Research Articles (Centre for Geo-Information Science (CGIS))

Permanent URI for this collectionhttp://hdl.handle.net/2263/42368

Browse

Now showing 1 - 20 of 64

Phenology-based winter wheat classification for crop growth monitoring using multi-temporal sentinel-2 satellite data
(Elsevier, 2024-12) Newete, Solomon W.; Abutaleb, Khaled; Chirima, Johannes George; Dabrowska-Zielinska, Katarzyna; Gurdak, Radoslaw
Wheat is one of the most important staple crops consumed by more than four billion people in the world. However, its production is challenged by the impact of climate change which accounts for a 5.5 % reduction in wheat yield and it is predicted to dwindle further by about 30 % in 2050, due to trends in temperature, precipitation, and carbon dioxide. An effective annual crop estimate is necessary not only to inform governments the status of national food security, but also to determine the benchmark on which agricultural commodities are priced in the market. Thus, annual crop monitoring and yield estimate is paramount to determine the amount of wheat imports required to make up for the shortfalls in the national wheat production in South Africa, which has been a net importer of wheat since 1998. This study aimed at investigating the most distinguishable crop phenology for accurate winter wheat classification during the growing season from August – December 2020 using Sentinel-2 imageries and Random Forest algorithm. The winter wheat crop was more accurately identified during the crop ‘heading’ stage in October yielding the highest user’s (75.56 %) and producer’s (92.52 %) accuracies, despite the relatively lower overall accuracy (78.14 %) compared to that of December with overall accuracy of 83.58 % obtained during the maturity stage. This study, therefore, found that the extraction of NDVI values of the winter wheat crop over the period of the growing season using the Sentinel-2 NDVI series method and grouping these values into distinct classes using the K-means unsupervised clustering techniques assist to identify the different crop phenologies based on which the winter wheat crop could be detected and mapped accurately. The phenology-based classification of the winter wheat crop during the heading stage, reduce the ambiguity of spectral confusion created with surrounding grass and maize crops.
Developing models to detect maize diseases using spectral vegetation indices derived from spectral signatures
(Elsevier, 2024-09) Nkuna, Basani L.; Chirima, Johannes George; Newete, Solomon W.; Nyamugama, Adolph; Van der Walt, Adriaan J.
Maize, a vital global crop, faces numerous challenges, including outbreaks. This study explores the use of spectral vegetation indices for the early detection of maize diseases in individual leaves based on crop phenology at the vegetative, tasselling, and maturity stages. The research was conducted in rural areas of Giyani in the Limpopo province, South Africa, where smallholder farmers heavily rely on maize production for sustenance. Fungal and viral diseases pose significant threats to maize crops, necessitating precise and timely disease detection methods. Hyperspectral remote sensing, with its ability to capture detailed spectral information, offers a promising solution. The study analysed spectral reflectance data collected from healthy and diseased maize leaves. Various vegetation indices derived from spectral signatures, including the Normalized difference vegetation index (NDVI), Anthocyanin Reflectance Index (ARI), photochemical Reflectance Index (PRI), and Carotenoid Reflectance Index (CRI) were investigated for their ability to show disease-related spectral variations. The results indicated that during the tasselling stage, the spectral differences had minimum absorption in the blue region. However, a distinct shift in spectral reflectance was observed during the vegetative stage with 70 % increase in reflectance. First derivative reflectance analysis revealed peaks at approximately 715 nm and 722 nm, which were useful in the discrimination of the different growth stages. Generalized Linear Models (GLM) with binomial link functions and Akaike Information Criterion (AIC) showed that individual vegetation indices performed equally well. NDVI (P<0.001) and CRI (P<0.000) showed the lowest AIC values across all growth stages, suggesting their potential as effective disease indicators. These findings underscores the significance of employing remote sensing technology and spectral analysis as essential tools in the endeavours to tackle the difficulties encountered by maize growers, especially those operating small-scale farms, and to advance sustainable farming practices and ensure food security
