Browsing by Author "Ramilan T"

Now showing 1 - 19 of 19
  • Thumbnail Image
    Item
    A Mixed-Methods Study of Factors Influencing Access to and Use of Micronutrient Powders in Rwanda
    (Johns Hopkins Center for Communication Programs, 2021-06-30) Dusingizimana T; Weber JL; Ramilan T; Iversen PO; Brough L
    The World Health Organization recommends point-of-use fortification with multiple micronutrients powder (MNP) for foods consumed by children aged 6-23 months in populations where anemia prevalence among children under 2 years or under 5 years of age is 20% or higher. In Rwanda, anemia affects 37% of children under 5 years. The MNP program was implemented to address anemia, but research on factors affecting the implementation of the MNP program is limited. We conducted a mixed-methods study to examine the factors influencing access to and use of MNP among mothers (N=379) in Rutsiro district, northwest Rwanda. Inductive content analysis was used for qualitative data. Logistic regression analysis was used to determine factors associated with the use of MNP. Qualitative results indicated that the unavailability of MNP supplies and distribution issues were major barriers to accessing MNP. Factors influencing the use of MNP included mothers' perceptions of side effects and health benefits of MNP, as well as inappropriate complementary feeding practices. Mothers of older children (aged 12-23 months) were more likely to use MNP than those of younger children (aged 6-11 months) (adjusted odds ratio [aOR]=3.63, P<.001). Mothers whose children participated in the supplementary food program were nearly 3 times more likely to use MNP than those whose children had never participated in the program (aOR=2.84, P=.001). Increasing household hunger score was significantly associated with lower odds of using MNP (aOR=0.80, P=.038). Mechanisms to monitor MNP supply and program implementation need to be strengthened to ensure mothers have access to the product. MNP program implementers should address gaps in complementary feeding practices and ensure mothers have access to adequate complementary foods. L'Organisation Mondiale de la Santé recommande l'enrichissement de l'alimentation à domicile (enrichissement sur le point d'utilisation) à l'aide des poudres de micronutriments multiples (PMN) pour les aliments consommés par les enfants âgés de 6 à 23 mois dans les populations où la prévalence de l'anémie chez les enfants de moins de 2 ans ou 5 ans est de 20% ou plus. Au Rwanda, l'anémie touche 37% des enfants de moins de 5 ans et le programme de PMN a été mis en œuvre pour lutter contre l'anémie. Cependant, la recherche sur les facteurs qui affectent la mise en œuvre du programme de PMN est limitée. Nous avons mené une étude par méthodes mixtes pour examiner les facteurs qui influencent l'accès des mères (n=379) à la PMN et son utilisation dans le district de Rutsiro, au nord-ouest du Rwanda. L'analyse du contenu inductif a été utilisée pour les données qualitatives. Pour déterminer les facteurs associés à l'utilisation des PMN, une régression logistique a été utilisée. Les résultats qualitatifs ont indiqué que l'indisponibilité des approvisionnements en PMN et les problèmes de distribution constituaient des obstacles majeurs à l'accès à la PMN. Les facteurs qui influencent l'utilisation des PMN comprenaient les perceptions, chez les mères, des effets secondaires et des avantages des PMN pour la santé, ainsi que des pratiques d'alimentation complémentaire inappropriées. Les mères d'enfants plus âgés (12 à 23 mois) étaient plus susceptibles d'utiliser la PMN que celles d'enfants plus jeunes (6 à 11 mois) (odds ratio ajusté [ORA]=3,63, P<0,001). Les mères des enfants qui avaient participé au programme d'alimentation complémentaire étaient près de 3 fois plus susceptibles d'utiliser la PMN que celles des enfants qui n'avaient jamais participé au programme (ORA=2,84, P=0,001). L'augmentation du score de faim dans les ménages était significativement associée à des chances plus faibles d'utiliser la PMN (ORA=0,80, P=0,038). Les mécanismes de suivi de l'approvisionnement en PMN et de la mise en œuvre du programme doivent être renforcés pour s'assurer que les mères ont accès au produit. Les responsables de la mise en œuvre du programme de PMN devraient combler les lacunes au niveau des pratiques d'alimentation complémentaire et veiller à ce que les mères aient accès à des aliments complémentaires adéquats.
