Long-term Time Series Data Research Articles

Do trends in Bangladeshi rice yields support Conway’s hypotheses about the consequences of modern agroecosystems?

Gordon Conway postulated that in comparison to traditional agroecosystems, crop yields from modern agroecosystems are: (1) higher; (2) less sustainable; and (3) more variable, particularly in the early adoption stages. The existing literature predates more recent developments and does not employ long-term time series data to identify time phases using any econometric tests for structural breaks. This paper fills this gap by investigating the applicability of Conway’s hypotheses to Bangladeshi rice yields over sixty years examining trends, their sustainability, and their variability. We find mixed support for Conway’s hypotheses. Rice yields rose substantially following the Green Revolution. Overall, higher yields have been sustained. However, they have now virtually plateaued, and various factors (including environmental and ecological) have increased the risk of these yields becoming unsustainable. Until recently, absolute variations in Bangladesh’s rice yields were found to be higher than before the Green Revolution. This supports Conway’s third hypothesis. However, the relative variability of Bangladesh’s rice yields has consistently fallen since the Green Revolution. We highlight the trio of general factors determining rice yields and underscored the need for holistic analysis. Non-economists often overlook economic factors while economists often do not pay adequate attention to eco-environmental factors.

Citizen Scientists Contribute to Real-Time Monitoring of Lake Water Quality Using 3D Printed Mini Secchi Disks

Citizen science aims to mobilise the general public, motivated by curiosity, to collect scientific data and contribute to the advancement of scientific knowledge. In this article, we describe a citizen science network that has been developed to assess the water quality in a 100 km long tropical lake-estuarine system (Vembanad Lake), which directly or indirectly influences the livelihood of around 1.6 million people. Deterioration of water quality in the lake has resulted in frequent outbreaks of water-associated diseases, leading to morbidity and occasionally, to mortality. Water colour and clarity are easily measurable and can be used to study water quality. Continuous observations on relevant spatial and temporal scales can be used to generate maps of water colour and clarity for identifying areas that are turbid or eutrophic. A network of citizen scientists was established with the support of students from 16 colleges affiliated with three universities of Kerala (India) and research institutions, and stakeholders such as houseboat owners, non-government organisations (NGOs), regular commuters, inland fishermen, and others residing in the vicinity of Vembanad Lake and keen to contribute. Mini Secchi disks, with Forel-Ule colour scale stickers, were used to measure the colour and clarity of the water. A mobile application, named “TurbAqua,” was developed for easy transmission of data in near-real time. In-situ data from scientists were used to check the quality of a subset of the citizen observations. We highlight the major economic benefits from the citizen network, with stakeholders voluntarily monitoring water quality in the lake at low cost, and the increased potential for sustainable monitoring in the long term. The data can be used to validate satellite products of water quality and can provide scientific information on natural or anthropogenic events impacting the lake. Citizens provided with scientific tools can make their own judgement on the quality of water that they use, helping toward Sustainable Development Goal 6 of clean water. The study highlights potential for world-wide application of similar citizen-science initiatives, using simple tools for generating long-term time series data sets, which may also help monitor climate change.

Estimation of ecological water supplement for typical bird protection in the Yellow River Delta wetland

With abundant resources in the wetland, the Yellow River Delta provides important habitats for various kinds of rare bird species. However, due to climate change and human water withdrawal activities, no-flow events of the Yellow River occurred frequently since the 1970s, resulting in serious wetland degradation and biodiversity losses. Ecological water supplement has been regarded as one of the most effective measures to cope with these environmental problems. Accordingly, how much water is required for the ecological restoration becomes an essential question to be answered. Since the primary function of the Yellow River Delta Natural Reserve (YRDNR), the typical wetland in the Yellow River Delta, is to provide suitable habitats for various rare birds, the purpose of this paper is to propose a method based on water balance analysis to specify monthly ecological water supplement for the habitats of typical birds in the YRDNR. Specifically, three typical birds were selected as the protection objects, including Grus japonensis, Ciconia boyciana, and Larus saundersi. Monthly water supplement was determined by monthly long-term time series data (2003–2018) of evapotranspiration product derived from remote sensing and meteorological data (precipitation and pan evaporation) acquired from National Meteorological Information Center. The results showed that the annual ecological water supplement for 10 km2 ideal habitats was 666.17×104 m3, 1165.22×104 m3, and 664.48×104 m3 for G. japonensis, C. boyciana, and L. saundersi, respectively. Besides, it was found that annual precipitation generally had a significant negative relation with annual ecological water supplement, which suggested that local precipitation should be considered as a key factor in determining the amount of annual water supplement. The frequency analysis was then implemented to provide reasonable scheme of actual ecological water supplement under different precipitation scenarios. The results indicated that annual ecological water supplement for the whole YRDNR was 1.81×108 m3, 1.94×108 m3, 2.10×108 m3 and 2.25×108 m3 respectively under wet, average, dry and extreme dry conditions. If water storage measurements are implemented to store surplus precipitation under rainy season, the average volume would reduce by nearly 10%.