Unraveling the relationship between soil nutrients and maize leaf disease occurrences in Mopani district municipality, Limpopo province, South Africa
(MDPI, 2024-10) Nkuna, Basani L.; Chirima, Johannes George; Newete, Solomon W.; Van der Walt, Adriaan J.; Nyamugama, Adolph
Maize is a staple crop important for food security that millions globally depend upon as an energy source, primarily due to its high starch and fat content. For growth and disease resistance, maize production requires a balanced intake of essential nutrients, including nitrogen (N), phosphorus (P) and potassium (K). This study investigated the relationship between soil nutrient levels and maize disease occurrences in the Mopani District Municipality, Limpopo Province, South Africa. Soil and maize leaves were collected using a systematic sampling approach. Grids of 10 × 10 m were created, covering a maize field. Forty soil samples were collected a day before the planting date and sent to the laboratory for analysis of N, P and K. During the tasseling stage of the maize plant, 40 maize leaf samples were collected and sent to the laboratory for disease identification. Maize leaves were classified as healthy, southern corn leaf blight (Bipolaris maydis), northern corn leaf blight (Exserohilum turcicum), maize streak disease (Maize streak virus), nitrogen-deficient or phosphorusdeficient. Generalized Linear Models (GLMs) with a corrected Akaike Information Criterion (AICc) showed a significant relationship between low soil nutrient levels of N, P and K and maize disease occurrence (p < 0.0001). The interaction of the N*P*K model had the lowest AIC value (AICc = 28.53), indicating the necessity of considering synergistic effects in maize disease management. All the model performances had a delta AICc = 0. These findings highlight the significance of comprehensive soil management strategies in enhancing the disease resistance, well-being and yields of maize crops.
Synergetic use of Sentinel-1 and Sentinel-2 data for wheat-crop height monitoring using machine learning
(MDPI, 2024-06) Nduku, Lwandile; Munghemezulu, Cilence; Mashaba-Munghemezulu, Zinhle; Ratshiedana, Phathutshedzo Eugene; Sibanda, Sipho; Chirima, Johannes George
Please read abstract in article.
Seasonal monitoring of biochemical variables in natural rangelands using Sentinel-1 and Sentinel-2 data
(Taylor and Francis, 2024) Rapiya, Monde; Ramoelo, Abel; Truter, Wayne Frederick; u16400829@tuks.co.za
Rangelands are natural ecosystems that serve as essential sources of forage for domesticated livestock and wildlife. Therefore, accurately mapping nutrient levels in rangelands is crucial for sustainable development and effective management of grazing animals. Remote sensing tools offer a reliable means to explore nutrient concentrations across large spatial areas. This study aimed to estimate and map seasonal foliar concentrations of nitrogen (N), phosphorus, and neutral detergent fibre (NDF) in mesic tropical rangelands of Limpopo using Sentinel-1, Sentinel-2, and the integration of S1 and S2 data. Fieldwork was conducted to collect samples for seasonal foliar nutrients (N, P, and NDF) during early-summer (November-January 2020), winter (July-August 2021), and late-summer (February-March 2022). Various conventional and red-edge-based vegetation indices were computed. The results demonstrate that integration data from S1 and S2 can effectively estimate and predict foliar concentrations of N, P, and NDF in mesic rangelands throughout the seasons, achieving R2 values of 0.76, 0.78, and 0.71, with corresponding RMSE values of 0.13, 0.04, and 2.52. Notably, red-edge variables emerged as the most significant parameters for predicting seasonal N, P, and NDF concentrations. Additionally, factors such as season and slope significantly influenced the distribution and occurrence of these foliage nutrients, with higher foliage production observed during late-summer and on steeper slopes. The study concludes that the integration of S1 and S2 data can effectively monitor the seasonal dynamics of biochemical parameters. This finding holds significant implications for policymakers and rangeland users, offering a comprehensive understanding of the intricate variations within rangeland ecosystems. Further research could expand on these findings by applying the knowledge to various datasets, exploring different rangelands, and examining additional ecological factors such as slope altitude to detect foliar fibre biochemicals. Finally, the applications of this research extend beyond individual properties, providing practical tools for sustainable rangeland management and informed decision-making in resource utilization and conservation.
