Cultivation Planning Research Articles

Evaluation of infiltration rate and pedotransfer function under different landuse patterns for Kalyanpur block, Bihar

The information on the pedotransfer function of soil is vital for crop cultivation planning and management. The present study evaluated the pedotransfer function of ten locations (different villages) in the Kalyanpur block of Bihar state, India. The various soil parameters like soil texture, infiltration rate, soil penetration resistance, Bulk density, porosity, water holding capacity, available water content, field capacity, permanent wilting point, gravimetric moisture content and organic carbon were evaluated. The infiltration rate and soil penetration resistance were measured in the field and other parameters were evaluated in the laboratory with suitable techniques. The observed data of the above parameters were analyzed for given locations. A good coefficient of determination (R2 = 0.61) was discovered for the linear relationship between the infiltration rate as determined by the double ring infiltrometer and the soil penetration resistance (measured using a cone penetrometer down to a depth of 60 cm). Further, the nomographic representation of soil texture, final infiltration rate (mm h−1) and weighted mean of penetration resistance (kPa) was also developed. Furthermore, the regression analysis was done on five combinations (C-1, C-2, C-3, C-4 and C-5) of obtained parameters in which infiltration rate was taken as output and other parameters as input parameters to develop the pedotransfer model. Based on the obtained results, all combinations showed good correlation and C-3 with input parameters such as sand, silt, clay, porosity, GMC and AWC were found superior with good coefficient of determination values (R2 = 0.914). C-3 combination has the edge over C-4 combination (input: sand, silt, clay, porosity and organic carbon), having R2 values of 0.910. Therefore, the information obtained from the study will be helpful for policymakers in irrigation and other crop cultivation activities.

GIS-Based Tool for Pest Specific Area-Wide Planning of Crop Rotation Distance with Land Use Data.

Crop rotation is an important strategy for pest reduction. For mono-, or oligophagous pests that overwinter at a previously infested site, crop rotation means that the pests must find new host crop sites in the following year, and it is more efficient if a pest-specific distance is applied. Here, we report the development of a GIS-based tool for efficient cultivation planning using the example of the pest complex pea moth (Cydia nigricana) and grain and green peas (Pisum sativum). Monitoring data for four consecutive years (2016-2019) from 513 sites were used. Infestation of pea seeds and the distance to the previous year's pea sites were recorded. An adjustable Python script was developed by means of infestation-distance-correlation as a pest and crop-specific minimum migration distance (MD). The output of the tool is a risk map as decision support for cultivation planning. It shows different risk buffers with distances from 1261 m to 1825 m, depending on the cultivation type. The web tool is easily adjustable to other pests and crops anywhere in the world. The tool helps to prevent damages caused by agricultural, mono-, or oligophagous insect pests and consequently reduces pesticide applications for the benefit of the environment and biodiversity.

Distribution and protection of Thesium chinense Turcz. under climate and land use change.

Wild medicinal plants are prominent in the field of Traditional Chinese Medicine (TCM), but their availability is being impacted by human activities and ecological degradation in China. To ensure sustainable use of these resources, it is crucial to scientifically plan areas for wild plant cultivation. Thesium chinense, a known plant antibiotic, has been overharvested in recent years, resulting in a sharp reduction in its wild resources. In this study, we employed three atmospheric circulation models and four socio-economic approaches (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) to investigate the primary environmental factors influencing the distribution of T. chinense. We also examined changes in its suitable area using the Biomod2 package. Additionally, we utilized the PLUS model to project and analyze future land use changes in climate-stable regions for T. chinense. Our planning for wild tending areas of T. chinense was facilitated by the ZONATION software. Over the next century, the climate-stable regions for T. chinense in China is approximately 383.05 × 104 km2, while the natural habitat in this region will progressively decline. Under the current climate conditions, about 65.06% of the habitats in the high suitable areas of T. chinense are not affected by future land use changes in China. Through hotspot analysis, we identified 17 hotspot cities as ideal areas for the wild tending of T. chinense, including 6 core hotspot cities, 6 sub-hotspot cities, and 5 fringe hotspot cities. These findings contribute to a comprehensive research framework for the cultivation planning of T. chinense and other medicinal plants.

