Quality of planting materials determines future successes of plantations and subsequent endeavours in the life cycle. Oil palm (Elaeis guineensis) breeding triggered an industry in Malaysia through “Plant Introduction” with the establishment of the first oil palm plantation. At the wake of the oil palm industry, plantations utilised the dura planting material. The hallmark discovery of the single gene inheritance for shell thickness led to the prolific dura x pisifera (DxP) derived tenera planting material. Subsequent parental inbred lines developed in recurrent selections, crossed and progeny tested exploiting heterosis had boosted yields. Further improvements were foresighted and executed in the widening of the genetic pool and collections of germplasm in centres of origin/diversity in Africa and Latin America. Field Genebank of the Malaysian Palm Oil Board (MPOB) forms the world’s largest ex situ oil palm conservation programme. This programme enabled the developments of elite breeding populations harbouring specialty oils and products. Meanwhile, opening of large oil palm areas by the Federal Land Development Authority (Felda) set the momentum in rapid expansion of the industry. Felda is an exemplary in wealth creation and quality of life (QOL). Resettlements of landless farmers into Felda schemes, employing modern farming, mainly in oil palm has helped eradicate poverty and uplifting QOL among settlers, employees and their families. Impacts of the success in wealth creation and its distribution leading to better QOL, rooted from breeding through the supply of quality planting materials. Phenotypic expressions of the planting materials were realised through genotypic and environment interactions; the former through breeding, the latter through agronomic practices. Efforts in oil palm breeding helped paved the way to a mammoth industry, contributing to the nation economic growths, impacting livelihood of the people. Further progress in yield is expected from clones, where breeding has a role in the supply of quality ortets. Genetic potential of planting materials can be further exploited through interdisciplinary approach in breeding, biotechnology and genomics. With continuing wealth creation, the oil palm saga continues. Once wealth is created, QOL will follow.
Pesticides and chemical fertilizers are widely used in agriculture to increase crop productivity among farmers. However, exposure to pesticides will give potential risk to human health. The aim of this study was to analyze the frequency of micronucleus (MN) and binucleus (BNu) formation in buccal cells from farmers who were exposed to pesticides using the MN assay. Buccal swabs were collected from the farmers in Tanjung Karang (n = 32) and Kelantan (n = 43) using wooden tongue depressor. A structured questionnaire was used to obtain demographic data of the farmers. Cytogenetic analysis was carried out by Acridin Orange (AO) staining 0.0025% (w/v). The frequency of MN and BNu as the biomarkers for cytogenetic damage was observed by using a fluorescence microscope. Comparison of frequency of MN and BNu is conducted in two areas namely Tanjung Karang, Selangor and Kelantan because of the agricultural activity and the type of pesticides used are different. Results showed that the frequencies of both MN and BNu among farmers in Tanjung Karang were significantly higher (p < 0.05) compared to farmers in Kelantan. Meanwhile, for the socio-demographic factors (age, smoking status, working period), MN and BNu frequencies among farmers in Tanjung Karang were also significantly higher (p < 0.05) as compared to farmers in Kelantan. While in the aspect of pesticide exposure, the frequencies of MN and BNu showed no significant difference between the frequency of pesticide spraying (p > 0.05) and the practices of PPE (Personal Protective Equipment) (p > 0.05). This may suggests that cytogenetic changes were not influenced by these factors. In addition, correlation study shows positive correlation between the frequency of MN with the pesticide exposure of farmers in Tanjung Karang (p > 0.05, r = 0.015) and Kelantan (p > 0.05, r = 0.0158). Besides, the frequency of BNu also has a positive correlation with the pesticide exposure among farmers in Tanjung Karang (p > 0.05, r = 0.036) and farmers in Kelantan (p > 0.05, r = 0.013). Hence, this present study demonstrated that exposure to pesticides increased the formation of MN and BNu among farmers and the prolonged use of pesticides may induce genotoxicity and DNA damage to human
The present work highlighted the production of violacein by the locally isolated Chromobacterium violaceum (GenBank accession no. HM132057) in various agricultural waste materials (sugarcane bagasse, solid pineapple waste, molasses, brown sugar), as an alternative to the conventional rich medium. The highest yield for pigment production (0.82 g L⁻¹) was obtained using free cells when grown in 3 g of sugarcane bagasse supplemented with 10% (v/v) of L-tryptophan. A much lower yield (0.15 g L⁻¹) was obtained when the cells were grown either in rich medium (nutrient broth) or immobilized onto sugarcane bagasse. Violacein showed similar chemical properties as other natural pigments based on the UV-Vis, Fourier transform infrared spectroscopy, thin-layer chromatography, nuclear magnetic resonance, and mass spectrometry analysis. The pigment is highly soluble in acetone and methanol, insoluble in water or non-polar organic solvents, and showed good stability between pH 5-9, 25-100 °C, in the presence of light metal ions and oxidant such as H₂O₂. However, violacein would be slowly degraded upon exposure to light. This is the first report on the use of cheap and easily available agricultural wastes as growth medium for violacein-producing C. violaceum.
