Risk Of Crop Failure Research Articles

Climate-adaptative management strategies for soybean production under ENSO scenarios in Southern Brazil: An in-silico analysis of crop failure risk

CONTEXTSoybeans (Glycine max L.) are a crucial crop for global food security, and the state of Rio Grande do Sul (RS), Brazil, plays a significant role. However, climate instability, particularly water stress (WS), is a major concern in this region, causing large interannual yield variability. OBJECTIVEThis study aims to address this issue using an in-silico approach to: i) characterize WS and spatial patterns and their frequency within ENSO events; and ii) explore climate-adaptative management strategies such as planting dates and maturity groups to mitigate the risk of crop failure and maximize seed yield and profits. METHODSCrop growth simulations were performed testing three soybean maturity groups (MG, 5.0, 5.8, and 6.4) and eight planting dates (from October 5th to January 20th) over 30 years at 187 locations in RS, Brazil, using APSIM Next Generation. Failure risk was calculated as the percentage of simulations that yielded less than the economic break-even soybean yield in a given scenario. RESULTS AND CONCLUSIONSThe simulated yields were clustered into four regions: Northeast, North, Central, and Southwest. Four WS seasonal patterns were then defined (no stress, early stress, late stress, and whole season stress). On average, WS reduced yields up to 2 Mg ha-1 (∼50 % relative to the maximum). WS varied among regions, with the SW experiencing more frequent and severe stress (up to 50 % of whole season stress during La Nina). ENSO events influenced WS frequency, with El Niño events associated with reduced stress and La Niña events to increased stress. The MG 5.0 resulted in a higher probability of failure risk in all regions. Early planting dates resulted in the highest yield variability (up to 5 Mg ha-1). Climate-adaptative management strategies, such as optimizing planting dates and maturity groups, resulted in a 15 % reduction in crop failure. SIGNIFICANCEOur findings provide valuable insights for developing targeted approaches to enhance soybean yield stability, thereby increasing the resilience of agriculture in the face of future climate uncertainties.

Sistem Sewa Lahan Pertanian Masyarakat Pedesaan dalam Perspektif Ekonomi

This study aims to analyze the system of agricultural land leases carried out by landowners in rural Ngronggot Nganjuk District with qualitative research methods. The results of this study found that the land leasing system is called the Ngronggot Village community of Nganjuk Regency as the "Annual Adol". In the "Annual Adol" system, there are three stages of agreement found, namely: Pre agreement, Agreement, and after agreement. In an economic perspective the phenomenon of agricultural land rent is carried out by landowners against the backdrop of economic rationality which provides a higher opportunity cost when deciding to lease land compared to working on agricultural land itself mainly related to the risk of crop failure and the amount of harvest produced is lower than the cost his farming business. Whereas for land tenants the economic rationality underlying is that the greater the mastery of the cultivated agricultural land, the greater economic benefits received by the land tenant.

FARMERS' ATTITUDES TOWARDS RICE FARMING INSURANCE IN MANARAP BARU VILLAGE, KERTAK HANYAR SUB-DISTRICT, BANJAR DISTRICT

This study aimed to investigate the attitudes of farmers toward agricultural insurance programs and to explain the reasons for their participation or non-participation in these programs. Both primary and secondary data were used as sources of information. A total of 40 farmers were selected using stratified random sampling. The results revealed that program participants had a neutral attitude towards the Manarap Baru Village Rice Farmer Insurance Program (AUTP), with an index of 3.18. At the same time, non-participants tended to disagree, with an index of 2.54. The main reasons for farmers not participating in the AUTP program varied, with some citing a lack of understanding about the program despite efforts by the insurance company to provide direct counseling. Conversely, the reasons for farmers joining AUTP included various advantages, such as financial protection against the risk of crop failure, reduced loss risk, and an easy claims process in case of loss.