Assessing species richness, diversity and assemblage of forest patches within a grassland matrix in the Afrotemperate ecosystems
(Borntraeger Science Publishers, 2024-08) Daemane, Mahlomola Ernest; Adelabu, Samuel; Ramoelo, Abel
Changes in species diversity have been widely used in environmental monitoring and global change studies as an indicator of vegetation change over time. High mountain ecosystems such as the Golden Gate Highlands National Park (GGHNP) host a relatively high number of plant species due to less human disturbances compared to the surrounding lowland areas. This study investigated the species richness and diversity in the Afrotemperate forest and woodland communities of the GGHNP. For vegetation classification, the TWINSPAN algorithm was firstly used to do a floristic analysis of thirty-two sampling plots and refined further using the Braun-Blanquet procedures and JUICE programmes. The Detrended Correspondence Analysis (DCA) and the phytosociological analysis of the vegetation data resulted in five plant communities and one sub-community across various topographic gradient. The Olinia emarginata–Podocarpus latifolius forest was found to be the most diverse forest whereas the Kiggelaria africana forest showed relatively lower species diversity. Species richness was also relatively high in the Olinia emarginata–Podocarpus latifolius forest plots, compared to the Leucosidea sericea–Buddleja salviifolia woodland, and the Euclea crispa–Protea caffra–roupelliae savannas. Data on plant assemblages and classification provide invaluable information for studies focussing on climate change, species distribution models and the associated bioclimatic variables. Understanding the importance and complexities of high mountains and forest ecosystems is therefore essential for developing effective conservation strategies.
The extent, perceived causes and impacts of land use and land cover change in Tyhume Valley, South Africa
(Frontiers Media, 2023-08-25) Masiza, Wonga; Hamandawana, Hamisai; Chirima, Johannes George; Khoboko, Pitso; Parkies, Nombuso
There is limited knowledge on how people living in communal areas perceive land use and land cover (LULC) change and the impacts it has on sustainable access to essential ecosystem goods and services. This study used seven wet season Landsat images covering 1989 to 2019 and the Extreme Gradient Boosting algorithm to map LULC in Tyhume Valley, South Africa. Analyses of trends in LULC and long-term changes in rainfall over the same period were based on the Mann Kendall (MK) statistical technique. Perceptions on the causes and impacts of the observed trends were solicited from 102 respondents and summarized through frequency analysis. Major trends that emerged from imagebased analysis include the expansion of Vachellia karroo by 25% (t = 0.98; p = 0.004), decrease in pastureland by 18% (t = –0.90, p = 0.007), decrease in cropland by 9.6% (t = –0.90, p = 0.007), decrease in surface water by 1.1% (-0.90, p = 0.007), and increase in built-up area by 2.5% (t = 1.00, p = 0.003). Perceived causes of these trends include the eradication of land access control systems, poor management of surface water, lack of farmer support programs, and 14 years of negative rainfall anomalies. The impacts of these changes include longdistance trekking of animals to pastures and watering points, increased livestock malnutrition and mortality, decline of medicinal and culturally significant trees, increased purchasing of stock feed, increased unemployment, and consumption of unhealthy food. The study concludes by highlighting the need to accommodate local perceptions in the formulation of policies and practices for sustainable use of ecosystem services.
Seasonal evaluation and mapping of aboveground biomass in natural rangelands using Sentinel-1 and Sentinel-2 data
(Springer, 2023-12) Rapiya, Monde; Ramoelo, Abel; Truter, Wayne Frederick; u16400829@tuks.co.za
Rangelands play a vital role in developing countries’ biodiversity conservation and economic growth, since most people depend on rangelands for their livelihood. Aboveground-biomass (AGB) is an ecological indicator of the health and productivity of rangeland and provides an estimate of the amount of carbon stored in the vegetation. Thus, monitoring seasonal AGB is important for understanding and managing rangelands’ status and resilience. This study assesses the impact of seasonal dynamics and fire on biophysical parameters using Sentinel-1 (S1) and Sentinel-2 (S2) image data in the mesic rangeland of Limpopo, South Africa. Six sites were selected (3/ area), with homogenous vegetation (10 plots/site of 30m2). The seasonal measurements of LAI and biomass were undertaken in the early summer (December 2020), winter (July–August 2021), and late summer (March 2022). Two regression approaches, random forest (RF) and stepwise multiple linear regression (SMLR), were used to estimate seasonal AGB. The results show a significant difference (p < 0.05) in AGB seasonal distribution and occurrence between the fire (ranging from 0.26 to 0.39 kg/m2) and non-fire areas (0.24–0.35 kg/m2). In addition, the seasonal predictive models derived from random forest regression (RF) are fit to predict disturbance and seasonal variations in mesic tropical rangelands. The S1 variables were excluded from all models due to high moisture content. Hence, this study analyzed the time series to evaluate the correlation between seasonal estimated and field AGB in mesic tropical rangelands. A significant correlation between backscattering, AGB and ecological parameters was observed. Therefore, using S1 and S2 data provides sufficient data to obtain the seasonal changes of biophysical parameters in mesic tropical rangelands after disturbance (fire) and enhanced assessments of critical phenology stages.
