Search results for: system of crop intensification

Authors: Mamta Rana, K. K. Singh, Nisha Kumari

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The simulation models with its soil-weather-plant atmosphere interacting system are important tools for assessing the crops in changing climate conditions. The CERES-Wheat & Rice vs. 4.6 DSSAT was calibrated and evaluated for one of the major producers of wheat and rice state- Haryana, India. The simulation runs were made under irrigated conditions and three fertilizer applications dose of N-P-K to estimate crop yield and other growth parameters along with the phenological development of the crop. The genetic coefficients derived by iteratively manipulating the relevant coefficients that characterize the phenological process of wheat and rice crop to the best fit match between the simulated and observed anthesis, physological maturity and final grain yield. The model validated by plotting the simulated and remote sensing derived LAI. LAI product from remote sensing provides the edge of spatial, timely and accurate assessment of crop. For validating the yield and yield components, the error percentage between the observed and simulated data was calculated. The analysis shows that the model can be used to simulate crop yield and yield components for wheat and rice cultivar under different management practices. During the validation, the error percentage was less than 10%, indicating the utility of the calibrated model for climate risk assessment in the selected region.

Keywords: simulation model, CERES-wheat and rice model, crop yield, genetic coefficient

Procedia PDF Downloads 276

Authors: Fuzhang Zhao

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Particle swarm optimization (PSO) is becoming one of the most important swarm intelligent paradigms for solving global optimization problems. Although some progress has been made to improve PSO algorithms over the last two decades, additional work is still needed to balance parameters to achieve better numerical properties of accuracy, efficiency, and stability. In the optimal PSO algorithm, the optimal weightings of (√ 5 − 1)/2 and (3 − √5)/2 are used for the cognitive factor and the social factor, respectively. By the same token, the same optimal weightings have been applied for intensification searches and diversification searches, respectively. Perturbation and constriction effects are optimally balanced. Simulations of the de Jong, the Rosenbrock, and the Griewank functions show that the optimal PSO algorithm indeed achieves better numerical properties and outperforms the canonical PSO algorithm.

Keywords: diversification search, intensification search, optimal weighting, particle swarm optimization

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Authors: Sabiha Shahid Antora, Young Ki Chang

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Along with various farm management technologies, imagery is an important tool that facilitates crop assessment, monitoring, and management. As a consequence, drone imaging technology is playing a vital role to capture the state of the entire field for yield mapping, crop scouting, weed detection, and so on. Although it is essential to inspect the cultivable lands in real-time for making rapid decisions regarding field variable inputs to combat stresses and diseases, drone imagery is still evolving in this area of interest. Cost margin and post-processing complexions of the image stream are the main challenges of imaging technology. Therefore, this proposed project involves the cost-effective field programmable gate array (FPGA) based image processing device that would process the image stream in real-time as well as providing the processed output to support on-the-spot decisions in the crop field. As a result, the real-time FPGA-based image processing system would reduce operating costs while minimizing a few intermediate steps to deliver scalable field decisions.

Keywords: real-time, FPGA, drone imagery, image processing, crop monitoring

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Authors: Aschalew C. Workneh, K. S. Hari Prasad, C. S. P. Ojha

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The crop water stress index (CWSI) is a cost-effective, non-destructive, and simple technique for tracking the start of crop water stress. This study investigated the feasibility of CWSI derived from canopy temperature to detect the water status of wheat crops. Artificial intelligence (AI) techniques have become increasingly popular in recent years for determining CWSI. In this study, the performance of two AI techniques, adaptive neuro-fuzzy inference system (ANFIS) and self-organizing maps (SOM), are compared while determining the CWSI of paddy crops. Field experiments were conducted for varying irrigation water applications during two seasons in 2022 and 2023 at the irrigation field laboratory at the Civil Engineering Department, Indian Institute of Technology Roorkee, India. The ANFIS and SOM-simulated CWSI values were compared with the experimentally calculated CWSI (EP-CWSI). Multiple regression analysis was used to determine the upper and lower CWSI baselines. The upper CWSI baseline was found to be a function of crop height and wind speed, while the lower CWSI baseline was a function of crop height, air vapor pressure deficit, and wind speed. The performance of ANFIS and SOM were compared based on mean absolute error (MAE), mean bias error (MBE), root mean squared error (RMSE), index of agreement (d), Nash-Sutcliffe efficiency (NSE), and coefficient of correlation (R²). Both models successfully estimated the CWSI of the paddy crop with higher correlation coefficients and lower statistical errors. However, the ANFIS (R²=0.81, NSE=0.73, d=0.94, RMSE=0.04, MAE= 0.00-1.76 and MBE=-2.13-1.32) outperformed the SOM model (R²=0.77, NSE=0.68, d=0.90, RMSE=0.05, MAE= 0.00-2.13 and MBE=-2.29-1.45). Overall, the results suggest that ANFIS is a reliable tool for accurately determining CWSI in wheat crops compared to SOM.

Keywords: adaptive neuro-fuzzy inference system, canopy temperature, crop water stress index, self-organizing map, wheat

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Authors: M. Yusuf

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The recent discovery of the phenomenon of RNA interference has led to its application in various aspects of plant improvement. Crops can be modified by engineering novel RNA interference pathways that create small RNA molecules to alter gene expression in crops or plant pests. RNA interference can generate new crop quality traits or provide protection against insects, nematodes and pathogens without introducing new proteins into food and feed products. This is an advantage in contrast with conventional procedures of gene transfer. RNA interference has been used to develop crop varieties resistant to diseases, pathogens and insects. Male sterility has been engineered in plants using RNA interference. Better quality crops have been developed through the application of RNA interference etc. The objective of this paper is to highlight the application of RNA interference in crop improvement and to project its potential future use to solve problems of agricultural production in relation to plant breeding.