Physical-Environmental Characterization of Areas for the Cultivation of Guaraná (Paullinia cupana) in Bahia, Brazil

Brazil is the world's leading producer of guarana and Bahia is the largest producing state, which suggests the need for cultivation planning. In this study, the physical-environmental factors of six municipalities in Lower Southern Bahia, Brazil were characterized to identify favorable areas for guarana cultivation. A database with the basic physical-environmental conditions was used to characterize and identify such areas. As described in the literature, the thresholds of conditioning factors for successful cultivation were selected using Structured Query Language (SQL) based on Geographic Information Systems (GIS), with logical operators and overlay tools. The factors considered in the analysis were soils (drainage and texture), climate (precipitation and temperature), and topography (slope). The results indicate the existence of favorable areas for guarana cultivation in Lower Southern Bahia, Brazil, which can significantly contribute to the planning and optimization of fruit cultivation in the region. A total of 23,489.7 hectares were considered favorable areas for guarana cultivation. This value is double the area allocated for cultivation in Brazil and more than four times the area allocated for harvesting in Bahia.

Integrating Agricultural and Ecotourism Development: A Crop Cultivation Suitability Framework Considering Tourists’ Landscape Preferences in Qinghai Province, China

Ecotourism and agricultural development have been proven to have synergistic effects, although few studies have employed a spatial planning approach to incorporate tourism growth into crop cultivation planning. This study constructed a theoretical framework of environmental suitability—farmland accessibility—tourist’s landscape preferences for crop cultivation planning to link regional agriculture and ecotourism development. The spatial planning of rapeseed cultivation in Qinghai Province was chosen as a case study. The main research methods include an environmental suitability analysis based on remote sensing and Maxent modeling, a farmland accessibility analysis based on a GIS platform, and a landscape preference questionnaire survey of tourists. According to the survey’s findings, almost 80% of tourists thought rapeseed flowers enhanced the beauty of natural landscapes. This demonstrated the enormous potential of rapeseed fields for fostering ecotourism. Based on environmental factors, the optimum region for rapeseed cultivation covered 5.38% of the study area, or roughly 6327 km2. The comprehensive optimum zone, which encompassed both agricultural accessibility and environmental suitability, was equal to 12.63% of the study area’s farming area, or around 929 km2. This study’s crop cultivation suitability framework can integrate agricultural and ecotourism development, with substantial implications for achieving coordinated economic, social, and environmental development.

RoseTracker: A system for automated rose growth monitoring

In cut-flower cultivation, production planning is an important task because demand fluctuates throughout the year. For precise cultivation planning, understanding the cultivation status is necessary by the growing stage. However, manually counting all the roses in the greenhouse to determine the cultivation status is difficult without incurring considerable time and labor. Some studies have engaged in detecting the number of flowers, but these studies used close-up images and could not count flowers without omissions or overlapping in an entire farm. In addition, limited datasets for object detection based on cut-flower blooming stages are available. In this study, we propose the RoseBlooming dataset and an efficient rose-monitoring system called RoseTracker to bridge the gap between computer vision techniques and the horticulture cultivation industry. The RoseBlooming dataset is the innovative dataset of labeled images for cut flowers at the growing stage. RoseTracker can detect small roses from various angles while moving the camera, reduces detection omissions, and achieves an F1 score of 0.950, thereby outperforming conventional models. For application, we used overhead images captured under actual growing conditions. RoseTracker and the RoseBlooming dataset contribute to constructing the rose-growth monitoring system in high demand worldwide.

Characteristics and risk assessment of chilling injury on Morchella esculenta in Western Sichuan Plateau, China.