Management Of Pest And Disease Control In Sallots (Allium Ascalonicum L) Farm Activities Sub-District Sungai Ulin Banjarbaru City

This research aims to identify management pest and disease control and cost incurred of shallot (Allium ascalonicum L) farmer in Banjarbaru City, This research activity was carried out began on December-January 2024 with research sampling method is case study, data analysis with management implementation and financial analysis. Result of this research Pest control is very important for farmers to reduce the risk of crop failure due to pests, the purpose of pest and disease control management is not to eliminate pests altogether but to reduce the risk of attacks due to pests and diseases in shallot plants. Farmers need to apply management functions in controlling pests and diseases, namely the functions of planning, organizing, implementing and supervising. The application of management function in Bawang Mas Horti Langgeng Farmer Group has not been fully carried out properly and Average total cost incurred by each farmer is Rp.640,720. The average cost of medicines per one harvest period is 4 months amounting to Rp.277,800. The average depreciation cost of tools amounted to Rp.41,670.

Water balance optimization for strategic planting patterns and calendars in paddy (Oryza sativa L.) cultivation in rainfed regions

Paddy (Oryza sativa L.) is a staple food crop in Indonesia that requires more water compared to other annual crops. Cultivating paddy in rainfed areas necessitates effective water management to prevent crop failure, making it essential to calculate water balance, planting patterns, and planting calendars. This study aims to analyze the water balance in relation to planting patterns and calendars based on water availability in the field. The research was conducted in Cukilan Village, Semarang Regency, Indonesia. A descriptive quantitative method using Cropwat 8.0 was employed to determine water balance, planting patterns, and planting calendars. The results indicate that from November to April, there is a water surplus, while from May to October, there is a water deficit. Planting can be conducted once per growing season with two possible periods: November-March or December-April. During Period I (November-March), the water requirement is 640.7 mm with effective rainfall of 1031.2 mm. In Period II (December-April), the water requirement is 638 mm with effective rainfall of 935.7 mm. Planting should begin in November or December, with harvest in March or April. From May to October, the land remains fallow due to insufficient water availability. These findings are valuable for enhancing the effectiveness of paddy cultivation in rainfed areas, assisting farmers with planting planning, and minimizing the risk of crop failure due to water scarcity.

A Review on Biotechnological Innovations in Developing Stress-Tolerant Crops for Adverse Environmental Conditions

The development of stress-tolerant crops through advanced biotechnological approaches is critical for enhancing global food security and sustainability in the face of climate change and increasing environmental stresses. Emerging tools such as CRISPR/Cas9 gene editing and synthetic biology are revolutionizing genetic engineering by enabling precise, targeted modifications of plant genomes to improve drought, salinity, heat, and cold tolerance. Integrative approaches that combine genomics, transcriptomics, proteomics, and metabolomics provide a comprehensive understanding of plant stress responses, facilitating the identification of key regulatory genes and metabolic pathways. High-throughput phenotyping and RNA interference (RNAi) technologies further enhance trait identification and manipulation, accelerating the development of robust stress-tolerant varieties. The deployment of these crops has demonstrated significant yield improvements and stability in adverse conditions, reducing the risk of crop failures and food shortages. Drought-tolerant maize and rice varieties have increased yields by 20-30% under water-scarce conditions, while salt-tolerant rice and wheat varieties have enabled cultivation in saline soils. Heat-tolerant crops maintain productivity in high-temperature regions, and cold-tolerant varieties extend growing seasons in temperate areas. These advancements not only enhance crop productivity but also promote sustainable agricultural practices by reducing the need for chemical inputs and supporting the resilience of food systems. Moreover, the socio-economic benefits include improved livelihoods for smallholder farmers through increased incomes and economic stability. Continued interdisciplinary research and collaboration are essential to fully realize the potential of these technologies in addressing global agricultural challenges and ensuring a stable food supply for future generations.

Agricultural land measures for climate change adaptation in arid regions: Can the farmers do it alone?