Keywords: RNA interference, application, crop Improvement, agricultural production

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Authors: M. Johnson, R. Faggian, V. Sposito

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A participatory and engaged approach is key in connecting agricultural managers to sustainable agricultural systems to support and optimize production in Victoria’s food bowl. A sustainable intensification (SI) approach is well documented globally, but participation rates amongst Victorian farmers is fragmentary, and key outcomes and implementation strategies are poorly understood. Improvement in decision-support management tools and a greater understanding of the productivity gains available upon implementation of SI is necessary. This paper reviews the current understanding and uptake of SI practices amongst farmers in one of Victoria’s premier food producing regions, the Goulburn Broken; and it spatially analyses the potential for this region to adapt to climate change and optimize food production. A Geographical Information Systems (GIS) approach is taken to develop an interactive decision-support tool that can be accessible to on-ground agricultural managers. The tool encompasses multiple criteria analysis (MCA) that identifies factors during the construction phase of the tool, using expert witnesses and regional knowledge, framed within an Analytical Hierarchy Process. Given the complexities of the interrelations between each of the key outcomes, this participatory approach, in which local realities and factors inform the key outcomes and help to strategies for a particular region, results in a robust strategy for sustainably intensifying production in key food producing regions. The creation of an interactive, locally embedded, decision-support management and education tool can help to close the gap between farmer knowledge and production, increase on-farm adoption of sustainable farming strategies and techniques, and optimize farm productivity.

Keywords: agriculture, decision-support management tool, Geographic Information System, GIS, sustainable intensification

Procedia PDF Downloads 135

Authors: Emmanuel Olugbenga Fakoya

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The study examined the perceived impact of climate change on the livelihood of arable crop farmers in Ipokia Local Government Area of Ogun State, Nigeria. Multistage sampling technique was used to select 80 arable crop farmers in the study area. Data collected were analyzed using percentages, frequencies and Chi square analysis. The result showed that 63.8 percent of the respondents were male while 55.0 percent were married. Less than half (30.0 percent) of the respondents were between the age bracket of 41-50 years and 50.0 percent had 6-10 household size. Furthermore, majority (40.0 percent) of the arable crop farmers farmed on an inherited land and 51.3 percent had 2-3 hectares of land. Majority (38.8 percent) of the farmers intercrop maize with cassava and maize with yam. Various strategies adapted to reduce the effect of climate change on their crop and livelihood include: crop rotation (53.8 percent), planting of leguminous crop (35.0 percent), application of organic fertilizers (45.0 percent), mulching (56.3 percent) and by planting drought resistance crops (46.5 percent). Reported among the effects of climate change on crop and farmers’ livelihood were: discoloration of crop leave (63.8 percent), increase infestation of pests and diseases (58.8 percent) and reduction of crop yield (60.0 percent). Chi- square analysis showed significant relationship between impact of climate change on arable crop production and thus famers’ livelihood. It was concluded from the study that climate change is an impinging factor that seriously affect arable crop production and hence farmers’ livelihood despite coping strategies to minimize its effect. It was however recommended that Agricultural policies and practices that could minimize or eliminate its effect should be seriously enacted to boost production and increase farmers’ livelihood.

Keywords: agricultural extension, extension agent, private sector, perception

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Authors: Mohammadali Abedini Sanigy, Jiangang Fei

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In this paper, a mathematical optimization model was developed to maximize the profit in a vegetable production planning problem. It serves as a decision support system that assists farmers in land allocation to crops and harvest scheduling decisions. The developed model can handle different rotation rules in two consecutive cycles of production, which is a common practice in organic production system. Moreover, different production methods of the same crop were considered in the model formulation. The main strength of the model is that it is not restricted to predetermined production periods, which makes the planning more flexible. The model is classified as a mixed integer programming (MIP) model and formulated in PYOMO -a Python package to formulate optimization models- and solved via Gurobi and CPLEX optimizer packages. The model was tested with secondary data from 'Australian vegetable growing farms', and the results were obtained and discussed with the computational test runs. The results show that the model can successfully provide reliable solutions for real size problems.

Keywords: crop rotation, harvesting, mathematical model formulation, vegetable production

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Authors: Manpreet Kaur, Cheol-Hong Min

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The increasing advancement in the robotics equipped with machine vision sensors applied to precision agriculture is a demanding solution for various problems in the agricultural farms. An important issue related with the machine vision system concerns crop row and weed detection. This paper proposes an automatic furrow detection system based on real-time processing for identifying crop rows in maize fields in the presence of weed. This vision system is designed to be installed on the farming vehicles, that is, submitted to gyros, vibration and other undesired movements. The images are captured under image perspective, being affected by above undesired effects. The goal is to identify crop rows for vehicle navigation which includes weed removal, where weeds are identified as plants outside the crop rows. The images quality is affected by different lighting conditions and gaps along the crop rows due to lack of germination and wrong plantation. The proposed image processing method consists of four different processes. First, image segmentation based on HSV (Hue, Saturation, Value) decision tree. The proposed algorithm used HSV color space to discriminate crops, weeds and soil. The region of interest is defined by filtering each of the HSV channels between maximum and minimum threshold values. Then the noises in the images were eliminated by the means of hybrid median filter. Further, mathematical morphological processes, i.e., erosion to remove smaller objects followed by dilation to gradually enlarge the boundaries of regions of foreground pixels was applied. It enhances the image contrast. To accurately detect the position of crop rows, the region of interest is defined by creating a binary mask. The edge detection and Hough transform were applied to detect lines represented in polar coordinates and furrow directions as accumulations on the angle axis in the Hough space. The experimental results show that the method is effective.

