Search results for: crop evapotranspiration

Authors: Azar Khodabakhshi, Elham Bolandnazar

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Agriculture as the most important food manufacturing sector is not only the energy consumer, but also is known as energy supplier. Using energy is considered as a helpful parameter for analyzing and evaluating the agricultural sustainability. In this study, the pattern of energy consumption of strawberry greenhouses of Jiroft in Kerman province of Iran was surveyed. The total input energy required in the strawberries production was calculated as 113314.71 MJ /ha. Electricity with 38.34% contribution of the total energy was considered as the most energy consumer in strawberry production. In this study, Neuro Fuzzy networks was used for function modeling in the production of strawberries. Results showed that the best model for predicting the strawberries function had a correlation coefficient, root mean square error (RMSE) and mean absolute percentage error (MAPE) equal to 0.9849, 0.0154 kg/ha and 0.11% respectively. Regards to these results, it can be said that Neuro Fuzzy method can be well predicted and modeled the strawberry crop function.

Keywords: crop yield, energy, neuro-fuzzy method, strawberry

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Authors: Suprehatin Suprehatin

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Improving the participation of smallholder farmers in horticultural value chains to benefit from the rapidly growing demand for high-value agricultural products is one strategy for raising farm income. However, smallholder farmer participation in Indonesian horticultural value chains is under-researched. To address this knowledge gap, this study aims to describe the current status of horticultural crop adoption in Indonesia and analyze the motivations and dynamics of smallholder farmer participation in horticultural value chains: why some small farmers join these new and potentially profitable chains and continue their participation. This study also examines the characteristics of farmers who adopted and those who did not adopt a new horticultural crop with respect to the household (farmer), farm and institutional characteristics. The analysis was conducted using unique data from a 2013 survey of 960 Indonesian farmers on Java Island that produce a variety of agricultural products. Basic statistical analysis showed relatively low adoption rates (10%) of new horticultural crops amongst 960 selected Indonesian farmers with different decisions made in terms of number and timing of new horticultural crop adoption. Adopters were motivated mainly by higher profit, higher yield, and more cash opportunities. The result also showed that current low rates of horticultural crop adoption are associated with a variety of factors, such as lower levels of education among farmers, resource constraints, lack of information on horticultural crop production and low participation in farmer groups. These findings will be helpful for policymakers when designing policies and programs to promote greater participation of Indonesian smallholder farmers in horticultural value chains. In other words, a revitalisation of agricultural policy beyond staple food is important to seize potential benefits from the ongoing agricultural food market transformation.

Keywords: farmer adoption, high value, horticultural crops, Indonesia

Procedia PDF Downloads 253

Authors: G. Disciglio, G. Gatta, F. Lops, A. Libutti, A. Tarantino, E. Tarantino

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The experimental trial was carried out in open field at Foggia district (Apulia Region, Southern Italy), during the spring-summer season 2014, in order to evaluate the effect of four biostimulant products (RadiconÒ, Viormon plusÒ, LysodinÒ and SiaptonÒ 10L), compared with a control (no biostimulant), on the infestation of processing tomato crop (cv Dres) by the chlorophyll-lacking root parasite Phelipanche ramosa. Biostimulants consist in different categories of products (microbial inoculants, humic and fulvic acids, hydrolyzed proteins and aminoacids, seaweed extracts) which play various roles in plant growing, including the improvement of crop resistance and quali-quantitative characteristics of yield. The experimental trial was arranged according to a complete randomized block design with five treatments, each of one replicated three times. The processing tomato seedlings were transplanted on 5 May 2014. Throughout the crop cycle, P. ramosa infestation was assessed according to the number of emerged shoots (branched plants) counted in each plot, at 66, 78 and 92 day after transplanting. The tomato fruits were harvested at full-stage of maturity on 8 August 2014. From each plot, the marketable yield was measured and the quali-quantitative yield parameters (mean weight, dry matter content, colour coordinate, colour index and soluble solids content of the fruits) were determined. The whole dataset was tested according to the basic assumptions for the analysis of variance (ANOVA) and the differences between the means were determined using Tukey’s tests at the 5% probability level. The results of the study showed that none of the applied biostimulants provided a whole control of Phelipanche, although some positive effects were obtained from their application. To this respect, the RadiconÒ appeared to be the most effective in reducing the infestation of this root-parasite in tomato crop. This treatment also gave the higher tomato yield.

