection and management as well as strict law enforcement are required by all parties concerned. Obstacles in law enforcement against business actors who commit water pollution include non-optimal environmental protection and management, violations of existing regulations, and obstacles in the waste filtration process. Therefore, decisive action is needed in law enforcement, including operational requirements, waste treatment, and more effective supervision to control and prevent water pollution in Cianjur Regency.

Microbial Interactions in the Management of Groundnut Stem Rot

Groundnut (Arachis hypogaea L.) is an important oil seed crop in the world belonging to the Leguminosae family. It is one of the essential food and cash crops of our country. In India, a large number of diseases attack groundnut [1]. Among soil borne diseases, stem rot or white mold caused by Sclerotium rolfsii Sacc. [2] is an important disease causing significant yield losses in several groundnut growing countries (Mehan et al., 1994). Sclerotium rolfsii Sacc. is a destructive soil borne fungal pathogen of oil seed crop in India. Different practices are recommended for management of groundnut stem rot such as deep summer ploughing, destruction of plant debris, crop rotation with jowar and bajra, seed treatment with carbendazim or captan or mancozeb or tebuconazole, soil drenching with hexaconazole, application of ammonium sulphate or calcium ammonium nitrate instead of urea and application of gypsum at flowering stage. Further, no single treatment is full proof and disease continues to cause losses in farmers’ fields. Biological control offers an interesting alternative to fungicides for sustainable management of soil borne diseases. Biocontrol is a non-chemical measure, could be effective as chemical control by various techniques. Among the techniques, the mixture of antagonists was studied to enchance the control efficacy. In most studies, the involvement of only mechanism of biological control is demonstrated. Involvement of more than one mechanism has been reported in only a few systems. Use of several biocontrol agents with several mechanisms of control fits in well with the concepts of integrated disease management, in which several means of disease suppression are applied concurrently. When single or more means of mechanisms are not effective, the others may compensate for the former absence. The present study, involved three major bacterial antagonists viz., Streptomyces violaceusniger, Streptomyces exfoliatus and Pseudomonas fluorescens to find out the effective dose of mixtures. The different doses from 10-1 to 10-5 were studied, the dose 10-1 of Streptomyces violaceusniger have more efficiency of 86.70, than S. exfoliatus. The efficacy of Streptomyces violaceusniger with combination of other antagonists were tested and found that, the mixture of Streptomyces violaceusniger and P. fluorescens have synergistic activity than any other combinations which have synergistic factor greater than one.

Model Analysis of Gated Recurrent Unit for Multivariate Rice Price Forecasting

Food security, especially in the agricultural sector in the form of food price stability of rice as a national food ingredient is a strategic issue for Indonesia. Rice price forecasting is needed to mitigate rising rice food prices. Rice price fluctuations can be caused by internal factors such as bad weather or external factors such as the low selling price of rice, resulting in losses for farmers. This study aims to carry out multivariate rice price forecasting in DKI Jakarta by involving rice prices, weather, economic, and health factors using the Gated Recurrent Unit (GRU) algorithm where the accuracy test is based on the MAPE value between forecasting results and actual data. As a result of the GRU algorithm for multivariate rice price forecasting, the MAPE for training and testing is 0.964% and 2.628%, indicating that all models in the measurement category are very well represented.

The dynamics of changes in soil temperature due to the application of rice straw mulch on lettuce (Lactuca sativa L.)

Climate change that occurs globally in agriculture causes fail crops production, causing losses for farmers. This loss can be minimized by using rice straw mulch to reduce soil temperature and protect the soil from direct solar radiation on lettuce land. This study will examine whether treatment with rice straw mulch can affect soil temperature in lettuce land. The research was conducted at Nudira Fresh Green House PT. Nudira Sumber Daya Indonesia, Warnasari Village, Pangalengan Subdistrict, Bandung District, West Java. This research was conducted from March 1, 2022 to August 31, 2022. The data of this study used a factorial 4 x 2 Randomized Block Design with 3 replications. Factor I was the thickness of rice straw mulch 0 t ha− 1 (R0), 4 t ha−1 or 0.48 kg plot−1 (R1), 6 t ha−1 or 0.72 kg plot−1 (R2), and 8 t ha−1 or 0.96 kg plot−1 (R3) and factor II was the dose of cow manure (F1) and compost (F2) each 20 t ha−1 or 2.4 kg plot−1. The results showed that the application of rice straw mulch with the organic fertilizer has not significant effect on soil temperature in lettuce land. However, the application of rice straw mulch at a dose of 8 t ha−1 slightly better results in decreasing soil surface temperature at a depth of 0 cm, although not significantly different.