Keywords: furrow detection, morphological, HSV, Hough transform

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Authors: M. Saleem, M. Shahnawaz, A. W. Gandahi, S. M. Bhatti

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The micronutrients boron and zinc deficiencies are extensive in the areas of rice-wheat cropping system. Optimum levels of these nutrients in soil are necessary for healthy crop growth. Since rice and wheat are major staple food of worlds’ populace, the higher yields and nutrition status of these crops has direct effect on the health of human being and economy of the country. A field study was conducted to observe the direct and residual effect of two selected micronutrients boron (B) and zinc (Zn)) on rice and wheat crop growth and its grain nutrient status. Each plot received either B or Zn at the rates of 0, 1, 2, 3 and 4 kg B ha⁻¹, and 5, 10, 15 and 20 kg Zn ha⁻¹, combined B and Zn application at 1 kg B and 5 kg Zn ha⁻¹, 2 kg B and 10 kg Zn ha⁻¹. Colemanite ore were used as source of B and zinc sulfate for Zn. The second season wheat crop was planted in the same plots after the interval period of 30 days and during this time gap soil was fallow. Boron and Zn application significantly enhanced the plant height, number of tillers, Grains panicle⁻¹ seed index fewer empty grains panicle⁻¹ and yield of rice crop at all defined levels as compared to control. The highest yield (10.00 tons/ha) was recorded at 2 Kg B, 10 Kg Zn ha⁻¹ rates. Boron and Zn concentration in grain and straw significantly increased. The application of B also improved the nutrition status of rice as B, protein and total carbohydrates content of grain augmented. The analysis of soil samples collected after harvest of rice crop showed that the B and Zn content in post-harvest soil samples was high in colemanite and zinc sulfate applied plots. The residual B and Zn were also effectual for the second season wheat crop, as the growth parameters plant height, number of tillers, earhead length, weight 1000 grains, B and Zn content of grain significantly improved. The highest wheat grain yield (4.23 tons/ha) was recorded at the residual rates of 2 kg B and 10 kg Zn ha⁻¹ than the other treatments. This study showed that one application of B and Zn can increase crop yields for at least two consecutive seasons and the mineral colemanite can confidently be used as source of B for rice crop because very small quantities of these nutrients are consumed by first season crop and remaining amount was present in soil which were used by second season wheat crop for healthy growth. Consequently, there is no need to apply these micronutrients to the following crop when it is applied on the previous one.

Keywords: residual boron, zinc, rice, wheat

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Authors: V. S. Manivasagam, R. Nagarajan

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Modeling the soil, water and crop growth interactions are attaining major importance, considering the future climate change and water availability for agriculture to meet the growing food demand. Progress in understanding the crop growth response during water stress period through crop modeling approach provides an opportunity for improving and sustaining the future agriculture water use efficiency. An attempt has been made to evaluate the potential use of crop modeling approach for assessing the minimal supplementary irrigation requirement for crop growth during water limited condition and its practical significance in sustainable improvement of crop yield and water productivity. Among the numerous crop models, water driven-AquaCrop model has been chosen for the present study considering the modeling approach and water stress impact on yield simulation. The study has been evaluated in rainfed maize grown area of semi-arid Shanmuganadi watershed (a tributary of the Cauvery river system) located in southern India during the rabi cropping season (October-February). In addition to actual rainfed maize growth simulation, irrigated maize scenarios were simulated for assessing the supplementary irrigation requirement during water shortage condition for the period 2012-2015. The simulation results for rainfed maize have shown that the average maize yield of 0.5-2 t ha-1 was observed during deficit monsoon season (<350 mm) whereas 5.3 t ha-1 was noticed during sufficient monsoonal period (>350 mm). Scenario results for irrigated maize simulation during deficit monsoonal period has revealed that 150-200 mm of supplementary irrigation has ensured the 5.8 t ha-1 of irrigated maize yield. Thus, study results clearly portrayed that minimal application of supplementary irrigation during the critical growth period along with the deficit rainfall has increased the crop water productivity from 1.07 to 2.59 kg m-3 for major soil types. Overall, AquaCrop is found to be very effective for the sustainable irrigation assessment considering the model simplicity and minimal inputs requirement.