  • Thumbnail Image
    Item
    Assessing the Leaf Blade Nutrient Status of Pinot Noir Using Hyperspectral Reflectance and Machine Learning Models
    (MDPI AG, 8/03/2023) Lyu H; Grafton MC; Ramilan T; Irwin M; Sandoval - Cruz E; Díaz-Varela, RA
  • Thumbnail Image
    Item
    Assessing the Leaf Blade Nutrient Status of Pinot Noir Using Hyperspectral Reflectance and Machine Learning Models
    (MDPI (Basel, Switzerland), 2023-03-08) Lyu H; Grafton M; Ramilan T; Irwin M; Sandoval E; Díaz-Varela RA
    Monitoring grape nutrient status, from flowering to veraison, is important for viticulturists when implementing vineyard management strategies, in order to produce quality wines. However, traditional methods for measuring nutrient elements incur high labour costs. The aim of this study is to explore the potential of predicting grapevine leaf blade nutrient concentration based on hyperspectral data. Leaf blades were collected at two Pinot Noir commercial vineyards at Martinborough, New Zealand. The leaf blade spectral data were obtained with a handheld spectroradiometer, to evaluate surface reflectance and derivative spectra in the spectrum range between 400 and 2400 nm. Afterwards, leaf blades nutrient concentrations (N, P, K, Ca, and Mg) were measured, and their relationships with the hyperspectral data were modelled by machine learning models; partial least squares regression (PLSR), random forest regression (RFR), and support vector regression (SVR) were used. Pearson correlation and recursive feature elimination, based on cross-validation, were used as feature selection methods for RFR and SVR, to improve the model’s performance. The variable importance score of PLSR, and permutation variable importance of RFR and SVR, were used to determine the most sensitive wavelengths, or spectral regions related to each biochemical variable. The results showed that the best predictive performance for leaf blade N concentration was based on PLSR to raw reflectance data (R2 = 0.66; RMSE = 0.15%). The combination of support vector regression with the Pearson correlation selected method and second derivative reflectance provided a high accuracy for K and Ca modelling (R2 = 0.7; RMSE = 0.06%; R2 = 0.62; RMSE = 0.11%, respectively). However, the modelling performance for P and Mg, by different feature groups and variable selection methods, was poor (R2 = 0.15; RMSE = 0.02%; R2 = 0.43; RMSE = 0.43%, respectively). Thus, a larger dataset is needed for improving the prediction of P and Mg. The results indicated that for Pinot Noir leaf blades, raw reflectance data had potential for the prediction of N concentration, while the second-derivative spectra were more suitable to predict K and Ca. This study led to the provision of rapid and non-destructive measurements of grapevine leaf nutrient status.
  • Thumbnail Image
    Item
    Comparison of Nutritive Values of Tropical Pasture Species Grown in Different Environments, and Implications for Livestock Methane Production: A Meta-Analysis
    (MDPI (Basel, Switzerland), 2022-07-14) Jayasinghe P; Ramilan T; Donaghy DJ; Pembleton KG; Barber DG
    The demand for dairy products is ever increasing across the world. The livestock sector is a significant source of greenhouse gas (GHG) emissions globally. The availability of high-quality pasture is a key requirement to increase the productivity of dairy cows as well as manage enteric methane emissions. Warm-season perennial grasses are the dominant forages in tropical and subtropical regions, and thus exploring their nutritive characteristics is imperative in the effort to improve dairy productivity. Therefore, we have collated a database containing a total of 4750 records, with 1277 measurements of nutritive values representing 56 tropical pasture species and hybrid cultivars grown in 26 different locations in 16 countries; this was done in order to compare the nutritive values and GHG production across different forage species, climatic zones, and defoliation management regimes. Average edaphoclimatic (with minimum and maximum values) conditions for tropical pasture species growing environments were characterized as 22.5 °C temperature (range 17.5-29.30 °C), 1253.9 mm rainfall (range 104.5-3390.0 mm), 582.6 m elevation (range 15-2393 m), and a soil pH of 5.6 (range 4.6-7.0). The data revealed spatial variability in nutritive metrics across bioclimatic zones and between and within species. The ranges of these nutrients were as follows: neutral detergent fibre (NDF) 50.9-79.8%, acid detergent fibre (ADF) 24.7-57.4%, crude protein (CP) 2.1-21.1%, dry matter (DM) digestibility 30.2-70.1%, metabolisable energy (ME)3.4-9.7 MJ kg-1 DM, with methane (CH4) production at 132.9-133.3 g animal-1 day-1. The arid/dry zone recorded the highest DM yield, with decreased CP and high fibre components and minerals. Furthermore, the data revealed that climate, defoliation frequency and intensity, in addition to their interactions, have a significant effect on tropical pasture nutritive values and CH4 production. Overall, hybrid and newer tropical cultivars performed well across different climates, with small variations in herbage quality. The current study revealed important factors that affect pasture nutritive values and CH4 emissions, with the potential for improving tropical forage through the selection and management of pasture species.