The influence of fire presence and absence on grass species composition and species richness at Mountain Zebra National Park
(AOSIS, 2023-04) Munyai, Nthabeliseni; Ramoelo, Abel; Adelabu, Samuel; Bezuidenhout, Hugo
It is well known that fire is a common driver in many biomes and it plays a vital role in maintaining ecosystem functioning in many South African biomes. This ecosystem process is an important determinant of plant community composition and diversity, and can result in changes in structural composition and ecosystem functioning. The main objectives of this study are to determine the influence of fire on grass species richness, diversity and composition in Mountain Zebra National Park. Using satellite imagery, the park’s fire history was determined between 2000 and 2020. Eighty plots (approximately 20 m × 20 m; >100 m apart) were laid out purposively across different fire regimes. There was no significant difference in both species richness and diversity in burned and unburned sites. However, there was a difference in species composition between burned and unburned sites and between different fire frequencies. The unburned site had higher moribund material and unpalatable grasses compared to the burned area. CONSERVATION IMPLICATIONS : The results of this study will help in the completion of the fire management plan for the park which will enable conservation managers to make better decisions with regard to fire management in mountainous grassland at Mountain Zebra National Park. Consequently, this will lead to improved veld condition and vegetation structure.
Effects of environmental factors on plant productivity in the mountain grassland of the Mountain Zebra National Park, Eastern Cape, South Africa
(MDPI, 2023-12) Munyai, Nthabeliseni; Ramoelo, Abel; Adelabu, Samuel; Bezuidehout, Hugo; Sadiq, Hassan
The relationship between plant productivity, measured according to biomass and species richness, is a fundamental focal point in community ecology, as it provides the basis for understanding plant responses or adaptive strategies. Although studies have been conducted on plant biomass and environmental factors, research concerning mountainous grassland areas is scarce. Therefore, the aim of the present study was to examine the influence of environmental factors on aboveground plant biomass in the mountainous grassland of the Mountain Zebra National Park, South Africa. Biomass distribution was uneven within the park, owing to certain species having relatively higher biomass values. These differences may be attributed to the chemical and physical properties of the soil, including carbon and nitrogen content, soil pH, and soil texture (sand, silt, and coarse fragments). A disc pasture meter was used to collect biomass data. Multiple regression analysis revealed that most environmental factors did not significantly influence plant biomass. The only environmental factor influencing plant biomass was soil pH; the influences of other factors were not statistically significant. The results of this study elucidate the interactions of environmental factors with plant biomass. Future research could investigate how environmental factors influence plant biomass, both below and above the ground in mountainous grassland.
Development of the grass LAI and CCC remote sensing-based models and their transferability using sentinel-2 data in heterogeneous grasslands
(Taylor and Francis, 2023) Tsele, Philemon; Ramoelo, Abel; Qabaqaba, Mcebisi; philemon.tsele@up.ac.za
Please read abstract in the article.
Diversifying modelling techniques to disentangle the complex patterns of species richness and diversity in the protected afromontane grasslands
(Springer, 2023-03) Mashiane, K.K. (Katlego); Ramoelo, Abel; Adelabu, Samuel
Ecological research has focused on the importance of environmental factors on spatial biodiversity variations and organisation. This is important because of scant conservation resources. We used stepwise backward selection and random feature selection (RFE) to identify a parsimonious model that can predict species richness and diversity metrics in response to three models; biotic, abiotic, and topo-edaphic. Our results show that both metrics are good predictors of one another, mainly because species diversity is a combination of species richness and abundance, and further highlights the importance of biotic variables in predicting species distribution. The two modelling techniques selected soil texture and its interactions with topographic variables as the most important variables. However, random forest performed worse than multiple linear regression in the prediction of diversity metrics. This research highlights the importance of topographically controlled edaphic factors as drivers of species richness and diversity in mountainous grasslands where topography inherently controls the geomorphic, hydrological, and, as a result, ecological processes.