Keywords: biostimulant, control methods, Phelipanche ramosa, tomato crop

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Authors: Valdir Moura, Raniele dos Anjos de Souza, Fernando Gomes de Souza, Jose Vagner da Silva, Jerry Adriani Johann

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The estimate of the Area of Land to be planted with annual crops and its stratification by the municipality are important variables in crop forecast. Nowadays in Brazil, these information’s are obtained by the Brazilian Institute of Geography and Statistics (IBGE) and published under the report Assessment of the Agricultural Production. Due to the high cloud cover in the main crop growing season (October to March) it is difficult to acquire good orbital images. Thus, one alternative is to work with remote sensing data from dates before the crop growing season. This work presents the use of multitemporal Landsat data gathered on July and September (before the summer growing season) in order to estimate the area of land to be planted with summer crops in an area of São Paulo State, Brazil. Geographic Information Systems (GIS) and digital image processing techniques were applied for the treatment of the available data. Supervised and non-supervised classifications were used for data in digital number and reflectance formats and the multitemporal Normalized Difference Vegetation Index (NDVI) images. The objective was to discriminate the tracts with higher probability to become planted with summer crops. Classification accuracies were evaluated using a sampling system developed basically for this study region. The estimated areas were corrected using the error matrix derived from these evaluations. The classification techniques presented an excellent level according to the kappa index. The proportion of crops stratified by municipalities was derived by a field work during the crop growing season. These proportion coefficients were applied onto the area of land to be planted with summer crops (derived from Landsat data). Thus, it was possible to derive the area of each summer crop by the municipality. The discrepancies between official statistics and our results were attributed to the sampling and the stratification procedures. Nevertheless, this methodology can be improved in order to provide good crop area estimates using remote sensing data, despite the cloud cover during the growing season.

Keywords: area intended for summer culture, estimated area planted, agriculture, Landsat, planting schedule

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Authors: Kedibone Masenya, Memory Tekere, Jasper Rees

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Sorghum is an important cereal crop in the world. In particular, it has attracted breeders due to capacity to serve as food, feed, fiber and bioenergy crop. Like any other plant, sorghum hosts a variety of microbes, which can either, have a neutral, negative and positive influence on the plant. In the current study, regions (V3/V4) of 16 S rRNA were targeted to extensively assess bacterial multitrophic interactions in the phyllosphere of sorghum. The results demonstrated that the presence of a pathogen has a significant effect on the endophytic bacterial community. Understanding these interactions is key to develop new strategies for plant protection.

Keywords: bacteria, multitrophic, sorghum, target sequencing

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Authors: Manaranjan Pradhan, Shailaja Grover, U. Dinesh Kumar

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Farming community in India, as well as other parts of the world, is one of the highly stressed communities due to reasons such as increasing input costs (cost of seeds, fertilizers, pesticide), droughts, reduced revenue leading to farmer suicides. Lack of integrated farm advisory system in India adds to the farmers problems. Farmers need right information during the early stages of crop’s lifecycle to prevent damage and loss in revenue. In this paper, we use deep learning techniques to develop an early warning system for detection of crop diseases using images taken by farmers using their smart phone. The research work leads to building a smart assistant using analytics and big data which could help the farmers with early diagnosis of the crop diseases and corrective actions. The classical approach for crop disease management has been to identify diseases at crop level. Recently, ImageNet Classification using the convolutional neural network (CNN) has been successfully used to identify diseases at individual plant level. Our model uses convolution filters, max pooling, dense layers and dropouts (to avoid overfitting). The models are built for binary classification (healthy or not healthy) and multi class classification (identifying which disease). Transfer learning is used to modify the weights of parameters learnt through ImageNet dataset and apply them on crop diseases, which reduces number of epochs to learn. One shot learning is used to learn from very few images, while data augmentation techniques are used to improve accuracy with images taken from farms by using techniques such as rotation, zoom, shift and blurred images. Models built using combination of these techniques are more robust for deploying in the real world. Our model is validated using tomato crop. In India, tomato is affected by 10 different diseases. Our model achieves an accuracy of more than 95% in correctly classifying the diseases. The main contribution of our research is to create a personal assistant for farmers for managing plant disease, although the model was validated using tomato crop, it can be easily extended to other crops. The advancement of technology in computing and availability of large data has made possible the success of deep learning applications in computer vision, natural language processing, image recognition, etc. With these robust models and huge smartphone penetration, feasibility of implementation of these models is high resulting in timely advise to the farmers and thus increasing the farmers' income and reducing the input costs.

Keywords: analytics in agriculture, CNN, crop disease detection, data augmentation, image recognition, one shot learning, transfer learning

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Authors: Ali Bolat, Ali Bayat, Mustafa Gullu

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Maize can be cultivated both under main and second crop conditions in Turkey. Main pests of maize under second crop conditions are Sesamia nonagrioides Lefebvre (Lepidoptera: Noctuidae) and Ostrinia nubilalis Hübner (Lepidoptera: Crambidae). Aerial spraying applications to control these two main maize pests can be carried out until 2006 in Turkey before it was banned due to environmental concerns like drifting of sprayed pestisides and low biological efficiency. In this context, pulverizers which can spray tall maize plants ( > 175 cm) from the ground have begun to be used. However, the biological efficiency of these sprayers is unknown. Some methods have been tested to increase the success of ground spraying in field experiments conducted in second crop maize in 2008 and 2009. For this aim, 6 spraying methods (air assisted spraying with TX cone jet, domestic cone nozzles, twinjet nozzles, air induction nozzles, standard domestic cone nozzles and tail booms) were used at two application rates (150 and 300 l.ha-1) by a sprayer. In the study, biological efficacy evaluations of each methods were measured in each parcel. Biological efficacy evaluations included counts of number of insect damaged plants, number of holes in stems and live larvae and pupa in stems of selected plants. As a result, the highest biological efficacy value (close to 70%) was obtained from Air Assisted Spraying method at 300 l / ha application volume.