  • Thumbnail Image
    Item
    Enhancing climate resilience in northern Ghana: A stochastic dominance analysis of risk-efficient climate-smart technologies for smallholder farmers
    (Elsevier B.V., 2024-07-17) Ahiamadia D; Ramilan T; Tozer PR
    Northern Ghana is a semi-arid region characterised by a unimodal rainfall pattern, and hot and dry weather conditions. Heavy reliance on rain-fed agriculture and the lack of resources for irrigation, makes smallholder farmers in the region increasingly vulnerable to climate-related crop failures. In recent years, climate-smart technologies (CSTs) such as changing planting dates (PD), compartmental bunding (CB), mulching (M), and transplanting (TP) have been recommended to minimise yield losses. However, there is limited information on the most risk-efficient CSTs for crops cultivated in the region. This study used a stochastic dominance approach to identify the most risk-efficient CSTs for maize, rice, and sorghum. The stochastic modelling process employed the Aqua-crop model to simulate climate-related yield variability using Ghana climate data, and gross margin variability with crop budgets from literature sources. From the study's findings, changing planting date from April to May was the most risk-efficient choice for maize and sorghum under farmers' and recommended practices. In contrast, transplanting was the most risk-efficient technology for rice farming in the study area. The study also highlights the importance of considering the risk-averse nature of smallholder farmers when selecting CSTs. By identifying the most risk-efficient CSTs, the study can help improve the resilience of smallholder farmers. These findings have important implications for the development and adoption of CSTs in northern Ghana.
  • Thumbnail Image
    Item
  • Thumbnail Image
    Item
    Hyperspectral Imaging Spectroscopy for Non-Destructive Determination of Grape Berry Total Soluble Solids and Titratable Acidity
    (MDPI AG, 2024-05-07) Lyu H; Grafton M; Ramilan T; Irwin M; Sandoval E; Krasuki K; Weirzbicki D
  • Thumbnail Image
    Item
    Identifying potential for decision support tools through farm systems typology analysis coupled with participatory research: A case for smallholder farmers in Myanmar
    (MDPI (Basel, Switzerland), 2021-06-02) Thar SP; Ramilan T; Farquharson RJ; Chen D; Gröngröft A
    Decision Support Tools (DSTs) in agriculture have been widely developed but have not been well accepted by smallholder farmers. One reason for the limited use is that the tools do not account for the complexity of heterogeneous smallholder farming systems. Identifying farm typologies has facilitated technology transfer to target groups of farmers. Accounting for heterogeneity in farm systems can help in designing and deploying DSTs to address farmer needs. Typology analysis was applied to a 600-household survey dataset to identify different farm system types. Qualitative participatory research was used to assess the potential deployment of DSTs for fertilizer management. Six types of farm systems were identified with distinct characteristics in the study area of central Myanmar. Participatory research through focus group discussions with 34 participants from the six different farm types validated the farm typologies and found that farmers from one type considered that DSTs could be useful in gaining more information and knowledge. An important finding was that DSTs providing prescriptive advice were inconsistent with what many farmers want. Farmers indicated that discussion groups are a preferred learning-based approach rather than a prescriptive tool. Farmers preferred video clips and infographics integrated into existing familiar digital platforms. This study identifies heterogeneity within a large farm sample and develops a deeper understanding of fertilizer decisions as well as knowledge and intentions related to the use of DSTs or apps via follow-up focus group discussions. Incorporating a participatory research framework with typology identification can have a beneficial role in direct interactions with smallholders that may increase their acceptability of DSTs. This study has generated valuable information about farmer types and serves as a starting point for developing a framework for discussion support systems that may better relate to the needs of farmers.