Keywords: AquaCrop, crop modeling, rainfed maize, water stress

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Authors: Kamran Baksh Soomro, Amin Talei, Sina Alaghmand

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Water scarcity has exacerbated in the last couple of decades; it is incumbent on agriculture to maximize the use of water of all qualities. The drip irrigation system practice has shown a vast increase in profit and research interests in the last two decades. However, the application of this system is still limited. The two years field experiment was conducted with three replications at Malir, Karachi (a semi-arid region) in Pakistan. The aim was to evaluate the effects of two qualities of irrigation water IT1 (EC 0.56 dS.m⁻¹) and IT2 (EC 2.89 dS.m⁻¹) on water use efficiency. To achieve the aim, bitter gourd was grown under the drip irrigation system in 2016-17. The uniformity co-efficient (UC) ranged from 93 to 96%. Water use efficiency, of 1.60 and 1.21 kg.m⁻³ under IT1 was recorded higher in season 1 and 2. Using t-test at 5% significance level, the crop yield was higher in both seasons under IT1 compared to IT2. Using pairwise t-test at 5% significance level, the parameters related with the quality of fruit, like length, weight, and diameter, were higher in IT1 than IT2 in all plants; and in both seasons. A correlational study was also conducted to observe the trends in the variables associated with both irrigation treatments for the two seasons. Results showed that most of the parameters exhibited a similar linear trend in both the seasons. The study concluded that bitter gourd crop could be grown successfully in sandy loam using drip irrigation system, supplying saline ground-water. The sustainable use of saline irrigation water should be utilized for vegetable cultivation to meet the food demand in the rural areas of Pakistan.

Keywords: uniformity co-efficient, water use efficiency, drip irrigation, ground-water, t-test, correlation

Procedia PDF Downloads 118

Authors: Piyush Sharma, Devi Prasad Sharma, Sulabh Bansal

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Diseases are fairly common in fruits and vegetables because of the changing climatic and environmental circumstances. Crop diseases, which are frequently difficult to control, interfere with the growth and output of the crops. Accurate disease detection and timely disease control measures are required to guarantee high production standards and good quality. In India, apples are a common crop that may be afflicted by a variety of diseases on the fruit, stem, and leaves. It is fungi, bacteria, and viruses that trigger the early symptoms of leaf diseases. In order to assist farmers and take the appropriate action, it is important to develop an automated system that can be used to detect the type of illnesses. Machine learning-based image processing can be used to: this research suggested a system that can automatically identify diseases in apple fruit and apple plants. Hence, this research utilizes the hybrid SVM-RBN model. As a consequence, the model may produce results that are more effective in terms of accuracy, precision, recall, and F1 Score, with respective values of 96%, 99%, 94%, and 93%.

Keywords: fruit plant disease, crop disease, machine learning, image processing, SVM-RBN

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Authors: Rajeshwari Madli, Santhosh Hebbar, Vishwanath Heddoori, G. V. Prasad

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Agriculture is the major occupation in India and forms the backbone of Indian economy in which irrigation plays a crucial role for increasing the quality and quantity of crop yield. In spite of many revolutionary advancements in agriculture, there has not been a dramatic increase in agricultural performance. Lack of irrigation infrastructure and agricultural knowledge are the critical factors influencing agricultural performance. However, by using advanced agricultural equipment, the effect of these factors can be curtailed.  The presented system aims at increasing the yield of crops by using an intelligent irrigation controller that makes use of wireless sensors. Sensors are used to monitor primary parameters such as soil moisture, soil pH, temperature and humidity. Irrigation decisions are taken based on the sensed data and the type of crop being grown. The system provides a mobile application in which farmers can remotely monitor and control the irrigation system. Also, the water pump is protected against damages due to voltage variations and dry running.

Keywords: android application, Bluetooth, wireless sensors, irrigation, temperature, soil pH

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Authors: Fahad Suleiman

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Agriculture has been the mainstay of the nation’s economy in Nigeria. It provides food for the ever rapidly increasing population and raw materials for the industries. People especially the rural dwellers are gainfully employed on their crop farms and small-scale livestock farms for income earning. In farming, availability of funds and time management are one of the major factors that influence the system of farming in Nigeria in which mathematical science knowledge was highly required in order for farms to be managed effectively. Farmers often applied mathematics, almost every day for a variety of tasks, ranging from measuring and weighing, to land marking. This paper, therefore, explores some of the ways math is used in farming. For instance, farmers use arithmetic variety of farm activities such as seed planting, harvesting crop, cultivation and mulching. It is also important in helping farmers to know how much their livestock weighs, how much milk their cows produce and crop yield per acres, among others.

Keywords: agriculture, application, economic, farming, mathematics

Procedia PDF Downloads 216

Authors: Iqra Yaseen

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One of the well-known areas of computer science and engineering, image processing in the context of computer vision has been essential to automation. In remote sensing, medical science, and many other fields, it has made it easier to uncover previously undiscovered facts. Grading of diverse items is now possible because of neural network algorithms, categorization, and digital image processing. Its use in the classification of agricultural products, particularly in the grading of seeds or grains and their cultivars, is widely recognized. A grading and sorting system enables the preservation of time, consistency, and uniformity. Global population growth has led to an increase in demand for food staples, biofuel, and other agricultural products. To meet this demand, available resources must be used and managed more effectively. Image processing is rapidly growing in the field of agriculture. Many applications have been developed using this approach for crop identification and classification, land and disease detection and for measuring other parameters of crop. Vegetation localization is the base of performing these task. Vegetation helps to identify the area where the crop is present. The productivity of the agriculture industry can be increased via image processing that is based upon Unmanned Aerial Vehicle photography and satellite. In this paper we use the machine learning techniques like Convolutional Neural Network, deep learning, image processing, classification, You Only Live Once to UAV imaging dataset to divide the crop into distinct groups and choose the best way to use it.