IMPLEMENTATION OF CERTAINTY FACTOR METHOD IN PEST AND DISEASE DIAGNOSIS IN HYDROPONIC PLANTS

Pest and disease attacks often occur in hydroponic plants so that many plants are damaged and result in losses for farmers or crop failure. The problem of pests and plant diseases is the main obstacle in increasing agricultural productivity, these conditions affect farmers' income and the provision of hydroponic plant food. The farmers do not know what types of pests and symptoms of diseases attack plants, making it difficult to handle diseases on hydroponic plants. Likewise for hydroponic plants located at the Agricultural Service of Asahan Regency which are attacked by pests, hydroponic experts say that the appearance of small leaf spots appears on the upper side of the leaves that damage hydroponic plants and because the cause of pests that cause disease in hydroponic plants is unknown, so farmers are hesitant to take countermeasures. An expert system is a computer program designed to make decisions such as decisions taken by an expert or expert. Therefore, an expert system with the Certainty Factor method is made in the diagnosis of pests and diseases in hydroponic plants. The purpose of this study was to apply the certanity factor method to diagnose pests and diseases in hydroponic plants. The results of the pest data test found spider mites with a confidence level of 79% and wet rot disease with a confidence level of 62%. The conclusion obtained is that the certainty factor method can detect early types of pests and diseases on hydroponic plants quickly and accurately.

Impact of COVID-19 on Agri Supply Chain in Nashik District

India has faced many pandemic diseases like plague, swine flu, cholera, pneumonia, bird flu, etc., affecting the Indian economy. But COVID-19 is adversely affecting the country. To be specific Agriculture sector is most vulnerable to losses everywhere. Indian agriculture has not been very progressive as compared to other industries. Farmers don’t get a reasonable price for their produce. Farmers here are illiterate and trick by money lenders, middle-man, traders, etc. Most of the margin of farmers taken by middle-men results in loss of farmers and customers getting the product at a high price. Also, there are fewer storage facilities, so they have to sell their produce immediately after harvesting. Considering the research region, i.e., Nasik District, where most of the farm produce like grapes, cereals and vegetables have harvesting season in this period. Due to lockdown in the country, as COVID hit here, there have been more significant losses to grapes and other farms produce like cereals and vegetables. Nasik, which is the region of my research, has around 6 lakh farmers, wherein I have taken a sample survey of 100 farmers, which comprise small, marginal, medium, and large farmers. There is a need for change in agricultural practices wherein farmers should adopt concepts like proper supply chain management, cold storage, processing in a farm of whichever produce is possible, Direct marketing, Private wholesale market, organized retailing. Cold storage and cooperative storage facilities can play a significant role in such a critical situation. The need for change in agricultural marketing and coping with such issues as COVID is of utmost importance. New innovative ideas can help to sustain farmers in such severe problems.

An AI and Cloud Based Collaborative Platform for PlantDisease Identification, Tracking and Forecasting for Farmers

Plant diseases are a major threat to farmers, consumers, environment and the global economy. In India alone, 35% of field crops are lost to pathogens and pests causing lossesto farmers. Indiscriminate use of pesticides is also a serious health concern as many are toxic and biomagnified. These adverse effects can be avoided by early disease detection, crop surveillance and targeted treatments. Most diseases are diagnosed by agricultural experts by examining external symptoms. However, farmers have limited access to experts. Our project is the first integrated and collaborative platform for automated disease diagnosis, tracking and forecasting. Farmers can instantly and accurately identify diseases and get solutions with a mobile app by photographing affected plant parts. Real- time diagnosis is enabled using the latest Artificial Intelligence (AI) algorithms for Cloud-based image processing. The AI model continuously learns from user uploaded images and expert suggestions to enhance its accuracy. Farmers can also interact with local expertsthrough the platform. For preventive measures, disease density maps with spread forecasting are rendered from a Cloud based repository of geo-tagged images and micro-climactic factors. A web interface allows experts to perform disease analytics with geographical visualizations. In our experiments, the AI model (CNN) was trained with large disease datasets, created with plant images self-collected from many farms over 7 months. Test images were diagnosed using the automated CNN model and the results were validated by plant pathologists. Over 95% disease identification accuracy was achieved. Our solution is a novel, scalable and accessible tool for disease management of diverse agricultural crop plants and can be deployed as a Cloud based service for farmers and experts for ecologically sustainable crop production.