  • Thumbnail Image
    Item
    Long-term evaluation of pasture production, seasonality, and variability: An application of the DairyMod pasture model for three tropical species
    (Elsevier B V, 2024-05) Jayasinghe JMP; Pembleton KG; Donaghy DJ; Ramilan T; Barber DG
    Adoption of improved pastures coupled with intensified management provide quality pastures in adequate quantities and thus improve livestock productivity. While pasture modelling is imperative for exploring the performance of newer pastures, models are little used for long-term simulations of multiple tropical pastures (genotype), under varying soil, climate (environment) and pasture production systems (management). We applied the DairyMod, a biophysical model to simulate the long-term pasture production of Brachiaria ruziziensis x B. decumbens x B. brizantha ‘Brachiaria Mulato II’ (BM), Megathyrsus maximus ‘Gatton Panic’ (GP), and Chloris gayana ‘Rhodes grass cv. Reclaimer’ (RR) across major dairying regions of Sri Lanka under different management scenarios and characterize the long-term pasture growth, seasonality and spatial variability, and possible implications for dairying in Sri Lanka. Simulations of three pasture species were carried out for 16 locations (8 dry (DZ), 5 intermediate (IZ), and 3 wet zone (WZ)) over 30 years (1980–2010). Three pasture management scenarios simulated were; 1) potential pasture production system under non-limiting N and irrigation (Yp) 2) rainfed pasture production system under non-limiting N fertilizer (Yw), and 3) rainfed pasture production system under current nitrogen (N) fertilizer rate (Ya). Statistical techniques were used to identify the long-term growth rates, variability, and trends in pasture production. The long-term pasture production varied greatly among climate, species, and management scenarios. Overall, the Ya showed a seasonal cycle following the rainfall pattern, with a reduction in growth rates in dry seasons (May–September). Pasture growth rates were greater in GP at Ya, and BM at Yw and Yp while RR showed the lowest growth rate at all times. Variability of pasture growth was high in DZ (May–September) and RR has the lowest growth variability. The Yw increased the growth rate (doubled) while the Yp substantially increased (nearly tripled) the growth rate and growth pattern producing less variable pastures. Simulated growth rates suggest that GP in low-input and BM in high-input farming areas would be more suitable. Our study suggested that the BM, GP, and RR are edaphic-climatologically fit for major dairying regions in Sri Lanka and the appropriate fertilizer and irrigation management can greatly increase the herbage accumulation and availability of year-round pastures. While this study offers valuable insights, the species-specific growth pattern, growth variability, yield potential under different managements and the possible implications for herbage quality need to be sensibly considered when selecting the appropriate species.
  • Thumbnail Image
    Item
    Nitrogen decisions for cereal crops: a risky and personal business
    Farquharson R; Chen D; Yong L; Liu D; Ramilan T
    Cereal crops principally require Nitrogen (N) and water for growth. Fertiliser economics are important because of the cost at sowing with expectation of a financial return after harvest. The production economics framework can be used to develop information for ‘best’ fertiliser decisions. But the variability of yield responses for rainfed production systems means that fertiliser decisions are a risky business. How do farmers make such decisions, and can economics give any guidance? Simulated wheat yield responses to N fertiliser applications show tremendous variation between years or seasons. There are strong agronomic arguments for a Mitscherlich equation to represent yield responses. Plots of the 10th, 50th and 90th percentiles of yield response distributions show likely outcomes in ‘Poor’, ‘Medium’ and ‘Good’ seasons at four Australian locations. By adding the prices for Urea and wheat we predict that the ‘best’ decisions vary with location, soil, and (sometimes) season. We compare these predictions with typical grower fertiliser decisions. Australian wheat growers understand the yield responses in their own paddocks and the relative prices, so they are making relevant short-term fertiliser decisions. These are subjective or personal decisions. Myanmar smallholders grow rice and maize in the Central Dry Zone, with relatively low levels of fertiliser and low crop yields. They have pre-existing poverty, high borrowing costs and are averse to risky outcomes. A Marginal Rate of Return (MRR) analysis with a hurdle rate of 100% is illustrated for the Australian locations, and this approach will be tested in Myanmar.