Determination of soil electrical conductivity and moisture on different soil layers using electromagnetic techniques in irrigated arid environments in South Africa
(MDPI, 2023-05) Ratshiedana, Phathutshedzo Eugene; Abd Elbasit, Mohamed A. M.; Adam, Elhadi; Chirima, Johannes George; Liu, Gang; Chirima, Johannes George
Precise adjustments of farm management activities, such as irrigation and soil treatment according to site-specific conditions, are crucial. With advances in smart agriculture and sensors, it is possible to reduce the cost of water and soil treatment inputs but still realize optimal yields and highprofit returns. However, achieving precise application requirements cannot be efficiently practiced with spatially disjointed information. This study assessed the potential of using an electromagnetic induction device (EM38-MK) to cover this gap. An EM38-MK was used to measure soil apparent electrical conductivity (ECa) as a covariate to determine soil salinity status and soil water content θ post irrigation at four depth layers (Hz: 0–0.25 m; Hz: 0–0.75 m; Vz: 0.50–1 m). The inverse distance weighting method was used to generate the spatial distribution thematic layers of electrical conductivity. The statistical measures showed an R2 = 0.87; r > 0.7 and p ≤ 0.05 on correlation of ECa and SWC. Based on the South African salinity class of soils, the area was not saline ECa < 200 mS/m. The EM38-MK can be used to estimate soil salinity and SWC variability using ECa as a proxy, allowing precise estimations with depths and in space. These findings provide key information that can aid in irrigation scheduling and soil management.
Estimating mountainous plant species richness and diversity for monitoring global change in a protected grassland park
(Wiley, 2023-09) Mashiane, K.K. (Katlego); Ramoelo, Abel; Adelabu, Samuel; Damenae, Ernest
Assessments of species diversity and richness are essential to understand present ecological and biodiversity conditions for effective conservation management strategies. Biodiversity indicators determine rangeland health and response to grazing, fire regimes and climate change. This research examined species richness, diversity and composition in a protected mountainous grassland. Two data sets, both collected from a 30 × 30 m plot, with similar species composition and cover were combined. One data set was collected using a 100-step point survey and the other from a series of 16 plots. A single-factor analysis of variance was used to test if the mean species richness and diversity of the sites differed across the study area. Species accumulation curves were used to determine the relationship between species richness and the number of sampling units per site. The results from fitting a species–area equation showed that the estimated maximum species richness was slightly greater than the observed species pool in all sites, meaning that the sampling units were not adequate (albeit by small margins) to capture all vascular plant species in the sites. Diversity metrics could, thus, be used to monitor species change within grassland plant communities.
Public health awareness on bat rabies among bat handlers and persons residing near bat roosts in Makurdi, Nigeria
(MDPI, 2022-08-26) Ameh, Veronica Odinya; Chirima, Johannes George; Quan, Melvyn; Sabeta, Claude Taurai
Rabies is a neglected disease endemic in Asia and Africa but is still a significant public and veterinary health threat. Whilst a key delicacy for the local diet, bats are a natural reservoir host for many viral zoonotic agents including lyssaviruses, the causative agent of rabies. Studies on knowledge and practices linked to the disease will help to identify gaps and define preventive strategies that may subsequently result in a reduction and the potential elimination of human rabies. In order to assess the public health awareness of bat rabies among specific population groups in Makurdi (Nigeria), structured questionnaires (n = 154) were administered by face-to-face interviews to bat handlers and persons residing near bat roost sites. A total of 59.7% of the respondents were persons residing near bat roost sites, 13% were bat hunters, 25.3% were bat meat consumers and 1.9% were university researchers. Only 6.5% of respondents reported using some form of personal protective equipment (PPE) ranging from hand gloves, face/nose masks and protective boots to lab coats/coveralls while handling bats, whilst the majority (93.5%) did not use any form of PPE. With a mean knowledge score of 8.34 out of a possible 12 points, 50.6% of respondents had good knowledge of bats and their disease-carrying potential, 39.6% had fair knowledge, while 9.7% had poor knowledge. Log linear models showed significant associations between knowledge score and level of education, as well as knowledge score and occupation. The latter highlights the requirement to enhance public education among bat handlers and persons residing near bat roosts on the need to protect themselves better, while handling bats particularly during processing of bats for food and on steps to take when exposed to bites from bats.