Keywords: air assisted sprayer, drift nozzles, biological efficiency, maize plant

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Authors: Vasileios A. Giouvanis, Christos D. Papanikolaou, Dimitrios S. Dimakas, Maria A. Sakellariou-Makrantonaki

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In countries with limited water resources, where the irrigation demands are higher than the 70% of the total water use, the demand for fresh water increases while the quality of this natural resource is downgraded. The aromatic and pharmaceutical plants hold a high position in the culture of the most civilizations through the centuries. The ‘Mountain Tea,’ species of the Greek flora, is part of a series of aromatic plants and herbs that are famous for their pharmaceutical properties as well as their byproducts and their essential oils. The aim of this research was to study the effects of full and deficit irrigation on the growing and productive characteristics of organically cultivated ‘Mountain Tea’ (Sideritis raeseri). The research took place at the University of Thessaly farm in Velestino, Magnesia - Central Greece, during the year 2017, which was the third growing season. The experiment consisted of three treatments in three replications. The experimental design was a fully randomized complete block. Surface drip irrigation was used to irrigate the experimental plots. In the first treatment, the 75% (deficit irrigation) of the daily water needs was applied. In the second treatment, the 100% (full irrigation) of the daily water needs was applied. The third treatment was not irrigated (rainfed). The crop water needs were calculated according to the daily measured evapotranspiration (ETc) using the Penman-Monteith method (FAO 56). The plants’ height, fresh and dry biomass production were measured. The results showed that only the irrigated ‘Mountain Tea’ can be cultivated at low altitude areas with satisfactory results. Moreover, there are no statistically significant differences (P < 0.05) at the growing and productive characteristics between full and deficit irrigation treatments, which proves that by deficit irrigation, an important amount of irrigation water can be saved.

Keywords: mountain tea, surface drip irrigation, deficit irrigation, water saving

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Authors: Pascal François Faye, Baye Mor Sall, Bineta Dembele, Jeanne Ana Awa Faye

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In agriculture, estimating crop yields is key to improving productivity and decision-making processes such as financial market forecasting and addressing food security issues. The main objective of this paper is to have tools to predict and improve the accuracy of crop yield forecasts using machine learning (ML) algorithms such as CART , KNN and SVM . We developed a mobile app and a web app that uses these algorithms for practical use by farmers. The tests show that our system (collection and deployment architecture, web application and mobile application) is operational and validates empirical knowledge on agro-climatic parameters in addition to proactive decision-making support. The experimental results obtained on the agricultural data, the performance of the ML algorithms are compared using cross-validation in order to identify the most effective ones following the agricultural data. The proposed applications demonstrate that the proposed approach is effective in predicting crop yields and provides timely and accurate responses to farmers for decision support.

Keywords: prediction, machine learning, artificial intelligence, digital agriculture

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Authors: Rabin Paudel, Dambar Bahadur Mahato, Prabin Poudel, Bijaya Neupane, Sakar Jha

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Understanding Human-Elephant Conflict (HEC) is very important in countries like Nepal, where solutions to escalating conflicts are urgently required. However, most of the HEC mitigation measures implemented so far have been done on an ad hoc basis without the detailed understanding of nature and extent of the damage. This study aims to assess the current scenario of HEC in regards to crop and property damages by Wild Asian Elephant and people’s perception towards existing mitigating measures and elephant conservation in Buffer zone area of Bardia National Park. The methods used were a questionnaire survey (N= 178), key-informant interview (N= 18) and focal group discussions (N= 6). Descriptive statistics were used to determine the nature and extent of damage and to understand people’s perception towards HEC, its mitigation measures and elephant conservation. Chi-square test was applied to determine the significance of crop and property damages with respect to distance from the park boundary. Out of all types of damage, crop damage was found to be the highest (51%), followed by house damage (31%) and damage to stored grains (18%) with winter being the season with the greatest elephant damage. Among 178 respondents, the majority of them (82%) were positive towards elephant conservation despite the increment in HEC incidents as perceived by 88% of total respondents. Among the mitigation measures present, the most applied was electric fence (91%) followed by barbed wire fence (5%), reinforced concrete cement wall (3%) and gabion wall (1%). Most effective mitigation measures were reinforced concrete cement wall and gabion wall. To combat increasing crop damage, the insurance policy should be initiated. The efficiency of the mitigation measures should be timely monitored, and corrective measures should be applied as per the need.