  • Thumbnail Image
    Item
    Optimal nitrogen fertilizer decisions for rice farming in a cascaded tank system in Sri Lanka: An analysis using an integrated crop, hydro-nutrient and economic model
    (Elsevier B V, 2023-04-01) Kanthilanka H; Ramilan T; Farquharson RJ; Weerahewa J; Timsina J
    CONTEXT: The ancient irrigation systems in Sri Lanka, known as village tank cascade systems, were developed to ensure an adequate and sustainable supply of good quality water to communities. However, there is growing concern about health and environmental issues related to the degradation of water quality caused by excessive nitrogen (N) levels from the overuse of chemical fertilizer. Subsidies for chemical fertilizer have encouraged fertilizer use for rice production in Sri Lanka. OBJECTIVES: The objective was to evaluate the use of N fertilizers for rice production in the Thirappane cascaded tank system and its impact on nitrate water quality. An optimal rate of N use was determined based on private (farm-level) decisions on fertilizer use. However, the private optimal fertilizer rate is not adequate for overall social welfare due to market failures such as incomplete information and the lack of a market to account for the negative impact of fertilizer use on tank water quality. The hypothesis is that the social optimal fertilizer rate is lower than the private optimal rate due to this discrepancy. The study aims to identify the sources of inefficiency in the sub-optimal use of fertilizers from a social perspective. METHOD: We developed an integrated crop, hydro-nutrient and economic model to analyze fertilizer decisions in the rice production process. The method involved conducting a marginal economic analysis based on simulated yield responses to N fertilizer and prices for inputs and outputs. The analysis was performed for three soil types across the Maha (rainy) and Yala (dry) seasons and for three different weather scenarios within each season. RESULTS AND CONCLUSIONS: When the negative impact of nitrate contamination on water quality is taken into account, the optimal N fertilizer rate from a social perspective is always lower than the optimal rate determined solely by private economic considerations. These optimal rates varied based on factors such as soil type, season, weather conditions during the growing season, and fertilizer prices. At unregulated, higher, fertilizer prices, the crop yields achieved at the social optimum were only slightly lower than those achieved under the private economic optimum. However, under regulated, lower, fertilizer prices, achieving the social optimum would require a larger reduction in N fertilizer use and result in a greater decrease in crop yields. SIGNIFICANCE: A systematic analysis that takes into account the social costs can serve as a guide for creating effective policies aimed at enhancing fertilizer decision making
  • Thumbnail Image
    Item
    Predictors for achieving adequate antenatal care visits during pregnancy: a cross-sectional study in rural Northwest Rwanda
    (BioMed Central Ltd, 2023-01-26) Dusingizimana T; Ramilan T; Weber JL; Iversen PO; Mugabowindekwe M; Ahishakiye J; Brough L
    BACKGROUND: Inadequate antenatal care (ANC) in low-income countries has been identified as a risk factor for poor pregnancy outcome. While many countries, including Rwanda, have near universal ANC coverage, a significant proportion of pregnant women do not achieve the recommended regimen of four ANC visits. The present study aimed to explore the factors associated with achieving the recommendation, with an emphasis on the distance from household to health facilities. METHODS: A geo-referenced cross-sectional study was conducted in Rutsiro district, Western province of Rwanda with 360 randomly selected women. Multiple logistic regression analysis including adjusted odd ratio (aOR) were performed to identify factors associated with achieving the recommended four ANC visits. RESULTS: The majority (65.3%) of women had less than four ANC visits during pregnancy. We found a significant and negative association between distance from household to health facility and achieving the recommended four ANC visits. As the distance increased by 1 km, the odds of achieving the four ANC visits decreased by 19% (aOR = 0.81, P = 0.024). The odds of achieving the recommended four ANC visits were nearly two times higher among mothers with secondary education compared with mothers with primary education or less (aOR = 1.90, P = 0.038). In addition, mothers who responded that their household members always seek health care when necessary had 1.7 times higher odds of achieving four ANC visits compared with those who responded as unable to seek health care (aOR = 1.7, P = 0.041). Furthermore, mothers from poor households had 2.1 times lower odds of achieving four ANC visits than mothers from slightly better-off households (aOR = 2.1, P = 0.028). CONCLUSIONS: Findings from the present study suggest that, in Rutsiro district, travel distance to health facility, coupled with socio-economic constraints, including low education and poverty can make it difficult for pregnant women to achieve the recommended ANC regimen. Innovative strategies are needed to decrease distance by bringing ANC services closer to pregnant women and to enhance ANC seeking behaviour. Interventions should also focus on supporting women to attain at least secondary education level as well as to improve the household socioeconomic status of pregnant women, with a particular focus on women from poor households.