ARFIMA Modelling for Tectonic Earthquakes in The Maluku Region

Maluku Province is one of the regions in Indonesia with a very active and very prone earthquake intensity because it is a meeting place for 3 (three) plates, namely the Eurasian, Pacific and Australian plates. In the last 100 years, the history of tectonic earthquakes with tsunamis that occurred in Indonesia was 25-30% occurring in the Maluku Sea and Banda Sea. Based on this fact, this study aims to analyze the incidence of tectonic earthquakes that occurred in the Maluku region and its surroundings using the Autoregressive Fractionally Integrated Moving Averages (ARFIMA) model which has the ability to explain long-term time series data (long memory). The results of the research data analysis show that the best model for predicting the number of tectonic earthquakes that occur in Maluku and its surroundings is ARFIMA (0; 0.712; 1) with an MSE value of 0.1156. Meanwhile, the best model for predicting the average magnitude of the number of tectonic earthquakes that occurred in Maluku and its surroundings is ARFIMA (0; -3,224 x 10-9; 1) with an MSE value of 0.01237. Based on the two best models, the prediction results obtained from the number of tectonic earthquakes and the average magnitude of the number of tectonic earthquakes that occurred in Maluku and its surroundings for the next three periods, namely the first period there were 31 tectonic earthquakes with an average magnitude of 4.38481 SR. the second period there were 32 tectonic earthquakes with an average magnitude of 4.38407, and the third period there were 32 tectonic earthquakes with an average magnitude of 4.38333.

Warming drives cryptic declines of amphibians in eastern Brazil

The global amphibian extinction crisis has challenged scientists around the globe for decades. Robust long-term time-series data to detect such population declines are key to understanding how species respond to climatic variations. Thus, we assessed the species' richness and abundance of an anuran assemblage in the Atlantic Forest in Brazil, where average temperatures were rising over the last 30 years. A series of models were applied to a long time-series of amphibian standardized monitoring (2010–2019) to assess the impact of a combination of a series of local climatic factors, and species life-history traits, to detect any variation on species abundance and richness. We observed a significant reduction in species abundance and a trend of reduction in species richness over time. Temperature had a stronger influence on species abundance and richness compared to precipitation. Furthermore, the most resilient species were generalist and peri-anthropic ones. Although more robust data is needed to pinpoint climate change-related variation on species decline, anticipating amphibian sensitivity to climate shift, and the limited time to act, our results are concerning. We suggest further studies to evaluate other aspects that could be influencing this local population decline and highlights that a broader amphibian monitoring program should be implemented in the Atlantic forest to detect other equally silent gradual declines.

A Long-Term Terrestrial Laser Scanning Measurement Station to Continuously Monitor Structural and Phenological Dynamics of Boreal Forest Canopy.

The terrestrial laser scanner (TLS) has become standard technology for vegetation dynamics monitoring. TLS time series have significant underlying application in investigating structural development and dynamics on a daily and seasonal scale. However, the high potential of TLS for the monitoring of long-term temporal phenomena in fully grown trees with high spatial and temporal resolution has not yet been fully explored. Automated TLS platforms for long-term data collection and monitoring of forest dynamics are rare; and long-term TLS time series data is not yet readily available to potential end-user, such as forestry researchers and plant biologists. This work presents an automated and permanent TLS measurement station that collects high frequency and high spatial resolution TLS time series, aiming to monitor short- and long-term phenological changes at a boreal forestry field station (0.006° angular resolution, one scan per hour). The measurement station is the first of its kind considering the scope, accuracy, and length of the time series it produces. The TLS measurement station provides a unique dataset to monitor the 3D physical structure of a boreal forest, enabling new insights into forest dynamics. For instance, the information collected by the TLS station can be used to accurately detect structural changes in tree crowns surrounding the station. These changes and their timing can be linked with the phenological state of plants, such as the start of leaf-out during spring growing season. As the first results of this novel station, we present time series data products collected with the station and what detailed information it provides about the phenological changes in the test site during the leaf sprout in spring.