This study accounts for the Hicks neutral technical change in a calibrated model of climate analysis, to identify the optimum level of technical change for addressing climate changes. It demonstrates the reduction to crop damages, the costs to technical change, and the net gains for the adoption of technical change for a climate-sensitive Pakistan economy. The calibrated model assesses the net gains of technical change for the overall economy and at the agriculture-specific level. The study finds that the gains of technical change are overwhelmingly higher than the costs across the agriculture subsectors. The gains and costs following technical change differ substantially for different crops. More importantly, the study finds a cost-effective optimal level of technical change that potentially reduces crop damages to a minimum possible level. The study therefore contends that the climate policy for Pakistan should consider the role of technical change in addressing climate impacts on the agriculture sector.
This paper gives a projection of the possible damage of climate change on the agriculture sector of Pakistan for the period 2012-2037, based on a dynamic approach, using an environment-related applied computable general equilibrium model (CGE). Climate damage projections depict an upward trend for the period of review and are found to be higher than the global average. Further, the damage to the agricultural sector exceeds that for the overall economy. By sector, climatic damage disproportionately affects the major and minor crops, livestock and fisheries. The largest losses following climate change, relative to the other agricultural sectors, are expected for livestock. The reason for this is the orthodox system of production for livestock, with a low adaptability to negative shocks of climate change. Overall, the findings reveal the high exposure of the agriculture sector to climate damage. In this regard, policymakers in Pakistan should take seriously the effects of climate change on agriculture and consider suitable technology to mitigate those damages.
This study empirically estimates farmers' willingness to pay (WTP) for a planned adaptation programme for addressing climate issues in Pakistan's agricultural sectors. The contingent valuation method (CVM) was employed to determine a monetary valuation of farmers' preferences for a planned adaptation programme by ascertaining the value attached to address climatic issues. The survey was conducted by distributing structured questionnaires among Pakistani farmers. The study found that 67 % of respondents were willing to pay for a planned adaptation programme. However, several socioeconomic and motivational factors exert greater influence on their willingness to pay (WTP). This paper specifies the steps needed for all institutional bodies to better address issues in climate change. The outcomes of this paper will support attempts by policy makers to design an efficient adaptation framework for mitigating and adapting to the adverse impacts of climate change.
The significance of Science Framework (SF) to date is receiving more acceptances all over the world to address agricultural sustainability. The professional views, however, advocate that the SF known as Mega Science Framework (MSF) in the transitional economies is not converging effectively in many ways for the agricultural sustainability. Specially, MSF in transitional economies is mostly incapable to identify barriers in agricultural research, inadequate to frame policy gaps with the goal of strategizing the desired sustainability in agricultural technology and innovation, inconsistent in finding to identify the inequities, and incompleteness to rebuild decisions. Therefore, this study critically evaluates the components of MSF in transitional economies and appraises the significance, dispute and illegitimate issue to achieve successful sustainable development. A sound and an effective MSF can be developed when there is an inter-linkage within principal components such as of (a) national priorities, (b) specific research on agricultural sustainability, (c) adequate agricultural research and innovation, and (d) alternative policy alteration. This maiden piece of research which is first its kind has been conducted in order to outline the policy direction to have an effective science framework for agricultural sustainability.