  • Thumbnail Image
    Item
    Producing Higher Value Wool through a Transition from Romney to Merino Crossbred: Constraining Sheep Feed Demand
    (MDPI (Basel, Switzerland), 2021-10-01) Farrell LJ; Tozer PR; Kenyon PR; Cranston LM; Ramilan T
    A strategy to increase wool income for coarse wool (fibre diameter > 30 µm ) producers through a transition to higher value medium wool ( fibre diameter between 25 and 29 µm) was identified, with previous analyses allowing sheep feed demand increases to impractical levels during the transition period. This study modelled a whole flock transition from Romney breed to a 3/4Merino1/4Romney flock through crossbreeding with Merino sires, with sheep feed demand constrained between 55% and 65% of total grown feed. Transition was complete after 12 years, and the final 3/4M1/4R flock had higher COS (cash operating surplus; NZD 516/ha) than the base Romney flock (NZD 390/ha). Net present value analyses showed the transition always had an economic benefit (up to 13% higher) over the Romney flock. In a sensitivity analysis with sheep and wool sale prices changed by ±10%, higher sheep sale prices reduced the economic benefit of the transition (NPV up to 11% higher) over the Romney flock, as sheep sales comprised a higher proportion of income for the Romney flock, and higher wool sale prices increased the benefit (NPV up to 15% higher) of the transition to 3/4M1/4R over the Romney flock. This study demonstrated a whole flock transition from Romney to 3/4M1/4R breed was profitable and achievable without large variation in sheep feed demand, although the scale of benefit compared to maintaining a Romney flock was determined by changes in sheep and wool sale prices.
  • Thumbnail Image
    Item
    Quantifying Farm Household Resilience and the Implications of Livelihood Heterogeneity in the Semi-Arid Tropics of India
    (MDPI (Basel, Switzerland), 2022-03-25) Ramilan T; Kumar S; Haileslassie A; Craufurd P; Scrimgeour F; Kattarkandi B; Whitbread A; Caracciolo F
    The vast majority of farmers in the drylands are resource-poor smallholders, whose livelihoods depend heavily on their farming systems. Therefore, increasing the resilience of these smallholders is vital for their prosperity. This study quantified household resilience and identified livelihoods and their influence on resilience in the semiarid tropics of India by analysing 684 households. A resilience capacity index was devised based on the composition of household food and non-food expenditure, cash savings, and food and feed reserves. The index ranged from 8.4 reflecting highly resilient households with access to irrigation characteristics, to-3.7 for households with highly limited resilience and low household assets. The livelihoods were identified through multivariate analysis on selected socioeconomic and biophysical variables; households were heterogeneous in their livelihoods. Irrigated livestock and rainfed marginal types had the highest and lowest resilience capacity index with the mean score of 0.69 and −1.07, respectively. Finally, we quantified the influence of livelihood strategies on household resilience. Household resilience was strengthened by the possession of livestock, crop diversification and access to irrigation. Low resilience is predominantly caused by low household assets. The resilience capacity index and derived livelihood strategies helps to understand the complexity of household resilience, and will aid in targeting technology interventions for development.
  • Thumbnail Image
    Item
    Recommended vs. Practice: smallholder fertilizer decisions in central Myanmar
    (MDPI (Basel, Switzerland), 2021-01-14) Thar SP; Farquharson RJ; Ramilan T; Coggins S; Chen D
    Agriculture in Myanmar has substantial development potential given the abundance of land, water, and labor resources in the country. Despite this, agricultural productivity in Myanmar is low and farm incomes are amongst the lowest in Asia. The underperformance of crops and low yield is widely reported to be due to low fertilizer use by smallholders. This study investigated the perceptions of smallholders about fertilizer use for cereal crops by considering their motives and decision making. We reported results of a 600 smallholders’ survey and tested whether the reportedly low fertilizer use by smallholders is generally true for central Myanmar. We compared the fertilizer application timing against recommended “good management practices”. Among the surveyed rice farmers, the average fertilizer applied was much higher than previously reported national average fertilizer rates while the majority of the surveyed maize farmers were found to be applying less than the national recommended rates. With respect to timing, nearly half of the surveyed smallholders were not applying nitrogen at the estimated panicle initiation stage, which is often crucial to increase yield, and the majority (82%) of smallholders were applying phosphorus throughout the growth stages, when earlier applications are desirable. Smallholders may be able to reduce the cost of labor by reducing the number of P applications and avoiding late applications.