The development of precise climate risk zoning for chilling injury of Morchella esculenta can provide scientific basis for agricultural cultivation planning, dynamic assessment of chilling injury, and disaster prevention strategies. Based on meteorological data from 17 counties (cities) that located below the altitude of 3000 m in the Western Sichuan Plateau from 2011 to 2020, we analyzed the critical meteorological conditions for M. esculenta disasters in typical years. With the average yearly cold accumulation and cold injury frequency during the first day when the temperature remained stable between 5 ℃ and 10 ℃ during mushroom emergence as zoning indicators, we established a geographical spatial distribution model of the cold injury index, and then divided the risk level of M. esculenta cold injury in the Western Sichuan Plateau, evaluated the risk of cold injury. The results showed that the temperature index for chilling injury risk of M. esculenta in the study area was the daily minimum temperature ≤2.0 ℃. The daily average temperature <6.0 ℃ would cause slow growth or the cessation of growth, which was set as a warning indicator for chilling injury risk. Along the Dadu River and Minjiang River basins, the frequency of chilling injury on M. esculenta increased from south to north. Wenchuan, Maoxian, and Lixian had the fewest overall chilling injuries during the study period, whereas Jiulong, Yajiang, and Batang had the most. The duration for cold injury was mainly 1-3 d, followed by 4-5 d, and rarely for >5 d. The frequency of chilling injury lasting for more than 5 d in Xiangcheng, Batang, Jiulong, Yajiang, and Xiaojin was more than that lasting for 4-5 d. The annual average days of chilling injury of was 3.0-27.4 d, the daily average minimum temperature was -0.84-1.36 ℃, the extreme lowest temperature was -5.8-0.1 ℃, and the average accumulated cold was 0.16-9.64 ℃·d during the period of chilling injury. With the increases of elevation and latitude, the average days of chilling injury and the average accumulated cold increased. The largest duration of chilling injury was 3-20 d, the maximum accumulated cold was 0.44-13.34 ℃·d. The risk of chilling injury to M. esculenta increased from south to north and from low elevation to high elevation. The suitable planting areas were distributed in strips and branches along the direction of mountains and rivers, mainly in the flat areas of low mountains and valleys below the altitude of 2200 m, including Kangding, Luding, Danba, Wenchuan, Lixian, Maoxian, Jiuzhaigou, and Songpan.

Crop cultivation planning with fuzzy estimation using water wave optimization.

In a complex agricultural region, determine the appropriate crop for each plot of land to maximize the expected total profit is the key problem in cultivation management. However, many factors such as cost, yield, and selling price are typically uncertain, which causes an exact programming method impractical. In this paper, we present a problem of crop cultivation planning, where the uncertain factors are estimated as fuzzy parameters. We adapt an efficient evolutionary algorithm, water wave optimization (WWO), to solve this problem, where each solution is evaluated based on three metrics including the expected, optimistic and pessimistic values, the combination of which enables the algorithm to search credible solutions under uncertain conditions. Test results on a set of agricultural regions in East China showed that the solutions of our fuzzy optimization approach obtained significantly higher profits than those of non-fuzzy optimization methods based on only the expected values.

Modeling and predicting the effects of climate change on cotton-suitable habitats in the Central Asian arid zone

Climate change has significantly affected global agricultural production, particularly in arid zones of Central Asia. Thus, we analyzed changes in the habitat suitability of cotton in Central Asia under various shared socioeconomic pathway (SSP) scenarios during 2021–2060. The results showed that the average minimum temperature in April, precipitation seasonality, and distance to rivers were the main environmental factors influencing the suitable distribution of cotton. Suitable habitats expanded toward the north and east, reaching a maximum net increase of 10.85 × 10 4 km 2 under the SSP5–8.5 scenario during 2041–2060, while habitats in the southwestern area showed a contracting trend. The maximum decreased and increased habitats were concentrated at approximately 68°E and 87°E, respectively. In addition, their latitudinal distributions were concentrated at approximately 40°N and 44°N. The longitudinal and latitudinal dividing lines of increased and decreased habitats were 69°E and 41°N, respectively. Habitats at the same altitude showed an increasing trend, excluding the elevation range of 125–325 m. Habitat shifts could exacerbate spatial conflicts with forest/grassland and natural reserves. The maximum spatial overlap between them was observed under the SSP5–8.5 scenario during 2041–2060. These findings could provide scientific evidence for rational cotton cultivation planning in global arid zones. • Cotton-suitable habitats expanded toward the north and east but not the southwest. • Longitudinal/latitudinal dividing lines of cotton loss/gain areas were 69°E/41°N. • Cotton habitats at the same altitude (excluding 125–135 m) showed a rising trend. • Cotton habitat shifts may worsen conflicts with forest/grassland/natural reserves.