Cold plasma (low pressure) technology has been effectively used to boost the germination and growth of various crops in recent decades. The durability of these plasma-treated seeds is essential because of the need to store and distribute the seeds at different locations. However, these ageing effects are often not ascertained and reported because germination and related tests are carried out within a short time after the plasma-treatment. This research aims to fill that knowledge gap by subjecting three different types of seeds (and precursors): Bambara groundnuts (water), chilli (oxygen), and papaya (oxygen) to cold plasma-treatment. Common mechanisms found for these diverse seed types and treatment conditions were the physical and chemical changes induced by the physical etching and the cold plasma on the seeds and subsequent oxidation, which promoted germination and growth. The high glass transition temperature of the lignin-cellulose prevented any physical restructuring of the surfaces while maintaining the chemical changes to continue to promote the seeds germination and growth. These changes were monitored over 60 days of ageing using water contact angle (WCA), water uptake, electrical conductivity, field emission scanning electron microscopy (FE-SEM) and X-ray photoelectron spectroscopy (XPS). The vacuum effect was also investigated to separate its effect from cold plasma (low pressure). This finding offers a framework for determining how long agricultural seeds that have received plasma treatment can be used. Additionally, there is a need to transfer this research from the lab to the field. Once the impact of plasma treatment on seeds has been estimated, it will be simple to do so.
In Malaysia, pineapples are grown on peat soils, but most K fertilizer recommendations do not take into account K loss through leaching. The objective of this study was to determine applied K use efficiency under a conventionally recommended fertilization regime in pineapple cultivation with residues removal. Results showed that K recovery from applied K fertilizer in pineapple cultivation on tropical peat soil was low, estimated at 28%. At a depth of 0-10 cm, there was a sharp decrease of soil total K, exchangeable K, and soil solution K days after planting (DAP) for plots with K fertilizer. This decline continued until the end of the study. Soil total, exchangeable, and solution K at the end of the study were generally lower than prior values before the study. There was no significant accumulation of K at depths of 10-25 and 25-45 cm. However, K concentrations throughout the study period were generally lower or equal to their initial status in the soil indicating leaching of the applied K and partly explained the low K recovery. Potassium losses through leaching in pineapple cultivation on tropical peat soils need to be considered in fertilizer recommendations for efficient recovery of applied K.
Introduction: In view of high healthcare expenditure, Malaysia also faces problems in healthcare financing. The policy option is to establish a national health financing scheme. However, it is a problem to develop mechanisms to cover social insurance package to more than one third of the population working in informal sector such as farmers. Therefore, there is an urgent need to assess the ability and willingness of the farming community. The main objective was to study the ability and willingness in the farming community to contribute to national healthcare financing scheme.
Methodology: This a cross sectional study involved 400 farmers in Selangor. A total of 92.3% farmers were able to pay for the healthcare.
Results: Willingness to contribute to The national healthcare financing scheme were RM2.00 per month.
Conclusion: The education level influenced the ability to pay while the educational level and per capita income influenced willingness to pay.