Climate change has become an unequivocal issue; changing precipitation patterns and climate variability will have disastrous impacts on the fragile agricultural land resources of arid regions. Farmers, equipped with indigenous knowledge and readily available resources, are the most direct stakeholder interacting with agricultural lands; examining farmers’ capacity to successfully implement farm-level adaptive measures is a pressing matter. This paper provides an overview on various agricultural measures for climate change adaptation in arid regions and discusses some major relevant constrains and the extent of farmers’ capabilities to implement such strategies. Various techniques such as shifting sowing dates, conservation agriculture, and rainwater harvesting have shown potential to serve as adaptive strategies. These can help reduce crop failure risks, improve soil moisture conservation and increase water availability for crops, and could help adapt to precipitation variability in arid regions. However there is strong evidence that many farmers in arid regions may not be able to implement such strategies successfully as adaptive measures and there is no clear evidence that climate variability is currently less of a risk with existence of such measures. This can be attributed to the various constraints such as the need for reliable access to resources, climatic data, and research output by farmers for successful implementation. These constraints could be circumvented by institutional-level measures such as agricultural subsidies, relevant and dynamic policies, and programs. Overwhelming evidence suggests that this is only possible by involvement of various stakeholders in the planning process such as researchers, farmers and the private sector.

Determination of Micro-Insurance Premium Price for Fisheries Using Poisson-Exponential Aggregate Distribution Approach

Engaging in pond aquaculture is currently an attractive option amid the high demand for fish in the market. Entrepreneurial opportunities in the pond fish farming sector are increasingly open, although the risk of crop failure remains, both due to weather factors and the livestock process. Crop failure can have a significant financial impact on pond aquaculture cultivators. Therefore, it is necessary to have special insurance to protect against financial losses due to risks that can occur, namely Fisheries Micro Insurance. Microinsurance is a type of insurance product that is specifically designed for low-income people, offering simple, accessible, economically priced features and administration, and a fast compensation settlement process. The focus of this research is to calculate premium prices by applying an aggregate risk model approach. The data used is the number of incidents and the amount of losses due to crop failure in shrimp pond cultivation in Pandeglang Regency in the period of January 1, 2019-January 1, 2021. Data on the number of events follows the Poisson distribution, while data on the magnitude of losses follows the Exponential distribution. Furthermore, the Maximum Likelihood Estimation (MLE) method is used to calculate the parameter estimation. The average and variance estimates of the aggregate risk are used to determine the amount of premiums. The result of the premium selection in this study is IDR 42,005,600. The amount of premium reflects the collective premium resulting from the calculation based on the standard deviation principle.

Analysis of Sources and Diversification of Farmers' Household Income Around PT Jembayan Muara Bara, Tenggarong Seberang Sub-district, Indonesia

Most farming households depend entirely on sources of income from the agricultural sector, especially on-farm farming, this has the potential for quite a high risk of crop failure which results in a decrease in household income. Diversification of sources of income is aimed at creating wider employment opportunities, improving the economy in the agricultural and rural sectors, as well as developing services and service activities through linkages between agriculture and rural and non-agricultural sectors. The problem in realizing food security today is that the population continues to increase but rice production tends to decrease, thus affecting farmers' income in spending to meet basic needs, namely food. This study was conducted to find out the sources of income earned and the proportion of food expenditure of farmer households around PT Jembayan Muara Bara in Tenggarong Seberang subdistrict., Kutai Kartanegara Regency. This research was conducted in Mulawarman, Buana Jaya, Suka Maju, and Bukit Pariaman Villages, Tenggarong Seberang subdistrict. It rans from July 2023 to March 2024. Site selection is done by Purposive Sampling. Sampling using the method (Non Proportional Random Sampling) The number of samples is equalized in each village taking 25 families per village so that the total number of samples is 100 families. The types of data collected are primary data and secondary data. Data collection techniques with observation, and interviews. The results showed that sources of income in the study area, apart from farming activities, namely by working as mining employees, traders, builders, stone diggers, masseurs, farm workers, RT heads, Honorary, and civil servants; The average total family income per month ranges from IDR 2,500,000.00 to IDR 8,800,000.00. The average per capita income per month ranges from IDR 910,152.00 to IDR 1,362,339.00, while the distribution of income per sample capita per month is between IDR 416,667.00 to IDR 2,500,000.00; and the proportion of food expenditure by farmer households ranges from 50.72% - 54.51%, while the proportion of non-food expenditure by farmer households ranges from 45.45 - 49.25%.