Modelling of annual sunlight availability on vertical shelves: A case study in Thailand

Limited cultivation areas in major cities have led to the possibility of vertical farming (the practice of growing crops in vertically stacked layers). However, one of the drawbacks of vertical farming is the limited availability of solar energy at the lower shelves. This study presents a model for predicting the annual sunlight availability on vertical shelves. The model uses the shelf’s structure, orientation, hourly solar radiation, and sunshine duration as inputs to Rhinoceros 3D or RHINO (a 3D computer graphics and computer-aided design application software with the Grasshopper plug-in). The calculated solar energy available at each level of the shelves from RHINO was converted to photosynthetic photon flux density (PPFD) and daily light integral (DLI) using spreadsheet software. This study investigated a vertical farm, in Chiang Mai, Thailand, with six parallel shelves at 1-meter spacing. Each shelf contained three levels with a spacing of 0.5 meters. Both north–south and east–west orientations were investigated. The model could predict PPFD and DLI at every level on the considered shelves. The north–south orientation provided uniform PPFD and DLI throughout the year. The top level of the shelves experiences the highest PPFD (1,949.86μmol*m-2*s-1) and DLI (36.80 mol*m-2*day-1). The PPFD and DLI values at the middle and bottom level were approximately 60% and 50% of the values at the top level, respectively. This information can be used for cultivation planning when considering vertical farming in urban areas. This study provides a sustainable means for future food production.

Modeling the potential distribution of different types of Dendrocalamus sinicus, the strongest woody bamboo in the world, with MaxEnt model.

Climate change will significantly affect the distribution area of species. Through establishing distribution model, we can simulate the current and future potential distribution range and provide reference for the introduction and cultivation planning of rare or economic plants. Dendrocalamus sinicus, endemic to Yunnan Province of China, is the strongest woody bamboo in the world. In the present study, the MaxEnt model was performed to simulate the distribution of different types of D. sinicus in China and neighboring countries or regions. The results suggested that the suitable distribution range of “straight type”, the main type for cultivation and utilization, was 8°–30°N and 73°–122°E under the current climate conditions, while the potential distribution range of “bending type” was 6°–31°N and 79°–109°E. The two most key climate variables associated with distribution of “straight type” were “Temperature Annual Range” with 36.6% contribution rate and “Temperature Seasonality” (32.4%), while “Isothermality” (47.8%) and “Precipitation of Driest Month” (24.8%) for “bending type”. Under different climate change scenarios (SSP1-2.6, SSP5-8.5) and periods (2050, 2090), the potential distribution area of the “straight type” were apparently different, indicating that the distribution area of D. sinicus will be affected significantly by climate changes in the future. Our findings would be not only beneficial to understanding limiting factors for natural distribution of D. sinicus, but also helpful for further germplasm conservation, introduction and cultivation planning of this rare woody bamboo.

A parametric logistic equation with light flux and medium concentration for cultivation planning of microalgae.

Microalgae are considered to be promising producers of bioactive chemicals, feeds and fuels from carbon dioxide by photosynthesis. Thus, the prediction of microalgal growth profiles is important for the planning of cost-effective and sustainable cultivation–harvest cycles. This paper proposes a mathematical model capable of predicting the effect of light flux into culture and medium concentration on the growth profiles of microalgae by incorporating these growth-limiting factors into a logistic equation. The specific form of the equation is derived based on the experimentally measured growth profiles of Monoraphidium sp., a microalgal strain isolated by the authors, under 16 conditions consisting of combinations of incident light fluxes into culture and initial medium concentrations. Using a cross-validation method, it is shown that the proposed model has the ability to predict necessary incident light flux into culture and initial medium concentration for harvesting target biomass at a target time. Finally, model-guided cultivation planning is performed and is evaluated by comparing the result with experimental data.