Advanced sensor technology, especially those that incorporate artificial intelligence (AI), has been recognized as increasingly important in various contemporary applications, including navigation, automation, water under imaging, environmental monitoring, and robotics. Data-driven decision-making and higher efficiency have enabled more excellent infrastructure thanks to integrating AI with sensors. The agricultural sector is one such area that has seen significant promise from this technology using the Internet of Things (IoT) capabilities. This paper describes an intelligent system for monitoring and analyzing agricultural environmental conditions, including weather, soil, and crop health, that uses internet-connected sensors and equipment. This work makes two significant contributions. It first makes it possible to use sensors linked to the IoT to accurately monitor the environment remotely. Gathering and analyzing data over time may give us valuable insights into daily fluctuations and long-term patterns. The second benefit of AI integration is the remote control; it provides for essential activities like irrigation, pest management, and disease detection. The technology can optimize water usage by tracking plant development and health and adjusting watering schedules accordingly. Intelligent Control Systems (Matlab/Simulink Ver. 2022b) use a hybrid controller that combines fuzzy logic with standard PID control to get high-efficiency performance from water pumps. In addition to monitoring crops, smart cameras allow farmers to make real-time adjustments based on soil moisture and plant needs. Potentially revolutionizing contemporary agriculture, this revolutionary approach might boost production, sustainability, and efficiency.
Slaughterhouse and wet market wastes are pollutants that have been always neglected by society. According to the Food and Agriculture Organization of the United Nations, more than three billion and nineteen million livestock were consumed worldwide in 2018, which reflects the vast amount and the broad spectrum of the biowastes generated. Slaughterhouse biowastes are a significant volume of biohazards that poses a high risk of contamination to the environment, an outbreak of diseases, and insecure food safety. This work comprehensively reviewed existing biowaste disposal practices and revealed the limitations of technological advancements to eradicate the threat of possible harmful infectious agents from these wastes. Policies, including strict supervision and uniform minimum hygienic regulations at all raw food processing factories, should therefore be tightened to ensure the protection of the food supply. The vast quantity of biowastes also offers a zero-waste potential for a circular economy, but the incorporation of biowaste recycling, including composting, anaerobic digestion, and thermal treatment, nevertheless remains challenging.
Rhodamine B (RhB) is among the toxic dyes due to the carcinogenic, neurotoxic effects and ability to cause several diseases for humans. The adsorption with agricultural waste adsorbent recorded high performance for the RhB removal. The current review aimed to explore the efficiency of different adsorbents which have been used in the few last years for removing RhB dye from wastewater. The data of adsorption of RhB using agricultural wastes were collected from the Scopus database in the period between 2015 and 2021. The use of agricultural wastes and adsorbents as a replacement for the activated has received high attention among researchers. The RhB removal methods by microbial enzymes and biomass occurred between 76 and 90.1%. In comparison, the adsorption with agricultural wastes such as activated carbon white sugar reached 98% within 12 min. The adsorption process has a wide range of pH (3-10) due to the zwitterionic forms of RhB. Gmelina aborea leaf activated carbon is among the agriculture wastes absorbents that exhibited 1000 mg g-1 of the adsorption capacity. It appeared that the agricultural wastes adsorbents have a high potential for removing RhB from the wastewater.
In the present study, an assessment of land suitability potential for agriculture in the study area of IBB governorate, Republic of Yemen has been conducted through close examination of the indicators of land characteristics and qualities. The objective of this study is to evaluate the available land resource and produce the potential map of the study area. Remote sensing data help in mapping land resources, especially in mountainous areas where accessibility is limited. Satellite imagery data used for this study includes data from multi-temporal Landsat TM which dated June 2001. The parameters taken into consideration were 16 thematic maps i.e., slope, DEM, rainfall, soil, land use, land degradation as well as land characteristics maps. Satellite image of the study area has been classified for land use, land degradation and soil maps preparation, while topo sheet and ancillary data have been used for slope and DEM maps and soil properties determination. The land potential of the study area was categorized as very high, high, moderate, low and very low by adopting the logical criteria. These categories were arrived at by integrating the various layers with corresponding weights in a Geographical Information System (GIS). The study demonstrates that the study area can be categorized into spatially distributed agriculture potential zones based on the soil properties, terrain characteristics and analyzing present land use. This approach has the potential as a useful tool for guiding policy decision on sustainable land resource management.