Keywords: crop and property damage, elephant conflict, Asiatic wild elephant, mitigation measures

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Authors: Xuewen Chen, Aizhen Liang, Xiaoping Zhang

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To better understand the spatial variability of soil penetration resistance (SPR) and soil water content (SWC) induced by different tillage and crop rotation in a Mollisol of Northeast China, the soil was sampled from the tillage experiment which was established in Dehui County, Jilin Province, Northeast China, in 2001. Effect of no-tillage (NT), moldboard plow (MP) and ridge tillage (RT) under corn-soybean rotation (C-S) and continuous corn (C-C) system on SPR and SWC were compared with horizontal and vertical variations. The results showed that SPR and SWC spatially varied across the ridge. SPR in the rows was higher than inter-rows, especially in topsoil (2.5-15 cm) of NT and RT plots. SPR of MP changed in the trend with the curve-shaped ridge. In contrast to MP, NT, and RT resulted in average increment of 166.3% and 152.3% at a depth of 2.5-17.5 cm in the row positions, respectively. The mean SPR in topsoil in the rows means soil compaction is not the main factor limiting plant growth and crop yield. SPR in the row of RT soil was lower than NT at a depth of 2.5-12.5 cm. The SWC in NT and RT soil was highest in the inter-rows and least in the rows or shoulders, respectively. However, the lateral variation trend of MP was opposite to NT. From the profile view of SWC, MP was greater than NT and RT in 0-20 cm of the rows. SWC in RT soil was higher than NT in the row of 0-20 cm. Crop rotation did not have a marked impact on SPR and SWC. In addition to the tillage practices, the factor which affects SPR greatly was depth but not position. These two factors have significant effects on SWC. These results indicated that the adoption of RT was a more suitable conservation tillage practices than NT in the black soil of Northeast China.

Keywords: row, soil penetration resistance, spatial variability, tillage practice

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Authors: Meysam Abedinpour

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Water limitation is the main challenge for crop production in a semi-arid environment. Deficit irrigation is a strategy that allows a crop to sustain some degree of water deficit in order to reduce costs and potentially increase income. For this goal, a field experimental carried out at Asrieh fields of Gorgan city in the north of Iran, during summer season 2011. The treatments imposed were different irrigation water regimes (i.e. W1:70, W2:80, W3:90, and W4:100) percent of field capacity (FC). The results showed that there was Significant difference between the yield and (WUE) under different levels of irrigation, excepting of soil moisture content at field capacity (W4) and 90% of field capacity (W3) on yield and water use efficiency (WUE). The seasonal irrigation water applied were (i.e. 375, 338, 300, and 263 mm ha-1) under different irrigation water treatments (100, 90, 80, 80 and 70%) of FC, respectively. Grain yield productions under treatments were 4180, 3955, 3640, and 3355 (kg ha-1) respectively. Furthermore, the results showed that water use efficiency (WUE) at different treatments were 7.67, 7.79, 7.74, and 7.75 Kg mm ha-1 for (100, 90, 80, and 70) per cent of field capacity, therefore the 90 % of FC treatment (W3) is recommended for Soybean irrigation for water saving. Furthermore, the result showed that the treatment of 90 % of filed capacity (W3) seemed to be better adapted to product a high crop yield with acceptable yield coupling with water use efficiency in Golestan province.

Keywords: deficit irrigation, water use efficiency, yield, soybean

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Authors: Saeed Yazdani, Shahrbanoo Bagheri, Sepideh Nikravesh

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Considering the importance and scarcity of water resources, the efficient management of water resources is of great importance. Adoption of modern irrigation technology is considered to be a key of increasing the efficiency of water used in agriculture. Policy makers have implemented several ways to induce the adoption of new irrigation technology. The empirical studies show that farmers are reluctant to utilize the use of new irrigation methods. This study aims to assess factors affecting on farmer’s decision on the application of water saving technologies with emphasize on crop price variation and water sources. A Logit model was employed to examine the impact of different variables on use of water saving technology. The required data gathered from a sample of 204 farmers in the year 2012. The results indicate that different variables such as crop price variability, water supply source, high-value crops, farm size, income, education, membership in cooperatives have a positive effect and variables such as age and number of plots have a negative impact on the probability of adopting modern water saving technologies.

Keywords: irrigation, water, water saving technology, scarcity

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Authors: Muhammad Zafarullah Khan, Sumeera Abbasi

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The Farmers Field School (FFS) aims at benefiting poor farmers by improving their knowledge of existing agricultural technologies and integrated crop management to become independent and confident in their decision. The study on effect of farmer’s field school on vegetables production before and after FFS implementation in district Peshawar in four selected villages on each crop in 2011 was conducted from 80 farmers. The results were compared by using paired t-test. It was observed that 80% of the respondents were satisfied with FFS approach as there was a significant increase in vegetable production. The seed rate of tomato and cucumber decreased from 0.185kg/kanal to 0.1 kg/ kanal and 0.120kg/kanal to 0.01kg/kanal while production of tomato and cucumber were increased from 8158.75kgs/kanal to 1030.25kgs/kanal and 3230kgs/kanal to 5340kgs/kanal, respectively after the activities of FFS. FFS brought a positive effect on vegetable production and technology adoption improving their income, skills and knowledge ultimately lead farmers towards empowerment. The input cost including seed, crop management, FYM, and weedicides for tomato were reduced by Rs.28, Rs. 3170 and Rs.658 and cucumber reduced by Rs.35, Rs.570 and Rs.430. Only fertilizers cost was increased by Rs. 2200 in case of tomato and 465 in case of cucumber. FFS facilitator and coordinator should be more skilled and practical oriented to facilitate poor farmers. In light of the above study, more FFS should be planned so that the more farmers should be benefited.