Productivity enhancement using low-cost smart wireless programmable logic controllers: A case study of an oyster mushroom farm

• Smart system to enhance productivity for low-cost outdoor oyster mushroom farms. • Wireless Programmable Logic Controller (PLC) network is first reported in oyster mushroom farms. • Fuzzy logic is provided to the system for a better decision. • IoT weighting machine is proposed for decreasing mistakes made by farmers. This research work proposes the installation of a smart oyster mushroom farm combining sensory devices, a programmable logic controller (PLC), and wireless module. The smart wireless PLC encourages high-quality mushroom yield. The system is capable of wirelessly monitoring, controlling, and propagating information to a database. The smart wireless PLC adjusts the mushroom watering according to the temperature and humidity measurements and controls the ventilation by employing a cooling fan with fuzzy logic. The system uses mesh networks on the message queuing telemetry transport (MQTT) protocol to display real-time values and control mushroom cultures. Monitoring is executed via a web application on the PC, smartphone, and the HMI of the PLC. Measurement values are recorded on the database instantaneously for productivity prediction and cultivation planning. The mushroom cultivation controlled by the proposed system offers a higher yield than that of farm crops produced in the traditional way. Furthermore, the mushroom quality obtained is also better. In summary, the effectiveness of the smart wireless PLC enhances the quality and productivity of oyster mushroom cultivation.

Application of fuzzy goal programming model to assess optimal multi crop cultivation planning

Application of fuzzy goal programming model to assess optimal multi crop cultivation planning

Using a Self-Organizing Map to Explore Local Weather Features for Smart Urban Agriculture in Northern Taiwan

Weather plays a critical role in outdoor agricultural production; therefore, climate information can help farmers to arrange planting and production schedules, especially for urban agriculture (UA), providing fresh vegetables to partially fulfill city residents’ dietary needs. General weather information in the form of timely forecasts is insufficient to anticipate potential occurrences of weather types and features during the designated time windows for precise cultivation planning. In this research, we intended to use a self-organizing map (SOM), which is a clustering technique with powerful feature extraction ability to reveal hidden patterns of datasets, to explore the represented spatiotemporal weather features of Taipei city based on the observed data of six key weather factors that were collected at five weather stations in northern Taiwan during 2014 and 2018. The weather types and features of duration and distribution for Taipei on a 10-day basis were specifically examined, indicating that weather types #2, #4, and #7 featured to manifest the dominant seasonal patterns in a year. The results can serve as practical references to anticipate upcoming weather types/features within designated time frames, arrange potential/further measures of cultivation tasks and/or adjustments in response, and use water/energy resources efficiently for the sustainable production of smart urban agriculture.

Design of Smart Garden Based On The Internet of Things (IoT)

Currently, the control of cultivated plants is still manual, where farmers watering and checking soil moisture on the planting object. However, we cannot avoid technological progress in this life because technological progress will run following scientific advances. Therefore, every innovation is created to provide positive benefits for life. The ideal chili cultivation planning requires information about climatic conditions that play a significant role in chili cultivation is rainfall. Plant growth will be incredible if there is sufficient water supply; humidity for chili plants ranges from 60-80%. Therefore, chili cultivation planning must pay attention to rainfall because related to water availability. Smart Garden prototype design is a solution for taking appropriate actions during extreme weather and reducing human error. A prototype Smart Garden-based Internet of Things (IoT) using the Blynk app as User Interface (GUI) in the monitoring system and using the Soil Moisture sensor as input for soil moisture conditions, where the results of the process are sent via Wemos D1 mini-module to the Blynk server to display the state of soil moisture and automatic watering.