Abundance and diversity of benthic macroinvertebrates as well as physico-chemical parameters were investigated in five rivers of the Juru River Basin in northern Peninsula Malaysia: Ceruk Tok Kun River (CTKR), Pasir River (PR), Permatang Rawa River (PRR), Kilang Ubi River (KUR), and Juru River (JR). The physico-chemical parameters and calculated water quality index (WQI) were significantly different among the investigated rivers (ANOVA, P<0.05). The WQI classified CTKR, PR, and JR into class III (slightly polluted). However, PRR and KUR fell into class IV (polluted). High diversity and abundance of macroinvertebrates, especially the intolerant taxa, Ephemeroptera, Plecoptera, and Trichoptera, were observed in the least polluted river, CTKR. Decreasing abundance of macroinvertebrates followed the deterioration of river water quality with the least number of the most tolerant taxa collected from PR. On the basis of composition and sensitivity of macroinvertebrates to pollutants in each river, the highest Biological Monitoring Working Party (BMWP) index score of 93 was reported in CTKR (good water quality). BMWP scores in PRR and JR were 38.7 and 20.1, respectively, classifying both of them into "moderate water quality" category. Poor water quality was reported in PR and KUR. The outcome of the multivariate analysis (CCA) was highly satisfactory, explaining 43.32% of the variance for the assemblages of macroinvertebrates as influenced by 19 physical and chemical variables. According to the CCA model, we assert that there were three levels of stresses on macroinvertebrate communities in the investigated rivers: Level 1, characterized of undisturbed or slightly polluted as in the case of CTKR; Level 2, characterized by a lower habitat quality (the JR) compared to the CTKR; and Level 3 showed severe environmental stresses (PRR, PR, and KUR) primarily contributed by agricultural, industrial, and municipal discharges.
Environmental quality indicators are crucial for responsive and cost-effective policies. The objective of the study is to examine the relationship between environmental quality indicators and financial development in Malaysia. For this purpose, the number of environmental quality indicators has been used, i.e., air pollution measured by carbon dioxide emissions, population density per square kilometer of land area, agricultural production measured by cereal production and livestock production, and energy resources considered by energy use and fossil fuel energy consumption, which placed an impact on the financial development of the country. The study used four main financial indicators, i.e., broad money supply (M2), domestic credit provided by the financial sector (DCFS), domestic credit to the private sector (DCPC), and inflation (CPI), which each financial indicator separately estimated with the environmental quality indicators, over a period of 1975-2013. The study used the generalized method of moments (GMM) technique to minimize the simultaneity from the model. The results show that carbon dioxide emissions exert the positive correlation with the M2, DCFC, and DCPC, while there is a negative correlation with the CPI. However, these results have been evaporated from the GMM estimates, where carbon emissions have no significant relationship with any of the four financial indicators in Malaysia. The GMM results show that population density has a negative relationship with the all four financial indicators; however, in case of M2, this relationship is insignificant to explain their result. Cereal production has a positive relationship with the DCPC, while there is a negative relationship with the CPI. Livestock production exerts the positive relationship with the all four financial indicators; however, this relationship with the CPI has a more elastic relationship, while the remaining relationship is less elastic with the three financial indicators in a country. Energy resources comprise energy use and fossil fuel energy consumption, both have distinct results with the financial indicators, as energy demand have a positive and significant relationship with the DCFC, DCPC, and CPI, while fossil fuel energy consumption have a negative relationship with these three financial indicators. The results of the study are of value to both environmentalists and policy makers.