Keywords: effect, farmer field school, vegetables production, integrated crop management

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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: Vaclav Novak, Katerina Krizova, Petr Sarec

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Modern agriculture has to face many issues from which soil degradation and lack of organic matter in the soil are only a few of them. Apart from Climate Change, human utilization of landscape is the cause of a majority part of these problems. Cattle production in Czechia has been reduced by more than half in recent 30 years. However, cattle manure is considered as staple organic fertilizer, and its role in attempts for sustainable agriculture is irreplaceable. This study aims to describe the impact of so-called activators of biological manure transformation (Z´fix, Olmix Group) mainly on physical soil properties but also on crop status. The experiment has been established in 2017; nevertheless, initial measurements of implement draft have been performed before the treated manure application. In 2018, the physical soil properties and crop status (sugar beet) has been determined and compared with the untreated manure and control variant. Significant results have been observed already in the first year, where the implement draft decreased by 9.2 % within the treated manure variant in comparison with the control variant.

Keywords: field experiment, implement draft, vegetation index, sugar beet

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Authors: Vhahangwele Belemu, Isaac Busayo Oluwatayo

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Post-harvest losses pose a serious challenge to smallholder fruit crop farmers, especially in the rural communities of South Africa, affecting their economic livelihoods and food security. This study investigated the post-harvest handling practices and technologies harnessed by smallholder fruit crop farmers in the Vhembe district of Limpopo province, South Africa. Data were collected on a random sample of 224 smallholder fruit crop farmers selected from the four municipalities of the district using a multistage sampling technique. Analytical tools employed include descriptive statistics and the tobit regression model. A descriptive analysis of farmers’ socioeconomic characteristics showed that a sizeable number of these farmers are still in their active working age (mean = 52 years) with more males (63.8%) than their female (36.2%) counterparts. Respondents’ distribution by educational status revealed that only a few of these had no formal education (2.2%), with the majority having secondary education (48.7%). Results of data analysis further revealed that the prominent post-harvest technologies and handling practices harnessed by these farmers include using appropriate harvesting techniques (20.5%), selling at a reduced price (19.6%), transportation consideration (18.3%), cleaning and disinfecting (17.9%), sorting and grading (16.5%), manual cleaning (15.6%) and packaging technique (11.6%) among others. The result of the Tobit regression analysis conducted to examine the determinants of post-harvest technologies and handling practices harnessed showed that age, educational status of respondents, awareness of technology/handling practices, farm size, access to credit, extension contact, and membership of association were the significant factors. The study suggests enhanced awareness creation, access to credit facility and improved access to market as important factors to consider by relevant stakeholders to assist smallholder fruit crop farmers in the study area.

Keywords: fruit crop farmers, handling practices, post harvest losses, smallholder, Vhembe District, South Africa

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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: Marilyn S. Painagan, Willie Jones B. Saliling

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This paper reports an optimized cropping calendar and land suitability for maize in North Cotabato derived from modeling crop productivity over time and space. Using Quantum GIS, eight representative soil types and 0.3o x 0.3o climate grids shapefiles were intersected to form thirty two pedoclimatic zones within the boundaries of the province. Surveys were done to ascertain crop performance and phenological properties on field. Based on these surveys, crop parameters were calibrated specific for a variety of maize. Soil properties and climatic data (daily precipitation, maximum and minimum temperatures) from pedoclimatic zones were loaded to the FAO Aquacrop Water Productivity Model along with the crop properties from field surveys to simulate yield from 1980 to 2010. The average yield per month was computed to come up with the month of planting having the highest and lowest probable yield in a year assuming that all lands were planted with maize. The yield attributes were visualized in the Quantum GIS environment. The study revealed that optimal cropping patterns varied across North Cotabato. Highest probable yield (8000 kg/ha) can be obtained when maize is planted on May and September (sandy clay-loam soils) in the northern part of the province while the lowest probable yield (1000 kg/ha) can be obtained when maize is planted on January, February and March (clay loam soils) at the northern part of the province. Yields are simulated on the basis of varieties currently planted by farmers of North Cotabato. The resulting maps suggest where and when maize is most suitable to achieve high yields. There is a need to ground truth and validate the cropping calendar on field.