Road to the city: Impact of land expropriation on farmers’ urban settlement intention in China

The gains and losses of farmers’ welfare brought by China’s land expropriation have been of broad concern, but little literature has focused on the motivation of land-lost farmers to settle in cities. Although the Chinese government has implemented a series of major strategies influencing land-lost farmers’ migration, the citizenization of rural migrants has always been a challenge. Using the data from 2016 China’s Labor-force Dynamic Survey (CLDS), this paper discovers that land expropriation strengthens land-lost farmers’ willingness toward urban settlement. Two underlying mechanisms that may influence the decision of land-lost farmers to settle in cities are revealed: positive economic factors and negative social-cultural factors. The economic incentives caused by land compensation can promote the probability of farmers’ urban settlement intention. However, farmland loss also brought an increased employment risk for farmers. Besides, after the land expropriation, the land-lost farmers have more frequent contact with their old neighbors. These social-cultural factors would hinder the farmers’ willingness toward urban settlement. Our findings are robust using both Instrumental variable (IV) estimation and the propensity score matching (PSM) method. These findings provide a deeper understanding of the economic and social consequences of land expropriation policies. In addition to land expropriation, more attention should be paid to the resettlement of land-lost farmers.

Seroprevalence and Risk Factors Associated with Toxoplasma gondii and Chlamydophila abortus Infection in Domestic Small Ruminants in Cameroon

Toxoplasmosis and chlamydiosis remain among the primary causes of abortion and fetal loss in small ruminants. Consequently, they are a source of considerable economic losses for farmers. The objectives of this study were to determine the seroprevalence and highlight the risk factors associated with Toxoplasma gondii and Chlamydophila abortus infection in small ruminants in Cameroon. A cross-sectional study was conducted in 200 small ruminant farms during a period from April to October 2021. A total of 1061 small ruminants were sampled, and the sera obtained were analyzed using the indirect ruminant multi-species ELISA test for specific IgG antibody detection for T. gondii and C. abortus at the National Veterinary Laboratory. A questionnaire was constructed to collect information about flock management and risk factors possibly associated with T. gondii and C. abortus infection in goats and sheep. Overall, 329 small ruminants tested positive for T. gondii-specific IgG antibodies and 45 for C. abortus-specific IgG antibodies with a seroprevalence of 31.1% (95% CI: 28.2–33.8) for T. gondii and 4.2% (95% CI: 3.0–5.5) for C. abortus, respectively. However, a significant co-infection of 1.8% (95% CI: 0.37–3.3) was obtained between T. gondii and C. abortus (p = 0.02). Linear regression model analysis revealed that, the seroprevalence of T. gondii at the flock level was significantly correlated with the region (0.22 CI: 0.17; 0.26) the breeding objectives (0.36 CI: 0.17; 0.56) the level of hygiene (0.11 CI: 0.01; 0.21) and presence abortions (0.55 CI: 0.46; 0.64) on the farms and that of C. abortus was significantly correlated with the regions (0.01 CI: 0.00–0.02) and degree of abortion on the farms (−0.26 CI: −039; −0.14). At the individual level, a significant correlation was observed between the seroprevalence of T. gondii and region (−0.09 CI: −0.14; −0.04), species (0.07 CI: 0.01–0.14), sex (−0.11 CI: −0.18; −0.03), age (0.08 CI: 0.04–0.12) and physiological status (0.21 CI: 0.18; 0.24). For C. abortus seroprevalence, a significant correlation was observed with the regions (0.02 CI: 0.00; 0.03) and breed (0.01 CI: 0.01; 0.02) of small ruminants reared in the northern regions of Cameroon. These results could be used to implement efficient management measures to prevent and control T. gondii and C. abortus infection in goats and sheep in Cameroon.

Psychological Welfare Loss of Land-Expropriated Farmers in China

Mandatory land expropriation, resettlement and welfare loss of landless peasants have received increasing research attention. However, in most previous analyses, the loss of psychological welfare of landless farmers is often neglected, which may lead to biased land requisition compensation and resettlement programs and incomplete policy recommendations. This study attempts to use a three-dimensional framework of place attachment to analyze the causes of psychological welfare loss. China’s land expropriation and centralized resettlement cut off the material and emotional ties between farmers and land. Farmers’ dependence on land, or their dependence on land-dependent farming lifestyle, has been neglected in the process of land acquisition and resettlement, resulting in the loss of farmers’ psychological welfare. Based on a comparative analysis of the existing methods, this paper chooses the contingent valuation method (CVM) to measure the loss of psychological welfare of landless peasants via monetization. The feasibility of the model has been tested at a small scale in nine cities. The results show that 1) farmers have strong place attachment on agricultural land, although centralized land expropriation and resettlement have neglected this dependence and caused psychological welfare losses to landless peasants; and 2) the loss of psychological welfare of landless peasants can be measured by the CVM and presented in monetary units. The monetization measurement method in this paper still has some shortcomings and its feasibility requires more empirical tests. However, as a rare monetization measurement study, this paper can direct academic and policy attention towards this psychological welfare loss.