LOSS MODEL OF CLIMATE INSURANCE BASED ON EFFECT OF GROWING DEGREE DAYS INDEX

Climate change is a threat to agriculture, especially food crops such as rice. Climate index insurance is an alternative to cover the risk of agricultural losses due to crop failure due to climate change factors. The observed climate index is the effect of growing degree days which measures the impact of temperature on plant growth and development. The data used in this study is daily temperature data at Climatology Station Class 1 Darmaga, Bogor and Meteorological Station Class 3 Citeko, West Java, during the gadu (rice that is planted in the Gadu/Dry season) planting period. In determining the amount of loss, the average daily temperature on growing degree days is calculated using a combination of a time series model and a deterministic model. The deterministic model describes the trend and seasonality of the time series at each station. The parameters contained in the model will be estimated using least-square. To see the dependence of temperature at different stations using a normal bivariate distribution. The result show that the amount of loss based on the index of growing degree days per unit rupiah per degree Celsius (℃) for Meteorological Station Class 3 Citeko only occurs for certain percentages, namely 80%, 90%, and 95%, while for Climatology Station Class 1 Darmaga Bogor it can occur for each percentage. This indicates that the amount of losses obtained will depend on determining the strike level by using the mean and standard deviation of the growing degree days index distribution. Furthermore, these findings suggest that Climatology Station Class 1 Darmaga Bogor have higher risk of crop failure due to climate change than Meteorological Station Class 3 Citeko.

Analysis of Rahn and Murabahah Financing in Baitul Maal Wa Tamwil (BMT): Qualitative Study of Agricultural Businesses in Kanigoro, Blitar Regency, Indonesia

The agricultural sector in Indonesia plays an important role in supporting food security and economic stability. However, this sector is still faced with various obstacles, one of which is limited access to capital. Baitul Maal Wa Tamwil (BMT) as an Islamic microfinance institution offers financing solutions for farmers using sharia principles, such as rahn and murabahah. This research aims to analyze the rahn and murabahah financing schemes implemented by BMT in Kanigoro, Blitar Regency, as well as their impact on agricultural businesses in the area. This research uses a qualitative approach with a case study method. Data was collected through in-depth interviews with BMT administrators, rahn and murabahah financing customers, as well as other related parties. The data analysis technique was carried out using thematic analysis. BMT in Kanigoro, Blitar Regency implements rahn and murabahah financing schemes with sharia principles in accordance with the provisions of the Islamic religion. Rahn financing uses collateral, while murabahah financing uses a buying and selling system with an agreed profit margin. BMT rahn and murabahah financing has several advantages compared to conventional financing, namely: Based on sharia principles that avoid usury; Easy and fast application process; Competitive interest rates; Assistance and coaching for customers. However, BMT also faces several challenges in carrying out this financing, namely: Lack of public understanding about sharia financing; Strict requirements for applying for financing; High risk of crop failure. In conclusion, the rahn and murabahah financing offered by BMT in Kanigoro, Blitar Regency is an alternative solution for farmers in accessing capital. The implementation of this financing scheme has a positive impact on agricultural businesses and improves farmer welfare.

Performance of BSMRAU-developed vegetables under fruit tree-based agroforestry systems