  • Thumbnail Image
    Item
    Suitability Evaluation of Three Tropical Pasture Species (Mulato II, Gatton Panic, and Rhodes Grass) for Cultivation under a Subtropical Climate of Australia
    (MDPI (Basel, Switzerland), 2022-09-01) Jayasinghe P; Donaghy DJ; Barber DG; Pembleton KG; Ramilan T
    first_pagesettingsOrder Article Reprints Open AccessEditor’s ChoiceArticle Suitability Evaluation of Three Tropical Pasture Species (Mulato II, Gatton Panic, and Rhodes Grass) for Cultivation under a Subtropical Climate of Australia by Priyanath Jayasinghe 1,2ORCID,Daniel J. Donaghy 1ORCID,David G. Barber 3,Keith G. Pembleton 4,*ORCID andThiagarajah Ramilan 1ORCID 1 School of Agriculture and Environment, Massey University, Private Bag 11 222, Palmerston North 4440, New Zealand 2 Department of Animal Science, Faculty of Animal Science and Export Agriculture, Uva Wellassa University, Badulla 90000, Sri Lanka 3 Agri-Science Queensland, Department of Agriculture and Fisheries Queensland, University of Queensland, Gatton Campus, Lawes, QLD 4343, Australia 4 Centre for Sustainable Agricultural Systems and School of Agriculture and Environmental Science, University of Southern Queensland, Toowoomba, QLD 4350, Australia * Author to whom correspondence should be addressed. Agronomy 2022, 12(9), 2032; https://doi.org/10.3390/agronomy12092032 Submission received: 9 July 2022 / Revised: 15 August 2022 / Accepted: 16 August 2022 / Published: 26 August 2022 (This article belongs to the Section Grassland and Pasture Science) Downloadkeyboard_arrow_down Browse Figures Versions Notes Abstract Exploring improved tropical forages is considered to be an important approach in delivering quality and consistent feed options for dairy cattle in tropical and subtropical regions. The present study aimed to study the suitability of three improved tropical grasses, Chloris gayana ‘Rhodes grass cv. Reclaimer’ (RR), Megathyrsus maximus ‘Gatton Panic’ (GP), and Brachiaria ruziziensis x B. decumbens x B. brizantha ‘Brachiaria Mulato II’ (BM) evaluating their carbon assimilation, canopy structure, herbage plant–part accumulation and quality parameters under irrigated conditions. An experiment was conducted at Gatton Research Dairy (27°54′ S, 152°33′ E, 89 m asl) Queensland, Australia, which has a predominantly subtropical climate. Photosynthesis biochemistry, canopy structure, herbage accumulation, plant part composition, and nutritive value were evaluated. Photosynthesis biochemistry differed between pasture species. Efficiency of CO2 assimilation was highest for GP and quantum efficiency was highest for BM. Pasture canopy structure was significantly affected by an interaction between pasture species and harvest. Forage biomass accumulation was highest in GP, while BM produced more leaf and less stem compared to both GP and RR. A greater leafy stratum and lower stemmy stratum depth were observed in the vertical sward structure of BM. Brachiaria Mulato II showed greater carbon partitioning to leaves, leaf: stem ratio, canopy, and leaf bulk density. It also demonstrated greater nutritive value (Total digestible nutrients (TDN), acid detergent fibre (ADF), neutral detergent fibre (NDF), neutral detergent insoluble protein (NDICP), Starch, nonfibre carbohydrates (NFC), metabolisable energy (ME), mineral profile (Mg, P, K, Fe, Zn) and dietary cation–anion difference (DCAD) for leaf, stem, and the whole plant. Greater quantum efficiency, leaf accumulation, and nutritive value of BM observed in the present study suggest BM as an attractive forage option for dairying that warrants further research in pasture-based systems in tropical and subtropical climates.
  • Thumbnail Image
    Item
    The impacts of the COVID-19 shock on sustainability and farmer livelihoods in Sri Lanka.
    (Elsevier B.V., 2022) Rathnayake S; Gray D; Reid J; Ramilan T
    The COVID-19 pandemic and its handling in Sri Lanka has affected vegetable farmers in numerous ways and these impacts will constrain the country's move towards sustainable development. A field level study with vegetable farmers and key informants was carried out using exploratory research to understand, describe and analyze the impact of COVID-19 on the livelihoods of vegetable farmers and its relevance in achieving SDG 1. Data were supplemented by an extensive literature review. The analysis showed that the pandemic's impact on vegetable farmers in Sri Lanka is multidimensional and will increase vulnerability among vegetable farmers, for the long run. Adapting alternative inputs and marketing strategies, provision of immediate financial support, promoting innovative technology and service provision, and implementing intervention strategies tailored to farmer heterogeneity will improve farmer livelihoods and the prosperity of the sector.