Apportioning human-induced and climate-induced land degradation : a case of the greater Sekhukhune district municipality
(MDPI, 2023-03) Kgaphola, Motsoko Juniet; Ramoelo, Abel; Odindi, John; Kahinda, Jean-Marc Mwenge; Seetal, Ashwin; abel.ramoelo@up.ac.za
Land degradation (LD) is a global issue that affects sustainability and livelihoods of approximately 1.5 billion people, especially in arid/semi-arid regions. Hence, identifying and assessing LD and its driving forces (natural and anthropogenic) is important in order to design and adopt appropriate sustainable land management interventions. Therefore, using vegetation as a proxy for LD, this study aimed to distinguish anthropogenic from rainfall-driven LD in the Greater Sekhukhune District Municipality from 1990 to 2019. It is widely established that rainfall highly correlates with vegetation productivity. A linear regression was performed between the Normalized Difference Vegetation Index (NDVI) and rainfall. The human-induced LD was then distinguished from that of rainfall using the spatial residual trend (RESTREND) method and the Mann–Kendall (MK) trend. RESTREND results showed that 11.59% of the district was degraded due to human activities such as overgrazing and injudicious rangeland management. While about 41.41% was degraded due to seasonal rainfall variability and an increasing frequency of droughts. Climate variability affected vegetation cover and contributed to different forms of soil erosion and gully formation. These findings provide relevant spatial information on rainfall or human-induced LD, which is useful for policy formulation and the design of LD mitigation measures in semi-arid regions.
Integrating random forest and synthetic aperture radar improves the estimation and monitoring of woody cover in indigenous forests of South Africa
(Springer, 2023-03) Qabaqaba, Mcebisi; Naidoo, Laven; Tsele, Philemon; Ramoelo, Abel; Cho, Moses Azong; philemon.tsele@up.ac.za
Please read abstract in article.
Ecosystem service valuation for a critical biodiversity area : case of the Mphaphuli community, South Africa
(MDPI, 2022-09-30) Musetsho, Khangwelo Desmond; Chitakira, Munyaradzi; Ramoelo, Abel
The study of ecosystem services and the valuation of their contribution to human wellbeing is gaining increasing interest among scientists and decision-makers. The setting of this study was a critical biodiversity area on a portion of land largely presided over by a traditional leadership structure on behalf of a relatively poor local community in South Africa. The study identified several ecosystem services and performed an economic valuation of these services, and their importance both locally and globally using the Co$ting Nature V3 tool. The study identified ecosystem services such as the regulation of air quality, regulation of natural hazards, and provision of water. The economic valuation was carried out for all identified ecosystem services, realised and potential. The total realised economic value of ecosystem services was found to be US$528,280,256.00, whereas hazard mitigation potential was found to be US$765,598,080.00 across the study area. Artisanal fisheries were the least valued ecosystem service at US$5577.54. The values of the ecosystem services differed across the eleven land use land cover classes. The outcomes of the study focused on a very local scale, which was a departure from other studies previously carried out in South Africa, which focused more on the identification and valuation of regional and national scale ecosystem services.
A reflection about the recent Koedoe publications (Volume 64, No 1, 2022)
(AOSIS, 2022) Ramoelo, Abel
No abstract available.
Using mulching to reduce soil surface temperature to facilitate grass production
(Elsevier, 2022-12) Mangani, Tshepiso; Mangani, Robert; Chirima, Johannes George; Khomo, Lesego; Truter, Wayne Frederick
Ecosystems in semi-arid and arid Southern Africa experience high temperatures which translate to extremely hot soil surface temperatures. High soil surface temperatures lead to a decrease in seed germination and consequently less plant cover in these areas. To facilitate maintenance of optimum plant cover, soil surface temperature should be moderated with appropriate mitigation techniques. Temperature variations in low (kg.0.5 m 3) and high density (1 kg m 3) brush packing treatments were compared to bare soil. We also measured the grass productivity (g.m 2) against the effect of temperature in the three treatments. iButtons® were used to log soil surface temperature every hour for seven months. Daily and nightly temperatures of the hottest months were compared amongst the three treatments. Mid-day temperatures, corresponding to peak heat stress were also compared between the three treatments. There was a significant difference (p < 0.01) in soil surface temperature between the three treatments. The high density treatment was the most buffered against temperature variation, when compared to the bare soil. Grass production was generally higher in the high density treatment. Productivity can be increased by mulching the soil with brush packing as this will improve soil surface conditions such as moderating abrupt changes in temperatures to assist plant growth.

Browse

Recent Submissions