Many field crop lands are being transformed to fruit orchards in different locations of Bangladesh. There is an ample scope to cultivate intercrops in the orchards following scientific manner and proper technology including suitable varieties. In this study, mango and orange orchards were transformed to agroforestry system in Kotchandpur upazila under Jhenaidah district to investigate the performances of selected vegetable varieties (bottle gourd, stem amaranth, papaya, cauliflower, and country bean) developed by the Bangabandhu Sheikh Mujibur Rahman Agricultural University (BSMRAU). Randomized complete block design was used to compare agroforestry (mango and orange orchards) and non-agroforestry (open field) treatments for identifying suitable tree-vegetable combinations. The results indicated that yields of the tested vegetables were higher in the non-agroforestry system (crop field without trees) compared to the agroforestry system. Yields of bottle gourd stem amaranth, cauliflower, and country bean in agroforestry systems decreased by 21, 7, 18, and 19%, respectively, compared to non-agroforestry system. On the contrary, yield reductions were 7.9, 15.7, 3.9 and 4.6% in mango-based agroforestry than orange-based agroforestry for bottle gourd, stem amaranth, cauliflower, and country bean, respectively. The findings suggested that although all the tested vegetables can be grown in agroforestry, but creeping type vegetables are less suitable. Despite the observed reduction in yields, the fruit tree-based agroforestry system demonstrated its potential as a viable alternative, offering to reduce crop failure risk to the farmers and optimizing land utilization. The order of the suitability of the tested vegetables are stem amaranth > cauliflower > country bean > bottle gourd in agroforestry systems. Ann. Bangladesh Agric. 27(2):191-199

Predicting wheat yield gap and its determinants combining remote sensing, machine learning, and survey approaches in rainfed Mediterranean regions of Morocco

Wheat plays a crucial role in Morocco’s food security, economic stability, and livelihoods of farming communities. Assessing key vegetation indices (as yield predictors), along with understanding potential yield, yield gap, and major determinants for this gap at regional and national scales, is vital for improving food security with resilience in variable climatic conditions. Analysing the yield gap and its causes during drought and optimal weather conditions can reduce crop failure risks and enhance productivity specially in variable rainfed production systems. This study aimed to develop scalable methodology to predict field- and landscape-level yield and yield gaps for wheat and their underlying causes examplifying Morocco’s rainfed production environment combining remote sensing, machine learning, and ground information. By analysing six vegetation indices (EVI2, CGVI, MSR, NDVI, OSAVI, and RVI) derived from Sentinel-2 satellite imagery (10 m resolution) over three successive growing seasons (2018–2019, 2019–2020, and 2020–2021), the study employed advanced vegetation index models for accurate prediction of wheat yields and yield gaps at plot and on a larger regional scale within the Rabat-Sale-Kenitra region. To identify the determinants of yield gap, climate and soil datasets were merged with crop management information and the random forest model was fine-tuned and assessed for each season and cumulatively. The findings highlighted that RVI, GCVI, and NDVI vegetation indices were particularly effective in predicting wheat yields, showing the highest R2 and the lowest prediction errors (RMSE). Such predictive methodologies are crucial for policymakers to proactively plan and mitigate risk minimization and adaption plans at regional and national levels. The models predicted rainfed potential yields of 5.99, 1.53, and 4.66 t ha−1, with corresponding yield gap of 3.38, 0.73, and 1.58 t ha−1 for the seasons of 2018/2019 (favorable); 2019/2020 (drought) and 2020/2021 (favorable), respectively. Across three periods, critical factors determining yield include soil moisture, total rainfall during the crop growing period, evapotranspiration, and soil texture and carbon content. To minimize drought risks and maximize benefits during variable rainfall conditions, it is essential to implement pre-season drought forecasts, customize seeding dates based on soil moisture, adopt technologies that enhance soil moisture retention, and utilize climate-adapted farming practices in the semi-arid and arid rainfed regions.

Refining tomato disease recognition: hyperparameter tuning on ResNet-101 architecture for precise leaf-based classification

Tomatoes plants are widely recognized as versatile vegetables globally. This study aims to develop a high-precision web interface for classifying various leaf diseases in tomatoes. Utilizing a convolutional neural network (CNN) algorithm using the residual network-101 (ResNet-101) architecture, this tool aids farmers in accurately identifying leaf diseases in tomatoes, thereby reducing the risk of crop failure. The dataset comprises 6,800 images, categorized into four classes: early blight, spider mites two spotted, tomato yellow leaf curl virus, and healthy tomatoes, each containing 1,700 images. Hyperparameter tuning was conducted as part of an experiment aimed at enhancing the performance of the model. Experiments involved varying epoch values (10, 25, 30, 50, 60, 75, 100, and 110), a fixed batch size of 4, different learning rates (0.1, 0.01, 0.001, 0.0001), and num workers (4, 8, 16). The results demonstrated an accuracy of 99% with 100 epochs, a batch size of 4, a learning rate of 0.001, and 16 num workers. Consequently, this research contributes to a deeper understanding of disease management in tomato plants, ensuring optimal quality and quantity of the harvest.