Investment feasibility and risk management of a small-scale layer business in the Province of North Sulawesi, Indonesia

The main expectation of investment in the layers production is to provide benefit for small-scale farmers, who have limited capital and deal with varied business risks. The purpose of this research is to know the feasibility of financial investment and determine an alternative scenario in dealing with risk for farmers in the small-scale egg-layer business with limited capital. The research, then, employed a survey method with a multi-stages sampling technique on 85 farmers of the small-scale layer business located in the District of Tomohon Utara. The City of Tomohon as the center of layers population in the Province of North Sulawesi. Data collection used a list of questions related to the research problem. Further, the use of financial analysis was by criteria of investment feasibility with the measurement of net present value, cost ratio of benefit, internal rate of return, and followed by sensitivity analysis related to business risk. The results of this research demonstrate that the total rate of layer chickens raised by farmers was 2,136 birds and the net present value (NPV) was IDR 36,213,611. Within the five-year investment, the cost ratio of benefit (B/C) was 1.05, and the internal rate of return (IRR) was 22.85 %. Under the current management of a small-scale layer business, performed by farmers, the layers business was feasible to deal with the risk of increasing feed price, increased mortality, and declining egg price, which those factors caused loss for farmers. Therefore, based on the sensitivity analysis, the small-scale farmers had to improve their daily rate of production of eggs by 76%, when the feed price increased by 5%, or the egg price declined by 5%, in minimizing and preventing loss.

The Clustering Rice Plant Diseases Using Fuzzy C-Means and Genetic Algorithm

Rice is an agricultural sector that is very important for Indonesia's economy. The main problem with rice plants is pest and disease control which has a very dangerous impact as well as economic losses for farmers. The characteristics that are very visible on rice leaves have a greater area than other plant structures, rice leaves can be applied for early diagnosis of rice plant diseases. Fuzzy C-Means (FCM) and Genetic Algorithm-Fuzzy C-Means are the approaches employed (GA-FCM). The center of the cluster is obtained while adopting genetic algorithms for optimization. The primary dataset used in this research is Teaching Sawah Farm IPB, and the secondary dataset is UCI Rice Leaf Diseases. According to the results of the comparison the GA-FCM optimization results in a higher level of clustering precision with a 65% optimal cluster center point on the silhoutte coefficient value compared to just 60% for FCM. This research shows the results that the proposed method can add 5% accuracy to the clustering results in terms of identifying the types of rice plant diseases properly.

As the number falls, alternatives to the Hagberg-Perten falling number method: A review.

This review examines the application, limitations, and potential alternatives to the Hagberg-Perten falling number (FN) method used in the global wheat industry for detecting the risk of poor end-product quality mainly due to starch degradation by the enzyme α-amylase. By viscometry, the FN test indirectly detects the presence of α-amylase, the primary enzyme that digests starch. Elevated α-amylase results in low FN and damages wheat product quality resulting in cakes that fall, and sticky bread and noodles. Low FN can occur from preharvest sprouting (PHS) and late maturity α-amylase (LMA). Moist or rainy conditions before harvest cause PHS on the mother plant. Continuously cool or fluctuating temperatures during the grain filling stage cause LMA. Due to the expression of additional hydrolytic enzymes, PHS has a stronger negative impact than LMA. Wheat grain with low FN/high α-amylase results in serious losses for farmers, traders, millers, and bakers worldwide. Although blending of low FN grain with sound wheat may be used as a means of moving affected grain through the marketplace, care must be taken to avoid grain lots from falling below contract-specified FN. A large amount of sound wheat can be ruined if mixed with a small amount of sprouted wheat. The FN method is widely employed to detect α-amylase after harvest. However, it has several limitations, including sampling variability, high cost, labor intensiveness, the destructive nature of the test, and an inability to differentiate between LMA and PHS. Faster, cheaper, and more accurate alternatives could improve breeding for resistance to PHS and LMA and could preserve the value of wheat grain by avoiding inadvertent mixing of high- and low-FN grain by enabling testing at more stages of the value stream including at harvest, delivery, transport, storage, and milling. Alternatives to the FN method explored here include the Rapid Visco Analyzer, enzyme assays, immunoassays, near-infrared spectroscopy, and hyperspectral imaging.