Mosquito borne diseases have remained a grave threat to human health and are posing a significant burden on health authorities around the globe. The understanding and insight of mosquito breeding habitats features is crucial for their effective management. Comprehensive larval surveys were carried out at 14 sites in Qatar. A total of 443 aquatic habitats were examined, among these 130 were found positive with Culex pipiens, Cx. quinquefasciatus, Cx. mattinglyi, Ochlerotatus dorsalis, Oc. caspius and Anopheles stephensi. The majority of positive breeding habitats were recorded in urban areas (67.6%), followed by livestock (13.8%), and least were in agriculture (10.7%). An. stephensi larvae were positively correlated with Cx. pipien, Cx. quinquefasciatus, and negatively with water salinity. Large and shaded habitats were the most preferred by An. stephensi. In addition, Cx. pipiens was positively associated with the turbidity and pH, and was negatively associated with vegetation and habitat size. A negative association of Cx. quinquefasciatus with dissolved oxygen, water temperature, and salinity, while positive with habitat surface area was observed. Oc. dorsalis was negatively correlated with pH, water temperature, depth, and habitat surface area, whereas salinity water was more preferable site for females to lay their eggs. These results demonstrate that environmental factors play a significant role in preference of both anopheline and culicine for oviposition, while their effective management must be developed as the most viable tool to minimize mosquito borne diseases.
Citric acid (CA) has a high demand due to its various uses in the food and pharmaceutical industries. However, the natural supply of CA is minimal compared to its growing industrial demand. The increasing demand for CA can be fulfilled by using biotechnological processes. This study utilized liquid state bioconversion by Aspergillus niger for CA production using sugarcane molasses as the primary substrate. Sugarcane molasses which is agricultural waste consists of significant proportion of organic matters such as lipids and carbohydrates. This makes sugarcane molasses as a potential and alternative source of producing CA at a lower cost. In this study, statistical optimization was applied to improve CA production using submerged fermentation in shake flasks. Aspergillus niger was cultured in potato dextrose agar. Then, inoculum spores were introduced into the fermentation media for a specific duration according to the experimental design from Central Composite Design (CCD) tool under Response Surface Methodology (RSM) in Design Expert 6.0 software. Three parameters were chosen to be optimized at 32⁰C i.e.agitation rate (160, 80, 200 rpm), substrate concentration (47, 60, 73%) and fermentation time (24, 72, 120 h). High Performance Liquid Chromatography (HPLC)and Fourier-transform infrared spectroscopy(FTIR) analyses were conducted to measure CA yield. The optimization study showed that the media incubated for 72 hours with a substrate concentration of 60% and an agitation speed of 180 rpm produced the highest CA yield(21.2 g/L).The analysis of variance (ANOVA) also showed that CCD quadratic model was significant with P-value< 0.0104 and R2is0.8964.
Atmospheric air temperature is the most crucial metrological parameter. Despite its influence on multiple fields such as hydrology, the environment, irrigation, and agriculture, this parameter describes climate change and global warming quite well. Thus, accurate and timely air temperature forecasting is essential because it provides more important information that can be relied on for future planning. In this study, four Data-Driven Approaches, Support Vector Regression (SVR), Regression Tree (RT), Quantile Regression Tree (QRT), ARIMA, Random Forest (RF), and Gradient Boosting Regression (GBR), have been applied to forecast short-, and mid-term air temperature (daily, and weekly) over North America under continental climatic conditions. The time-series data is relatively long (2000 to 2021), 70% of the data are used for model calibration (2000 to 2015), and the rest are used for validation. The autocorrelation and partial autocorrelation functions have been used to select the best input combination for the forecasting models. The quality of predicting models is evaluated using several statistical measures and graphical comparisons. For daily scale, the SVR has generated more accurate estimates than other models, Root Mean Square Error (RMSE = 3.592°C), Correlation Coefficient (R = 0.964), Mean Absolute Error (MAE = 2.745°C), and Thiels' U-statistics (U = 0.127). Besides, the study found that both RT and SVR performed very well in predicting weekly temperature. This study discovered that the duration of the employed data and its dispersion and volatility from month to month substantially influence the predictive models' efficacy. Furthermore, the second scenario is conducted using the randomization method to divide the data into training and testing phases. The study found the performance of the models in the second scenario to be much better than the first one, indicating that climate change affects the temperature pattern of the studied station. The findings offered technical support for generating high-resolution daily and weekly temperature forecasts using Data-Driven Methodologies.