Keywords: image processing, UAV, YOLO, CNN, deep learning, classification

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Authors: Raj Chavan

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Currently, there are over 14,500 Water Resource Recovery Facilities (WRRFs) in the United States, with ~35% of them having some type of nutrient limits in place. These WRRFs account for about 1% of overall power demand and 2% of total greenhouse gas emissions (GHG) in the United States and contribute for 10 to 15% of the overall nutrient load to surface rivers in the United States. The evolution of densification technologies toward more compact and energy-efficient nutrient removal processes has been impacted by a number of factors. Existing facilities that require capacity expansion or biomass densification for higher treatability within the same footprint are being subjected to more stringent requirements relating to nutrient removal prior to surface water discharge. Densification of activated sludge has received recent widespread interest as a means for achieving process intensification and nutrient removal at WRRFs. At the core of the technology are the aerobic sludge granules where the biological processes occur. There is considerable interest in the prospect of producing granular sludge in continuous (or traditional) activated sludge processes (CAS) or densification of biomass by moving activated sludge flocs to a denser aggregate of biomass as a highly effective technique of intensification. This presentation will provide a fundamental understanding of densification by presenting insights and practical issues. The topics that will be discussed include methods used to generate and retain densified granules; the mechanisms that allow biological flocs to densify; the role that physical selectors play in the densification of biological flocs; some viable ways for managing biological flocs that have become densified; effects of physical selection design parameters on the retention of densified biological flocs and finally some operational solutions for customizing the flocs and granules required to meet performance and capacity targets. In addition, it will present some case studies where biological and physical parameters were used to generate aerobic granular sludge in the continuous flow system.

Keywords: densification, aerobic granular sludge, nutrient removal, intensification

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Authors: Narayan Prasad Adhikari

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In Nepal, the crop residues have often been considered as one of the potential sources of energy to cope with prevailing energy crisis. However, the lack of systematic studies about production and various other competent uses of crop production is the main obstacle to evaluate net potential of the residues for energy production. Under this background, this study aims to assess the net annual availability of crop residues for energy production by undertaking three different districts with the representation of country’s three major regions of lowland, hill, and mountain. The five major cereal crops of paddy, wheat, maize, millet, and barley are considered for the analysis. The analysis is based upon two modes of household surveys. The first mode of survey is conducted to total of 240 households to obtain key information about crop harvesting and livestock management throughout a year. Similarly, the quantification of main crops along with the respective residues on fixed land is carried out to 45 households during second mode. The range of area of such fixed land is varied from 50 to 100 m2. The measurements have been done in air dry basis. The quantity for competitive uses of respective crop residues is measured on the basis of respondents’ feedback. There are four major competitive uses of crop residues at household which are building material, burning, selling, and livestock fodder. The results reveal that the net annual available crop residues per household are 4663 kg, 2513 kg, and 1731 kg in lowland, hill, and mountain respectively. Of total production of crop residues, the shares of dedicated fodder crop residues (except maize stalk and maize cob) are 94 %, 62 %, and 89 % in lowland, hill, and mountain respectively and of which the corresponding shares of fodder are 87 %, 91 %, and 82 %. The annual percapita energy equivalent from net available crop residues in lowland, hill, and mountain are 2.49 GJ, 3.42 GJ, and 0.44 GJ which represent 30 %, 33 %, and 3 % of total annual energy consumption respectively whereas the corresponding current shares of crop residues are only 23 %, 8 %, and 1 %. Hence, even utmost exploitation of available crop residues can hardly contribute to one third of energy consumption at household level in lowland, and hill whereas this is limited to particularly negligible in mountain. Moreover, further analysis has also been done to evaluate district wise supply-demand context of dedicated fodder crop residues on the basis of presence of livestock. The high deficit of fodder crop residues in hill and mountain is observed where the issue of energy generation from these residues will be ludicrous. As a contrary, the annual production of such residues for livestock fodder in lowland meets annual demand with modest surplus even if entire fodder to be derived from the residues throughout a year and thus there seems to be further potential to utilize the surplus residues for energy generation.

Keywords: crop residues, hill, lowland, mountain

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Authors: Miroslav Dumbrovsky

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The paper deals with positive impacts of land consolidation on decreasing soil degradation with the main emphasis on soil and water conservation in the landscape. The importance of land degradation is very high because of its impact on crop productivity and many other adverse effects. Soil degradation through soil erosion is causing losses in crop productivity and quality of the environment, through decreasing quality of soil and water (especially water resources). Negative effects of conventional farming practices are increased water erosion, as well as crusting and compaction of the topsoil and subsoil. Soil erosion caused by water destructs the soil’s structure, reduces crop productivity due to deterioration in soil physical and chemical properties such as infiltration rate, water holding capacity, loss of nutrients needed for crop production, and loss of soil carbon. Recently, a new process of complex land consolidation in the Czech Republic has provided a unique opportunity for improving the quality of the environment and sustainability of the crop production by means a better soil and water conservation. The present process of the complex land consolidation is not only a reallocation of plots, but this system consists of a new layout of plots within a certain territory, aimed at establishing the integrated land-use economic units, based on the needs of individual landowners and land users. On the other hand, the interests of the general public and the environmental protection have to be solved, too. From the general point of view, a large part of the Czech landscape shall be reconstructed in the course of complex land consolidation projects. These projects will be based on new integrated soil-economic units, spatially arranged in a designed multifunctional system of soil and water conservation measures, such as path network and a territorial system of ecological stability, according to structural changes in agriculture. This new approach will be the basis of a rational economic utilization of the region which will comply with the present ecological and aesthetic demands at present.