An ecological stability-oriented model for the conjunctive allocation of surface water and groundwater in oases in arid inland river basins

Abstract With the continuous development of the population and social economy, the spatial and temporal distribution of water resources in arid inland river basins is severely uneven, and there is a sharp contradiction between agricultural water use and ecological water use. Irrational development and utilization of water resources has led to many problems, such as shrinking oases and drying lakes. To solve this problem, this study proposes a multiobjective, multiwater-source, ecological stability-oriented double-layer model for optimal allocation of water resources based on the large-scale system decomposition–coordination principle, the water balance principle, and a water supply and demand forecasting model. This model can resolve the contradiction between agricultural water use and ecological water use by optimizing and adjusting the crop planting structure, industrial structure, and the amount of water allocated to and groundwater level in each region and thereby achieve ecological stability and restoration of oases. The developed model was applied to the Heihe River Basin in an inland region of Northwest China. The long-term time series data of 2000–2016 were used to construct and calibrate the model. Finally, the practical ecological stability-oriented plan for conjunctive allocation of surface water and groundwater in different plan years was proposed. This model enriches the research results related to the conjunctive allocation of surface water and groundwater and provides a reference for the ecological restoration of oases in arid inland river basins.

Statistical analysis of structural changes in agriculture

Digital transformations in the Russian economy lead to significant internal changes in the formation of industrial relations in all types of activities and, in particular, in the agricultural sector. This arouses particular interest in the study of structural shifts in agricultural sectors that have occurred under the influence of external economic factors and changes in applied technologies. The study, based on the analysis of long-term time series and panel data, revealed significant structural changes in the production of agricultural products in general and by types of products in the context of producer categories and regions of the Russian Federation. Methods for identifying trends in time series made it possible to establish the increasing role of large and medium-sized agricultural enterprises in providing food to the country’s population during the redistribution of land resources towards the household sector with low efficiency of their use. In agricultural production, the use of digital technologies is currently of a point nature, which leads to the emergence of leading regions with innovative large-format production identified in this study. The analyzed structural changes will make it possible to determine the directions in clarifying the system of indicators for monitoring digitalization processes in agriculture.

Long-term warming destabilizes aquatic ecosystems through weakening biodiversity-mediated causal networks.

Understanding how ecosystems will respond to climate changes requires unravelling the network of functional responses and feedbacks among biodiversity, physicochemical environments, and productivity. These ecosystem components not only change over time but also interact with each other. Therefore, investigation of individual relationships may give limited insights into their interdependencies and limit ability to predict future ecosystem states. We address this problem by analyzing long-term (16-39years) time series data from 10 aquatic ecosystems and using convergent cross mapping (CCM) to quantify the causal networks linking phytoplankton species richness, biomass, and physicochemical factors. We determined that individual quantities (e.g., total species richness or nutrients) were not significant predictors of ecosystem stability (quantified as long-term fluctuation of phytoplankton biomass); rather, the integrated causal pathway in the ecosystem network, composed of the interactions among species richness, nutrient cycling, and phytoplankton biomass, was the best predictor of stability. Furthermore, systems that experienced stronger warming over time had both weakened causal interactions and larger fluctuations. Thus, rather than thinking in terms of separate factors, a more holistic network view, that causally links species richness and the other ecosystem components, is required to understand and predict climate impacts on the temporal stability of aquatic ecosystems.

An open-source database model and collections management system for fish scale and otolith archives

Scales and otoliths (ear stones) from fish are routinely sampled for age estimation and fisheries management purposes. Growth records from scales and otoliths can be used to generate long-term time series data, and in combination with environmental data, can reveal species specific population responses to a changing climate. Additionally, scale and otolith microchemical data can be utilized to investigate fish habitat usage. A common problem associated with biological collections, is that while sample intake grows, long-term physical storage is rarely a priority, and much of the sampling took place before the advent of open-access digital infrastructure. Material is often collected to meet short-term objectives and resources are seldom committed to maintaining and archiving collections. As a consequence, precious samples are frequently stored in many different and unsuitable locations, and may become lost or separated from associated metadata. The Marine Institute's ecological research station in in Newport, Co. Mayo, Ireland, holds a multi-decadal (1928–2020) collection of scales and otoliths from various fish species, gathered from many geographic locations. Here we present an open-source database model and archiving system to consolidate and digitize this collection, and show how this case study infrastructure could be used for other biological sample collections. The system utilizes the FAIR (Findable Accessible Interoperable and Reusable) open-data principles, and includes a physical repository, sample metadata catalogue, and image library.