RAFT: An IoT-Based Nutrition Monitoring System for Bok Choy Hydroponics Plants

The Internet of Things (IoT) plays a crucial role in technology advancements, especially in the agricultural sector, such as hydroponics. Manual monitoring of parameters like nutrient levels, pH, and water levels in plants consumes farmers' time and energy and increases the risk of crop failure. This research aims to evaluate the effectiveness of using IoT RAFT (Remote Automated Farming Technology) system for farmers, particularly hydroponic bok choy farmers, in monitoring and controlling plant nutrient levels and the development process using waterfall as a research methodology. Parameters tested in this research include bok choy height, number of leaves, and bok choy harvest weight. We conducted this research on 14 plants for one harvesting period, then we used linear regression to determine the growth rate by calculating the slope. The results show that the plant height, the number of leaves, and harvest weight using the IoT RAFT system are 0.5897 cm/day, 0.6391 leaves/day, and 216.43 grams, respectively. We also compared the IoT RAFT system with a non-IoT bok choy growing method, and we concluded our IoT RAFT system has a better growth rate compared to the non-IoT bok choy growing method.

ABILITY CONVOLUTIONAL FEATURE EXTRACTION FOR CHILI LEAF DISEASE USING SUPPORT VECTOR MACHINE CLASSIFICATION

Chili plants are among the most commonly used food ingredients in various dishes in Indonesia. Leaves on chili plants are often affected by disease; if the disease is not treated immediately, it can damage the plant and cause crop failure. Early detection of chili plant diseases is important to reduce the risk of crop failure. The development of technology and the application of machine-learning algorithms can automatically monitor chili plants using a computer system. Using this algorithm, the system analyzes and identifies diseases that a camera can observe and record. In this study, the proposed method for feature extraction uses a convolutional neural network (CNN) algorithm with transfer learning using VGG19. For classification using SVM for training data, accuracy generated 95%, precision 95%, recall 95%, and F1-Score 95%, and testing data accuracy generated 90%, precision 89%, recall 90%, and F1-Score 89%, proving that the convolutional process with architecture VGG19 and SVM algorithm is acceptable for classification. In future research, other architectures or extraction fusions can be used to maximize the results.

Advantages of early-maturity soybean varieties as a farmer adaptation effort to climate change in Grobogan Regency

The climate change impact is cropping patterns change, including unpredictable droughts and floods. These conditions require soybean farmers to adapt, one of them is adopting early maturing soybean varieties. This research aims to identify the advantages of early-maturity soybean varieties, financial feasibility, and technical Efficiency. This study collected data from 40 soybean farmers in the Grobogan Regency. Soybean farming data used in planting season III (May-August) 2021. Biosoy varieties are early maturing, compared to the Anjasmoro varieties as existing soybeans. The financial feasibility of early-maturing soybeans is analyzed by the Marginal Benefit Cost Ratio (MBCR), while technical Efficiency is by stochastic frontier. The research showed that a) advantages of Biosoy early-maturing soybeans include higher production, larger seeds, and reduced risk of crop failure due to drought; b) Biosoy varieties are feasible to develop with an MBCR level of 3.19, and c) an average technical efficiency is 0.92. Biosoy varieties demonstrated better advantages than Anjasmoro varieties. The government, as a policy maker, is expected to be able to encourage farmers to adopt Biosoy varieties and optimize production inputs to make them more efficient so that farmers can adapt to climate change adaption.