Keywords: aquacrop, quantum GIS, maize, cropping calendar, water productivity

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Authors: Brian Marsh

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Nitrogen fertilizer is the most used and often the most mismanaged nutrient input. Nitrogen management has tremendous implications on crop productivity, quality and environmental stewardship. Sufficient nitrogen is needed to optimum yield and quality. Soil and in-season plant tissue testing for nitrogen status are a time consuming and expensive process. Real time sensing of plant nitrogen status can be a useful tool in managing nitrogen inputs. The objectives of this project were to assess the reliability of remotely sensed non-destructive plant nitrogen measurements compared to wet chemistry data from sampled plant tissue, develop in-season nitrogen recommendations based on remotely sensed data for improved nitrogen use efficiency and assess the potential for determining yield and quality from remotely sensed data. Very good correlations were observed between early-season remotely sensed crop nitrogen status and plant nitrogen concentrations and subsequent in-season fertilizer recommendations. The transmittance/absorbance type meters gave the most accurate readings. Early in-season fertilizer recommendation would be to apply 40 kg nitrogen per hectare plus 16 kg nitrogen per hectare for each unit difference measured with the SPAD meter between the crop and reference area or 25 kg plus 13 kg per hectare for each unit difference measured with the CCM 200. Once the crop was sufficiently fertilized meter readings became inconclusive and were of no benefit for determining nitrogen status, silage yield and quality and grain yield and protein.

Keywords: wheat, nitrogen fertilization, chlorophyll meter

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Authors: S. K. Gali

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Spentwash, An effluent of distillery is an environmental pollutant because of its high load of pollutants (pH: 2-4; BOD>40,000 mg/l, COD>100,000mg/l and TDS >70,000mg/l). But However, after subjecting it to primary treatment (bio-methanation), Its pollutant load gets drastically reduced (pH: 7.5-8.5, BOD<10,000 mg/l) and could be disposed off safely as a source of organic matter and plant nutrients for crop production. With the consent of State Pollution Control Board, the distilleries in Karnataka are taking up ‘one time controlled land application’ of bio-methanated spentwash in farmers’ fields. A monitoring study was undertaken in Belgaum district of Karnataka State with an objective of studying the effect of land application of bio-methanated spent wash of a distillery on soil properties and crop growth. The treated spentwash was applied uniformly to the fallow dry lands in different farmers’ fields during summer, 2012 at recommended rate (based on nitrogen requirement of crops). The application was made at least a fortnight before sowing/planting operations. The analysis of soils collected before land application of spentwash and after harvest of crops revealed that there was no adverse effect of applied spentwash on soil characteristics. A slight build up in soluble salts was observed but, however all the soils recorded EC of less than 2.0 dSm-1. An increase in soil organic carbon (SOC) and available nitrogen (N) by about 10 to 30 % was observed in the spentwash applied soils. The presence of good amount of biodegradable organics in the treated spentwash (BOD of 6550 mg/l) contributed for increase in SOC and N. A substantial build up in available potassium (K) status (50 to 200%) was observed due to spentwash application. This was attributed to the high K content in spentwash (6950 mg/l). The growth of crops in the spentwash applied fields was higher and farmers could get nearly 10 to 20 per cent higher yields, especially in sugarcane and corn. The analysis of ground water samples showed that the quality of water was not affected due to land application of treated spentwash. Apart from realizing higher crop yields, the farmers were able to save money on N and K fertilisers as the applied spentwash met the crop requirement. Hence, it could be concluded that the bio-methanated distillery spentwash can be gainfully utilized in crop production without polluting the environment.

Keywords: bio-methanation, pollutant, potassium status, soil organic carbon

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Authors: Zama Mahmood

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The increasing demand of food management, monitoring of the crop growth and forecasting its yield well before harvest is very important. These days, yield assessment together with monitoring of crop development and its growth are being identified with the help of satellite and remote sensing images. Studies using remote sensing data along with field survey validation reported high correlation between vegetation indices and yield. With the development of remote sensing technique, the detection of crop and its mechanism using remote sensing data on regional or global scales have become popular topics in remote sensing applications. Punjab, specially the southern Punjab region is extremely favourable for wheat production. But measuring the exact amount of wheat production is a tedious job for the farmers and workers using traditional ground based measurements. However, remote sensing can provide the most real time information. In this study, using the Normalized Differentiate Vegetation Index (NDVI) indicator developed from Landsat satellite images, the yield of wheat has been estimated during the season of 2013-2014 for the agricultural area around Bahawalpur. The average yield of the wheat was found 35 kg/acre by analysing field survey data. The field survey data is in fair agreement with the NDVI values extracted from Landsat images. A correlation between wheat production (ton) and number of wheat pixels has also been calculated which is in proportional pattern with each other. Also a strong correlation between the NDVI and wheat area was found (R2=0.71) which represents the effectiveness of the remote sensing tools for crop monitoring and production estimation.