Keywords: soil degradation, land consolidation, soil erosion, soil conservation

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Authors: Luleka Dlamini, Olivier Crespo, Jos van Dam

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Accurately estimating crop growth and yield is pivotal for timely sustainable agricultural management and ensuring food security. Crop models and remote sensing can complement each other and form a robust analysis tool to improve crop growth and yield estimations when combined. This study thus aims to systematically evaluate how research that exclusively focuses on assimilating RS data into crop models varies among countries, crops, data assimilation methods, and farming conditions. A strict search string was applied in the Scopus and Web of Science databases, and 497 potential publications were obtained. After screening for relevance with predefined inclusion/exclusion criteria, 123 publications were considered in the final review. Results indicate that over 81% of the studies were conducted in countries associated with high socio-economic and technological advancement, mainly China, the United States of America, France, Germany, and Italy. Many of these studies integrated MODIS or Landsat data into WOFOST to improve crop growth and yield estimation of staple crops at the field and regional scales. Most studies use recalibration or updating methods alongside various algorithms to assimilate remotely sensed leaf area index into crop models. However, these methods cannot account for the uncertainties in remote sensing observations and the crop model itself. l. Over 85% of the studies were based on commercial and irrigated farming systems. Despite a great global interest in data assimilation into crop models, limited research has been conducted in resource- and data-limited regions like Africa. We foresee a great potential for such application in those conditions. Hence facilitating and expanding the use of such an approach, from which developing farming communities could benefit.

Keywords: crop models, remote sensing, data assimilation, crop yield estimation

Procedia PDF Downloads 91

Authors: Luleka Dlamini, Olivier Crespo, Jos van Dam

Abstract:

Accurately estimating crop growth and yield is pivotal for timely sustainable agricultural management and ensuring food security. Crop models and remote sensing can complement each other and form a robust analysis tool to improve crop growth and yield estimations when combined. This study thus aims to systematically evaluate how research that exclusively focuses on assimilating RS data into crop models varies among countries, crops, data assimilation methods, and farming conditions. A strict search string was applied in the Scopus and Web of Science databases, and 497 potential publications were obtained. After screening for relevance with predefined inclusion/exclusion criteria, 123 publications were considered in the final review. Results indicate that over 81% of the studies were conducted in countries associated with high socio-economic and technological advancement, mainly China, the United States of America, France, Germany, and Italy. Many of these studies integrated MODIS or Landsat data into WOFOST to improve crop growth and yield estimation of staple crops at the field and regional scales. Most studies use recalibration or updating methods alongside various algorithms to assimilate remotely sensed leaf area index into crop models. However, these methods cannot account for the uncertainties in remote sensing observations and the crop model itself. l. Over 85% of the studies were based on commercial and irrigated farming systems. Despite a great global interest in data assimilation into crop models, limited research has been conducted in resource- and data-limited regions like Africa. We foresee a great potential for such application in those conditions. Hence facilitating and expanding the use of such an approach, from which developing farming communities could benefit.

Keywords: crop models, remote sensing, data assimilation, crop yield estimation

Procedia PDF Downloads 54

Authors: A. L. Qureshi, A. A. Mahessar, R. K. Dashti, S. M. Yasin

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In this paper, effect of marginal quality groundwater on yield of cotton crop and soil salinity was studied. In this connection, three irrigation treatments each with four replications were applied. These treatments were use of canal water, use of marginal quality groundwater from tube well, and conjunctive use by mixing with the ratio of 1:1 of canal water and marginal quality tubewell water. Water was applied to the crop cultivated in Kharif season 2011; its quantity has been measured using cut-throat flume. Total 11 watering each of 50 mm depth have been applied from 20th April to 20th July, 2011. Further, irrigations were stopped from last week of July, 2011 due to monsoon rainfall. Maximum crop yield (seed cotton) was observed under T1 which was 1,516.8 kg/ha followed by T3 (mixed canal and tube well water) having 1009 kg/ha and 709 kg/ha for T2 i.e. marginal quality groundwater. This concludes that crop yield in T2 and T3 with in comparison to T1was reduced by about 53 and 30% respectively. It has been observed that yield of cotton crop is below potential limit for three treatments due to unexpected rainfall at the time of full flowering season; thus the yield was adversely affected. However, salt deposition in soil profiles was not observed that is due to leaching effect of heavy rainfall occurred during monsoon season.

Keywords: conjunctive use, cotton crop, groundwater, soil salinity status, water use efficiency

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Authors: Nicolae Bold, Daniel Nijloveanu

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The idea of cropping-system is a method used by farmers. It is an environmentally-friendly method, protecting the natural resources (soil, water, air, nutritive substances) and increase the production at the same time, taking into account some crop particularities. The combination of this powerful method with the concepts of genetic algorithms results into a possibility of generating sequences of crops in order to form a rotation. The usage of this type of algorithms has been efficient in solving problems related to optimization and their polynomial complexity allows them to be used at solving more difficult and various problems. In our case, the optimization consists in finding the most profitable rotation of cultures. One of the expected results is to optimize the usage of the resources, in order to minimize the costs and maximize the profit. In order to achieve these goals, a genetic algorithm was designed. This algorithm ensures the finding of several optimized solutions of cropping-systems possibilities which have the highest profit and, thus, which minimize the costs. The algorithm uses genetic-based methods (mutation, crossover) and structures (genes, chromosomes). A cropping-system possibility will be considered a chromosome and a crop within the rotation is a gene within a chromosome. Results about the efficiency of this method will be presented in a special section. The implementation of this method would bring benefits into the activity of the farmers by giving them hints and helping them to use the resources efficiently.