Perspectives of rural farming households on home gardens as an agroforestry for food security: a qualitative study in Indonesia

In developing countries, rural farmers contribute significantly to food production, but their households are still vulnerable to poverty, food insecurity, and malnutrition. Food security is a concept that refers to the ability of a country or region to ensure the availability, accessibility, and utilization of sufficient and nutritious food for its entire population, both in normal situations and under crisis conditions such as natural disasters or political instability. Food security is critical as it is directly related to national security, public health, and sustainable development. To improve food security, governments and international agencies often focus on policies such as increased food production, diversification of food sources, sustainable management of natural resources, and food assistance programs for vulnerable populations. The practice of agroforestry in home gardens offers a variety of readily available, cost-effective, and nutritious food options, which plays a significant role in enhancing the food security of agricultural families. Agroforestry, an integrated land management system combining food crops, trees, and sometimes livestock, offers a potential solution to improve food security. By growing various crops, farmers are not solely dependent on a single source of food, which reduces the risk of crop failure. Agroforestry systems increase resilience to climate change by reducing the impact of natural disasters, such as floods and droughts. Trees can act as wind barriers, reduce soil erosion, and aid in water conservation. This qualitative research with a systematic exploratory approach and grounded analysis examines how agroforestry contributes to farmers' food security by conducting in-depth interviews with farmers in North Luwu, Indonesia. The study identified home garden categories of agroforestry and then evaluated the role of agroforestry food production in terms of securing the food needs of households. The study discovered that restructure food bolsters across various income brackets by promoting healthier eating habits. Additionally, food production enhances the sustainability of food supplies for households and the health and prosperity of agriculturalists. The primary obstacles to producing food at the household level were identified as secure access to appropriate land and the possession of adequate farming expertise. Key words: agroforestry, farmer, rural, food security, household, home garden, Indonesia

PERANCANGAN ALAT MONITORING PERUBAHAN PPM DAN SUHU AIR SECARA DIGITAL

Limited land in Bekasi City is not an obstacle to farming for residents of Mustika Jaya, Bekasi City. The residents formed the "Amanah Hidroponik" Farmers Group in Cimuning Village, Mustika Jaya Subdistrict, Bekasi City to farm on the public facility land using the hydroponic method. The problem faced is the low participation of people in efforts to record ppm and water temperature data every hour and the lack of knowledge about the importance of data from the recording of ppm and water temperature data. This research aims to design a digital monitoring tool for ppm and water temperature changes to provide convenience to farmers in obtaining ppm information through Android mobile applications in real-time. Data collection methods were carried out by field studies and interviews of farmer group members. The results are in the form of a prototype with the ability and success of mixing nutrient water and clean water according to the predetermined ppm value of nutrients for each type of plant, providing benefits for hydroponic kale farmers in controlling nutrition and monitoring plants. In addition, it can also reduce the risk of crop failure of kale plants because it monitors by using a smartphone in real-time

Study of Developing Models of Crop Failure Risk Information

Climate is one factor that can influence plant growth. The risk of crop failure due to climate variability can be in the form of reduced water sources, which impact water needs in the land and the emergence of pests and diseases in plants. The risk of planting failure can impact product quality, which has the potential to decrease, higher plant handling costs, and various things that cause losses to farming businesses. The availability of climate forecast information, such as rainfall and other parameters, encourages writers to apply it to information that is easier for users to understand. One of the machine learning algorithms, Decision Tree, is used as a model in determining the risk of planting failure based on each attribute/parameter, including monthly rain, ENSO and IOD phenomena, drought, groundwater availability, and Oldeman climate type. This study aims to make a model prediction of crop failure risk potential, and the calculation is based on climate prediction data. The results of this study show differences in climatic conditions for each commodity when there is an increased potential risk of planting failure. Monthly rainfall is the most dominant factor influencing rice, maize, and soybean planting failure. Validation of the decision tree model shows that this model is quite good in determining the potential risk of crop failure in all commodities studied, with the proportion of correct proportion of more than 65%. However, the Heidke Skill Score (HSS) shows that this model is good for Paddy and Soybean; Maize shows an HSS of less than zero.