Keywords: landsat, NDVI, remote sensing, satellite images, yield

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Authors: Mpaphi Major

Abstract:

Soil erosion is becoming an important aspect of land degradation. Therefore it is of great consideration to note any factor that may escalate the rate of soil erosion in our arable land. There exist 3 main driving forces in soil erosion which are rainfall, wind and land use of which in this project only rainfall and land use will be looked at. With the increase in world population at an alarming rate, the demand for food production is expected to increase which will in turn lead to more land being converted from forests to agricultural use of which very few of it are now fertile. In our country Botswana, the rate of crop production is decreasing due to the wearing away of the fertile top soil and poor arable land management. As a result, some studies on the rate of soil loss and farm management practices should be conducted so that best soil and water conservation practices should be employed and hence reduce the risk of soil loss and increase the rate of crop production and yield. The Soil loss estimation model for Southern Africa (SLEMSA) will be used to estimate the rate of soil loss in some selected arable farms within the Palapye watershed and some field observations will be made to determine the management practices used and their impact on the arable land. Upon observations it have been found that many arable fields have been exposed to soil erosion, of which the affected parts are no longer suitable for any crop production unless the land areas are modified. Improper land practices such as ploughing along the slope and land cultivation practices were observed. As a result farmers need to be educated on best conservation practices that can be used to manage their arable land hence reduced risk of soil erosion and improved crop production.

Keywords: soil and water conservation, soil erosion, SLEMSA, land degradation

Procedia PDF Downloads 373

Authors: Javed Kamal

Abstract:

A proposed field study of wheat-based cropping systems was conducted at Faisalabad (Post-Graduate Research Station). We used 7 treatments and Jantar as a green manuring crop to increase the fertility status of soil; after the vegetative phases of wheat, rice, sorghum, and mungbean, the agronomic parameters of these crops were recorded. Hopefully, all increased with jantar treatment when compared with controls. The benefit: cost ratio and physicochemical characteristics of the soil before and after the crop harvest were also calculated.

Keywords: benifit cost ratio, jantar, sunflower, rice, wheat

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Authors: Shivangi Somvanshi

Abstract:

Poor crop productivity in salt-affected environment in the country is due to insufficient and untimely canal supply to agricultural land and inefficient field water management practices. This could further degrade due to inadequate maintenance of canal network, ongoing secondary soil salinization and waterlogging, worsening of groundwater quality. Large patches of low productivity in irrigation commands are occurring due to waterlogging and salt-affected soil, particularly in the scarcity rainfall year. Satellite remote sensing has been used for mapping of areas of low crop productivity, waterlogging and salt in irrigation commands. The spatial results obtained for these problems so far are less reliable for further use due to rapid change in soil quality parameters over the years. The existing spatial databases of canal network and flow data, groundwater quality and salt-affected soil were obtained from the central and state line departments/agencies and were integrated with GIS. Therefore, an integrated methodology based on remote sensing and GIS has been developed in ArcGIS environment on the basis of canal supply status, groundwater quality, salt-affected soils, and satellite-derived vegetation index (NDVI), salinity index (NDSI) and waterlogging index (NSWI). This methodology was tested for identification and delineation of area of low productivity in the Gautam Buddha Nagar district (Uttar Pradesh). It was found that the area affected by this problem lies mainly in Dankaur and Jewar blocks of the district. The problem area was verified with ground data and was found to be approximately 78% accurate. The methodology has potential to be used in other irrigation commands in the country to obtain reliable spatial data on low crop productivity.

Keywords: remote sensing, GIS, salt affected soil, crop productivity, Gautam Buddha Nagar

Procedia PDF Downloads 265

Authors: Olumuyiwa Idowu Ojo, Oluwatobi Peter Olowo

Abstract:

The study developed a Geographical Information Systems (GIS) database and mapped inter-annual changes in crop yields of cassava, cowpea, maize, rice, melon and yam as a response to inter-annual rainfall and temperature variability in Southwest, Nigeria. The aim of this project is to study the comparative analysis of the weather variability impact of six crops yield (Rice, melon, yam, cassava, Maize and cowpea) in South Western States of Nigeria (Oyo, Osun, Ekiti, Ondo, Ogun and Lagos) from 1991 – 2007. The data was imported and analysed in the Arch GIS 9 – 3 software environment. The various parameters (temperature, rainfall, crop yields) were interpolated using the kriging method. The results generated through interpolation were clipped to the study area. Geographically weighted regression was chosen from the spatial statistics toolbox in Arch GIS 9.3 software to analyse and predict the relationship between temperature, rainfall and the different crops (Cowpea, maize, rice, melon, yam, and cassava).