  • Thumbnail Image
    Item
    Using Remote and Proximal Sensing Data and Vine Vigor Parameters for Non-Destructive and Rapid Prediction of Grape Quality
    (MDPI AG, 2023-11-19) Lyu H; Grafton M; Ramilan T; Irwin M; Wei H-E; Sandoval E; Zhang C; Liu D
    The traditional method for determining wine grape total soluble solid (TSS) is destructive laboratory analysis, which is time consuming and expensive. In this study, we explore the potential of using different predictor variables from various advanced techniques to predict the grape TSS in a non-destructive and rapid way. Calculating Pearson’s correlation coefficient between the vegetation indices (VIs) obtained from UAV multispectral imagery and grape TSS resulted in a strong correlation between OSAVI and grape TSS with a coefficient of 0.64. Additionally, seven machine learning models including ridge regression and lasso regression, k-Nearest neighbor (KNN), support vector regression (SVR), random forest regression (RFR), extreme gradient boosting (XGBoost), and artificial neural network (ANN) are used to build the prediction models. The predictor variables include the unmanned aerial vehicles (UAV) derived VIs, and other ancillary variables including normalized difference vegetation index (NDVI_proximal) and soil electrical conductivity (ECa) measured by proximal sensors, elevation, slope, trunk circumference, and day of the year for each sampling date. When using 23 VIs and other ancillary variables as input variables, the results show that ensemble learning models (RFR, and XGBoost) outperform other regression models when predicting grape TSS, with the average of root mean square error (RMSE) of 1.19 and 1.2 ◦Brix, and coefficient of determination (R2 ) of 0.52 and 0.52, respectively, during the 20 times testing process. In addition, this study examines the prediction performance of using optimized soil adjusted vegetation index (OSAVI) or normalized green-blue difference index (NGBDI) as the main input for different machine learning models with other ancillary variables. When using OSAVI-based models, the best prediction model is RFR with an average R2 of 0.51 and RMSE of 1.19 ◦Brix, respectively. For NGBDI-based model, the RFR model showed the best average result of predicting TSS were a R2 of 0.54 and a RMSE of 1.16 ◦Brix, respectively. The approach proposed in this study provides an opportunity to grape growers to estimate the whole vineyard grape TSS in a non-destructive way.
  • Thumbnail Image
    Item
    Using Remote and Proximal Sensing Data and Vine Vigor Parameters for Non-Destructive and Rapid Prediction of Grape Quality
    (MDPI (Basel, Switzerland), 2023-11-19) Lyu H; Grafton M; Ramilan T; Irwin M; Wei H-E; Sandoval E; Zhang C; Liu D
    The traditional method for determining wine grape total soluble solid (TSS) is destructive laboratory analysis, which is time consuming and expensive. In this study, we explore the potential of using different predictor variables from various advanced techniques to predict the grape TSS in a non-destructive and rapid way. Calculating Pearson’s correlation coefficient between the vegetation indices (VIs) obtained from UAV multispectral imagery and grape TSS resulted in a strong correlation between OSAVI and grape TSS with a coefficient of 0.64. Additionally, seven machine learning models including ridge regression and lasso regression, k-Nearest neighbor (KNN), support vector regression (SVR), random forest regression (RFR), extreme gradient boosting (XGBoost), and artificial neural network (ANN) are used to build the prediction models. The predictor variables include the unmanned aerial vehicles (UAV) derived VIs, and other ancillary variables including normalized difference vegetation index (NDVI_proximal) and soil electrical conductivity (ECa) measured by proximal sensors, elevation, slope, trunk circumference, and day of the year for each sampling date. When using 23 VIs and other ancillary variables as input variables, the results show that ensemble learning models (RFR, and XGBoost) outperform other regression models when predicting grape TSS, with the average of root mean square error (RMSE) of 1.19 and 1.2 °Brix, and coefficient of determination (R2) of 0.52 and 0.52, respectively, during the 20 times testing process. In addition, this study examines the prediction performance of using optimized soil adjusted vegetation index (OSAVI) or normalized green-blue difference index (NGBDI) as the main input for different machine learning models with other ancillary variables. When using OSAVI-based models, the best prediction model is RFR with an average R2 of 0.51 and RMSE of 1.19 °Brix, respectively. For NGBDI-based model, the RFR model showed the best average result of predicting TSS were a R2 of 0.54 and a RMSE of 1.16 °Brix, respectively. The approach proposed in this study provides an opportunity to grape growers to estimate the whole vineyard grape TSS in a non-destructive way.