Impact of climate change on the agriculture sector and its adaptation strategies

Climate change in Indonesia, including erratic rainfall intensity, extreme temperatures, El Nino and La Nina events, can cause farmers not to implement agricultural efforts optimally. Multiplier effect caused is the risk of decreased agricultural production and even crop failure that causes losses for farmers. The impact can broadly reduce farmers’ welfare, food security and increase poverty, for which most Indonesians livelihood as farmers. The negative consequences of climate change require efforts to minimize the impact and anticipate through adaptation strategies. These research aims are to analyze the impact of climate change on the agriculture sector and develop an adaptation strategy to address the impact of climate change on the agricultural sector. This method uses review literature related to the impact of climate change on agricultural products and adaptation strategies to climate change in the agricultural sector. The impact of climate change caused a decline in agricultural production and even crop failure. The main actors of climate change that affect the agricultural sector include rain patterns, extreme climate events (floods and droughts), and increased air temperatures. Recommendations for adaptation strategies are using superior plants, adjusting planting time and pattern, water management technology (rain harvesting and irrigation), and biotic environmental strategies.

Leaf Disease Detection

Leaf disease detection is the process that help the users to detect the disease of the plant leafs. It is not only useful for farmers but also useful for plant researchers.Now a days agriculture wealth has been increased in each and every country particularly the agricultural strength in main in rural areas. But the technology hasn‟t inproved in rural area so people over there certainly not educated about plant disease. That causes heavy loss of plants and some of the disease can pass through one plant to another. That cause aheavy loss of plant as it gives a great loss for farmers. But we can prevent them by using this application. The primary obejective of this project to identify and detect the leaf disease from leaf image data using image processing approach, which is implemented using python. To perform this task, this work used convolution Neural network(CNN) algorithm for identify the leaf disease based on their unique physi3al shapes through geometric feather and venation architectures.Both real time and live detection of disease is done for the leafsamples.

Webmap Application Training As An Effort For Flood Mitigation In The Agricultural Sector In Dlanggu Village, Lamongan Regency, East Java, Indonesia

Natural conditions are increasingly uncertain and difficult to predict due to changes in rainfall patterns and climate. Excessive rainfall can cause flooding which is detrimental to the agricultural sector. Flooding is a natural challenge that is often faced in the agricultural sector. This challenge is faced by most of the agricultural land in Lamongan Regency, including in Dlanggu Village. The impact of flooding on rice fields in certain locations which is increasingly widespread and intensive from year to year has caused considerable losses for farmers. The purpose of this activity is to map flood-affected agricultural land in a participatory manner with the help of the WebMap application. The method of implementing this activity includes the preparation stage, implementation stage, and monitoring and evaluation stage. The results of the activity showed that participants were very enthusiastic about this activity program, as evidenced by the positive response in the form of the presence of 96% of the training participants and active discussion activities during the socialization and training took place. After the training, the participants also expected a follow-up in the form of assistance from the service team to improve skills with WebMap applications in other fields. This activity is expected to be a solution that can be offered to provide an early warning system based on mapping so that the delivery of information about floods can be conveyed more quickly to the community, especially people who have agricultural livelihoods to be aware of flood disasters.

Florecimento precoce, dissimilaridade genética e agrupamento de cultivares de alface com sementes tolerantes à termoinibição

ABSTRACT High temperatures interfere with lettuce cropping. Thermoinhibition of seed germination and early flowering cause important losses for farmers. The objectives of this work were to evaluate the tolerance to early flowering, to verify the genetic dissimilarity and to select lettuce cultivars tolerant to seed thermoinhibition. 18 cultivars were evaluated. The number of days until the first anthesis was verified for early flowering. Regarding seed thermoinhibition, the first and final germination counting and germination speed index were evaluated, using temperatures of 20, 25, 30 and 35ºC. The analyses were performed immediately after harvest and after six months under storage. The genetic dissimilarity was obtained using the Tocher Graph optimization and hierarchical UPGMA methods. Regression models were adjusted, and curves’ clustering was performed by testing the identity of the models. Early flowering was observed in cultivars Floresta, Colorado, Grand Rapids, and Everglades. Cultivars showed genetic variability and are dissimilar regarding tolerance to thermoinhibition. By clustering the regression equations, it was possible to select thermoinhibition tolerant cultivars.