Keywords: chromosomes, cropping, genetic algorithm, genes

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Authors: Nathenal Thomas Lambamo

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Based on the fact that more than 85% of the labor force and 90% of the export earnings are taken by agriculture in Ethiopia and it can be said that it is the backbone of the overall socio-economic activities in the country. Among the cereal crops that the agriculture sector provides for the country, wheat is the third-ranking one preceding teff and maize. In the present day, wheat is in higher demand related to the expansion of industries that use them as the main ingredient for their products. The local supply of wheat for these companies covers only 35 to 40% and the rest 60 to 65% percent is imported on behalf of potential customers that exhaust the country’s foreign currency reserves. The above facts show that the need for this crop in the country is too high and in reverse, the productivity of the crop is very less because of these reasons. Wheat disease is the most devastating disease that contributes a lot to this unbalance in the demand and supply status of the crop. It reduces both the yield and quality of the crop by 27% on average and up to 37% when it is severe. This study aims to detect the most frequent and degrading wheat diseases, Septoria and Leaf rust, using the most efficiently used subset of machine learning technology, deep learning. As a state of the art, a deep learning class classification technique called Convolutional Neural Network (CNN) has been used to detect diseases and has an accuracy of 99.01% is achieved.

Keywords: septoria, leaf rust, deep learning, CNN

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Authors: Dereje Regasa

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The global food system is at a crossroads, which requires prompt action to minimize the effects of the crises. Agroecology is gaining prominence due to its contributions to sustainable food systems. To support efforts in mitigating the crises, the Food and Agriculture Organization (FAO) established alternative approaches for sustainable agri-food systems. Agroecological elements and principles were developed to guide and support measures that countries need to achieve the Sustainable Development Goals (SDGs). The SDGs require the systemic integration of practices for a smart intensification or adaptation of traditional or industrial agriculture. As one of the countries working towards SDGs, the agricultural practices in Ethiopia need to be guided by these agroecological elements and principles. Aiming at the identification of challenging aspects of a sustainable agri-food system and the characterization of an enabling environment for agroecology, as well as exploring to what extent the existing policies and strategies support the agroecological transition process, five policy and strategy documents were reviewed. These documents are the Rural Development Policy and Strategy, the Environment Policy, the Biodiversity Policy, and the Soil Strategy of the Ministry of Agriculture (MoA). Using the Agroecology Criteria Tool (ACT), the contents were reviewed, focusing on agroecological requirements and the inclusion of sustainable practices. ACT is designed to support a self-assessment of elements supporting agroecology. For each element, binary values were assigned based on the inclusion of the minimum requirements index and then validated through discussion with the document owners. The results showed that the documents were well below the requirements for an agroecological transition of the agri-food system. The Rural Development Policy and Strategy only suffice to 83% in Human and Social Value. It does not support the transition concerning the other elements. The Biodiversity Policy and Soil Strategy suffice regarding the inclusion of Co-creation and Sharing of knowledge (100%), while the remaining elements were not considered sufficiently. In contrast, the Environment Policy supports the transition with three elements accounting for 100%. These are Resilience, Recycling, and Human and Social Care. However, when the four documents were combined, elements such as Synergies, Diversity, Efficiency, Human and Social value, Responsible governance, and Co-creation and Sharing of knowledge were identified as fully supportive (100%). This showed that the policies and strategies complemented one another to a certain extent. However, the evaluation results call for improvements concerning elements like Culture and food traditions, Circular and solidarity economy, Resilience, Recycling, and Regulation and balance since the majority of the elements were not sufficiently observed. Consequently, guidance for the smart intensification of local practices is needed, as well as traditional knowledge enriched with advanced technologies. Ethiopian agricultural and environmental policies and strategies should provide sufficient support and guidance for the intensification of sustainable practices and should provide a framework for an agroecological transition towards a sustainable agri-food system.

Keywords: agroecology, diversity, recycling, sustainable food system, transition

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Authors: Hoda Zolfagharnejad, Behnam Kamkar, Omid Abdi

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Crop coefficient (Kc) is an important factor contributing to estimation of evapotranspiration, and is also used to determine the irrigation schedule. This study investigated and determined the monthly Kc of winter wheat (Triticum aestivum L.) using five vegetation indices (VIs): Normalized Difference Vegetation Index (NDVI), Difference Vegetation Index (DVI), Soil Adjusted Vegetation Index (SAVI), Infrared Percentage Vegetation Index (IPVI), and Ratio Vegetation Index (RVI) of four basins in Golestan province, Iran. 14 Landsat-8 images according to crop growth stage were used to estimate monthly Kc of wheat. VIs were calculated based on infrared and near infrared bands of Landsat 8 images using Geographical Information System (GIS) software. The best VIs were chosen after establishing a regression relationship among these VIs with FAO Kc and Kc that was modified for the study area by the previous research based on R² and Root Mean Square Error (RMSE). The result showed that local modified SAVI with R²= 0.767 and RMSE= 0.174 was the best index to produce monthly wheat Kc maps.