Keywords: GIS, crop yields, comparative analysis, temperature, rainfall, weather variability

Procedia PDF Downloads 295

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: Rafeza Begum, Mohammad Mokhlesur Rahman, Safikul Moula, Rafiqul Islam

Abstract:

Potassium (K) is crucial for healthy soil and plant growth. However, K fertilization is either disregarded or poorly underutilized in Bangladesh agriculture, despite the great demand for crops. This could eventually result in a significant depletion of the soil's potassium reserves, irreversible alteration of the minerals that contain potassium, and detrimental effects on crop productivity. Soil K mining in Bangladesh is a worrying problem, and we need to evaluate it thoroughly and find remedies. A pot culture experiment was conducted in the greenhouse of Bangladesh Institute of Nuclear Agriculture (BINA) using eleven soil series of Bangladesh in order to see the depletion behaviour of potassium (K) by continuous cropping using rice (var. Iratom-24) as the test crop. The soil series were Ranishankhail, Kaonia. Sonatala, Silmondi, Gopalpur, Ishurdi, Sara, Kongsha, Nunni, Lauta and Amnura on which four successive rice plants (45 days duration) were raised with (100 ppm K) or without addition of potassium. Nitrogen, phosphorus, sulfur and zinc were applied as basal to all pots. Potassium application resulted in higher dry matter yield, increased K concentration and uptake in all the soils compared with no K treatment; which gradually decreased in the subsequent harvests. Furthermore, plant takes up K not only from exchangeable pool but also from non-exchangeable sites and a minimum replenishment of K from the soil reserve was observed. Continuous cropping has resulted in the depletion of available K of the soil. The result indicated that in order to sustain higher crop yield under intensive cultivation, the addition of potash fertilizer is necessary.

Keywords: potassium, exchangeable pool, depletion behavior., Soil series

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Authors: Chantal Hendriks, Harry Gibson, Anna Trett, Penny Hancock, Catherine Moyes

Abstract:

The use of modern inputs for crop protection, such as insecticides, is strongly underestimated in Sub-Saharan Africa. Several studies measured toxic concentrations of insecticides in fruits, vegetables and fish that were cultivated in Sub-Saharan Africa. The use of agricultural insecticides has impact on human and environmental health, but it also has the potential to impact on insecticide resistance in malaria transmitting mosquitos. To analyse associations between historic use of agricultural insecticides and the distribution of insecticide resistance through space and time, the use and environmental fate of agricultural insecticides needs to be mapped through the same time period. However, data on the use and environmental fate of agricultural insecticides in Africa are limited and therefore risk maps on the spatiotemporal occurrence of agricultural insecticides are created using environmental data. Environmental data on crop density and crop type were used to select the areas that most likely receive insecticides. These areas were verified by a literature review and expert knowledge. Pesticide fate models were compared to select most dominant processes that are involved in the environmental fate of insecticides and that can be mapped at a continental scale. The selected processes include: surface runoff, erosion, infiltration, volatilization and the storing and filtering capacity of soils. The processes indicate the risk for insecticide accumulation in soil, water, sediment and air. A compilation of all available data for traces of insecticides in the environment was used to validate the maps. The risk maps can result in space and time specific measures that reduce the risk of insecticide exposure to non-target organisms.

Keywords: crop protection, pesticide fate, tropics, insecticide resistance

Procedia PDF Downloads 115

Authors: D. Courault, L. Hossard, V. Demarez, E. Ndikumana, D. Ho Tong Minh, N. Baghdadi, F. Ruget

Abstract:

In the global change context, efficient management of the available resources has become one of the most important topics, particularly for sustainable crop development. Timely assessment with high precision is crucial for water resource and pest management. Rice cultivated in Southern France in the Camargue region must face a challenge, reduction of the soil salinity by flooding and at the same time reduce the number of herbicides impacting negatively the environment. This context has lead farmers to diversify crop rotation and their agricultural practices. The objective of this study was to evaluate this crop diversity both in crop systems and in agricultural practices applied to rice paddy in order to quantify the impact on the environment and on the crop production. The proposed method is based on the combined use of crop models and multispectral data acquired from the recent Sentinel 2 satellite sensors launched by the European Space Agency (ESA) within the homework of the Copernicus program. More than 40 images at fine spatial resolution (10m in the optical range) were processed for 2016 and 2017 (with a revisit time of 5 days) to map crop types using random forest method and to estimate biophysical variables (LAI) retrieved by inversion of the PROSAIL canopy radiative transfer model. Thanks to the high revisit time of Sentinel 2 data, it was possible to monitor the soil labor before flooding and the second sowing made by some farmers to better control weeds. The temporal trajectories of remote sensing data were analyzed for various rice cultivars for defining the main parameters describing the phenological stages useful to calibrate two crop models (STICS and SAFY). Results were compared to surveys conducted with 10 farms. A large variability of LAI has been observed at farm scale (up to 2-3m²/m²) which induced a significant variability in the yields simulated (up to 2 ton/ha). Observations on more than 300 fields have also been collected on land use. Various maps were elaborated, land use, LAI, flooding and sowing, and harvest dates. All these maps allow proposing a new typology to classify these paddy crop systems. Key phenological dates can be estimated from inverse procedures and were validated against ground surveys. The proposed approach allowed to compare the years and to detect anomalies. The methods proposed here can be applied at different crops in various contexts and confirm the potential of remote sensing acquired at fine resolution such as the Sentinel2 system for agriculture applications and environment monitoring. This study was supported by the French national center of spatial studies (CNES, funded by the TOSCA).

Keywords: agricultural practices, remote sensing, rice, yield

Procedia PDF Downloads 251