Progressive Web App for Crop Field Data Collection

General farming needs to have a matter of data collection. For efficient cultivation planning and harvesting, information must be collected for evaluation and analysis to make each harvest decision. For example, we need to assess plants’ ages, changes in size, color, and other senses (sweetness, smell, etc.) to plan for different crop field growth phases. Also, data collection problems occur due to the need to collect data in multiple locations simultaneously. Collecting information manually in the form of documents makes the process challenging to analyze. There is a need to collect field data more efficiently. Thus, this article proposes developing a web application to replace manual note-taking to reduce errors in recording and gathering data and missing data. Users can locate and modify crop field locations using latitude and longitude and work seamlessly with the Google Earth application. Furthermore, to accommodate practical use in real-life, the web application developed using Progressive Web Application (PWA) can perform without the Internet. Besides, PWA web applications are as fast as using an application on a smartphone without downloading and saving space. Based on the experimental result, compared with the process without using the application, it was found that the proposed application could reduce time in collecting sugarcane sampling data by about 45.28%

Plant factory crop scheduling considering volume, yield changes and multi-period harvests using Lagrangian relaxation

A plant factory is an environmentally controlled facility that can sustain stable crop cultivation while ensuring fast production and better crop quality by manipulating temperature, humidity, lighting, nutrient supply, and other cultivation factors. It requires better cultivation planning to fully utilise the facility since the set up and operating costs are high. This study aims to schedule crops in a commercial plant factory to maximise revenue by determining which crops are cultivated, the quantity, and at what time. The model considers not only crop market prices but also crop properties such as cultivation duration, volume change, multiple periods of harvests, and yield rates under different environmental settings. The problem is formulated as a mixed integer programming problem to find an optimal schedule. For a large size problem, Lagrangian relaxation with surrogate subgradient method is applied to obtain a good solution in a short time. The numerical results show that, compared to the integer program solver, the proposed method provides faster solutions with more than 80% efficacy when longer planning periods and multiple cultivation rooms are considered.

Energy sources analysis and optimization of energy efficiency and greenhouse gases in production of farm crops

ABSTRACT The present current study aimed at analyzing the input–output energies and optimizing energy efficiency and greenhouse gases (GHG) in crops production at the northeast of Iran. The results of this research showed that the total energy inputs were equal to 42.4, 28.6, and 72.8 GJha−1 for, wheat, barley, and sugar beet, respectively. It was also shown that the highest energy efficiency, the lowest GHG emissions, and GHG intensity all belonged to barley production. The highest percentage of GHG intensity pertained to sugar beet production (41%), which was followed by wheat (38%) and barley (21%). It was also revealed that GHG intensity is a better criterion for the study of GHG since it also makes use of the energy output information in crops. The optimal model responses for the crop type and proper cultivation planning was 284432 hectares in terms of barley production, while those responses for wheat and sugar beet were zero and, thereby, they were eliminated from the model of Multiple Goal Programming (MGP). In the same way, this model could increase energy efficiency to 444875, while GHG emissions and its intensity decreased by 213259 tonCO2eq and 1.3 tonCO2eq MJoutput−1 per year, respectively.

Climatic characterization and evaluation of the need for supplementary irrigation for cacao in southern Bahia, Brazil

Cacao is a typical plant of a tropical climate and demands ample water. Thus, in periods of water deficit, the need for supplementary irrigation can influence crop development. This study aimed to characterize the climate and evaluate the need for supplementary irrigation for cacao crops in the south of the state of Bahia (Brazil). For this research, historical data (2001-2017) for climate variables were obtained that are available at the weather station of the Executive Commission for Cacao Cultivation Planning (CEPLAC), located in southern Bahia. With the complete database, the monthly values for each variable were calculated and subjected to descriptive statistical analyses. Subsequently, the mean values for rainfall and air temperature were used to calculate potential evapotranspiration. The climatological water balance was then calculated, defining the periods of water deficit and surplus and whether supplementary irrigation was necessary or not, based on the total monthly water deficit. Overall, the region had a favorable climate for the development of cacao. However, this pattern did not continue throughout the historical series of climatic data, which showed temporal discontinuity, especially in February. The main months requiring supplementary irrigation for the cultivation of c hern Bahia were February and December.