Keywords: crop coefficient, remote sensing, vegetation indices, wheat

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Authors: Abdellatif Chebboub

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Agricultural production is sensitive to weather and thus directly affected by climate change. Plausible estimates of these climate change impacts require combined use of climate, crop, and economic models. Results from previous studies vary substantially due to differences in models, scenarios, and data. This paper is part of a collective effort to systematically integrate these three types of models. We focus on the economic component of the assessment, investigating how nine global economic models of agriculture represent endogenous responses to seven standardized climate change scenarios produced by two climate and five crop models. These responses include adjustments in yields, area, consumption, and international trade. We apply biophysical shocks derived from the Intergovernmental Panel on Climate Change’s representative concentration pathway with end-of-century radiative forcing of 8.5 W/m2. The mean biophysical yield effect with no incremental CO2 fertilization is a 17% reduction globally by 2050 relative to a scenario with unchanging climate. Endogenous economic responses reduce yield loss to 11%, increase area of major crops by 11%, and reduce consumption by 3%. Agricultural production, cropland area, trade, and prices show the greatest degree of variability in response to climate change, and consumption the lowest. The sources of these differences include model structure and specification; in particular, model assumptions about ease of land use conversion, intensification, and trade. This study identifies where models disagree on the relative responses to climate shocks and highlights research activities needed to improve the representation of agricultural adaptation responses to climate change.

Keywords: climate change, agriculture, weather change, danger of climate change

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Authors: Ezzat Abd El Lateef

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Agroforestry means combining the management of trees with productive agricultural activities, especially in semiarid regions where crop yield increases are limited in agroforestry systems due to the fertility and microclimate improvements and the large competitive effect of trees with crops for water and nutrients, in order to assess the effect of agroforestry of some field crops with citrus trees as an approach to establish biodiversity in fruit tree plantations. Three field crops, i.e., maize, soybean and sunflower, were inter-planted with seedless orange trees (4*4 m) or were planted as solid plantings. The results for the trees indicated a larger fruit yield was obtained when soybean and sunflowers were interplant with citrus. Statistically significant effects (P<0.05) were found for maize grain and biological yields, with increased yields when grown as solid planting. There were no differences in the yields of soya bean and sunflower, where the yields were very similar between the two cropping systems. It is evident from the trials that agroforestry is an efficient concept to increase biodiversity through the interaction of trees with the interplant field crop species. Maize, unlike the other crops, was more sensitive to shade conditions under agroforestry practice and not preferred in the biodiversity system. The potential of agroforestry to improve or increase biodiversity is efficient as the understorey crops are usually C4 species, and the overstorey trees are invariably C3 species in agroforestry. Improvement in interplant species is most likely if the understorey crop is a C3 species, which are usually light saturated in the open, and partial shade may have little effect on assimilation or by a concurrent reduction in transpiration. It could be concluded that agroforestry is an efficient concept to increase biodiversity through the interaction of trees with the interplant field crop species. Some field crops could be employed successfully, like soybean or sunflowers, while others like maize are sensitive to incorporate in agroforestry system.

Keywords: agroforestry, field crops, C3 and C4 plants, yield

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Authors: Mirza Waseem Abbas, Abdul Qayyum, Muhammad Islam

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Climate change has emerged as a significant threat to global food security, affecting crop production systems worldwide. This research paper aims to examine the specific impacts of climate change on wheat production in Pakistan, Punjab in particular, a country highly dependent on wheat as a staple food crop. Through a comprehensive review of scientific literature, field observations, and data analysis, this study assesses the key climatic factors influencing wheat cultivation and the subsequent implications for food security in the region. A comparison of two subsequent Wheat seasons in Punjab was examined through climatic conditions, area, yield, and production data. From the analysis, it is observed that despite a decrease in the area under cultivation in the Punjab during the Wheat 2023 season, the production and average yield increased due to favorable weather conditions. These uncertain climatic conditions have a direct impact on crop yields. Last year due to heat waves, Wheat crop in Punjab suffered a significant loss. Through crop insurance, Wheat growers were provided with yield loss protection keeping in view the devastating heat wave and floods last year. Under crop insurance by the Government of the Punjab, 534,587 Wheat growers were insured with a $1.6 million premium subsidy. However, due to better climatic conditions, no loss in the yield was recorded in the insured areas. Crop Insurance is one of the suitable options for policymakers to protect farmers against climatic losses in the future as well.

Keywords: climate change, crop insurance, heatwave, wheat yield punjab

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Authors: Md Yeasin, Ranjit Kumar Paul

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In recent years, recommendation systems have become indispensable tools for agricultural system. The accurate and timely recommendations can significantly impact crop yield and overall productivity. Causal inference modeling aims to establish cause-and-effect relationships by identifying the impact of variables or factors on outcomes, enabling more accurate and reliable recommendations. New advancements in causal inference models have been found in the literature. With the advent of the modern era, deep learning and machine learning models have emerged as efficient tools for modeling. This study proposed an innovative approach to enhance recommendation systems-based machine learning based casual inference model. By considering the causal effect and opportunity cost of covariates, the proposed system can provide more reliable and actionable recommendations for cereal farmers. To validate the effectiveness of the proposed approach, experiments are conducted using cereal cultivation data of eastern India. Comparative evaluations are performed against existing correlation-based recommendation systems, demonstrating the superiority of the advanced causal inference modeling approach in terms of recommendation accuracy and impact on crop yield. Overall, it empowers farmers with personalized recommendations tailored to their specific circumstances, leading to optimized decision-making and increased crop productivity.

Keywords: agriculture, casual inference, machine learning, recommendation system

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