Long-term discolouration modelling for cast iron mains

ABSTRACT Water companies have been working to introduce strategies to reduce discolouration customer contacts via non-specialist ‘business as usual’ practices. A greater understanding of discolouration material behaviour, however, is still needed to accurately inform the mobilisation response and regeneration rates in mains of different materials. The Variable Condition Discolouration Model (VCDM) that tracks both accumulation and mobilisation processes has been validated in some pipe materials using long-term time series data. This paper investigates calibration for a 15 km cast iron (CI) main, using daily turbidity responses with VCDM parameter sensitivity and temporal stability investigated using a statistical approach comparing three periods of the data. Results highlight the VCDM as widely applicable to determine long-term discolouration behaviour and improve behavioural understanding. In this case, analysis of different time periods indicates flow-conditioning not only improves network resilience but can also reduce mobilisation rates and discolouration risk.

Investigating the relationship between climate, stand age, and temporal trends in masting behavior of European forest trees.

Masting—temporally variable seed production with high spatial synchrony—is a pervasive strategy in wind‐pollinated trees that is hypothesized to be vulnerable to climate change due to its correlation with variability in abiotic conditions. Recent work suggests that aging may also have strong effects on seed production patterns of trees, but this potential confounding factor has not been considered in previous times series analysis of climate change effects. Using a 54 year dataset for seven dominant species in 17 forests across Poland, we used the proportion of seed‐producing trees (PST) to contrast the predictions of the climate change and aging hypotheses in Abies alba, Fagus sylvatica, Larix decidua, Picea abies, Pinus sylvestris, Quercus petraea, and Quercus robur. Our results show that in all species, PST increased over time and that this change correlated most strongly with stand age, while the standardized precipitation–evapotranspiration index, a measure of drought, contributed to temporal trends in PST of F. sylvatica and Q. robur. Temporal variability of PST also increased over time in all species except P. sylvestris, while trends in temporal autocorrelation and among‐stand synchrony reflect species‐specific masting strategies. Our results suggest a pivotal role of plant ontogeny in driving not only the extent but also variability and synchrony of reproduction in temperate forest trees. In a time of increasing forest regrowth in Europe, we therefore call for increased attention to demographic effects such as aging on plant reproductive behavior, particularly in studies examining global change effects using long‐term time series data.

Ecosystem modelling of data-limited fisheries: How reliable are Ecopath with Ecosim models without historical time series fitting?

Long-term time series data are not available for many of the African Great Lakes. This precludes fitting ecosystem model parameters to time series data, and we do not know how reliable non-fitted models are compared to fitted ones in terms of predicting consequences of alternative management strategies. To investigate this, we generate a historical Ecopath with Ecosim (EwE) model for Lake Victoria (East Africa), fitted to time series data (1980–2015), and a present-day EwE model (representing average conditions for the period 2010–2015). We do scenario simulations using the present-day model and the comparable 2015 end-state of the historical model, and test if incorporating information on short-term biomass trends by adjusting biomass accumulation (BA) parameter in the present-day model increases its reliability. We find that there are differences in model predictions, but those differences can be lessened by adjusting BA terms in the present-day model to reflect biomass trends from short-term empirical data. We also compare the models with and without fitted vulnerability parameters. The models generally give comparable results for the dominant commercial fisheries at low fishing pressure; when fishing mortality is increased, the models give variable predictions. This study adds to the current understanding of the limitations of EwE models that are not challenged to reproduce long-term historical fishery responses to perturbations. We conclude that for the less productive groups, as well as groups that suffer heavy mortality (either due to predation or fisheries), it may be appropriate to use negative BA as first draft assumption in present-day models.

A Synthesis of Evidence of Drivers of Amphibian Declines

Early calls for robust long-term time series of amphibian population data, stemming from discussion following the first World Congress of Herpetology, are now being realized after 25 yr of focused research. Inference from individual studies and locations have contributed to a basic consensus on drivers of amphibian declines. Until recently there were no large-scale syntheses of long-term time series data to test hypotheses about the generality of factors driving population dynamics at broad spatial scales. Through the U.S. Geological Survey's Powell Center for Analysis and Synthesis, we brought together a group of scientists to elucidate mechanisms underlying amphibian declines in North America and Europe. We used time series of field data collected across dozens of study areas to make inferences with these combined data using hierarchical and spatial models. We bring together results from four syntheses of these data to summarize our state of knowledge of amphibian declines, identify commonalities that suggest further avenues of study, and suggest a way forward in addressing amphibian declines—by looking beyond specific drivers to how to achieve stability in remaining populations. The common thread of the syntheses is that declines are real but not ubiquitous, and that multiple factors drive declines but the relative importance of each factor varies among species, populations, and regions. We also found that climate is an important driver of amphibian population dynamics. However, the direction and magnitude of sensitivity to change vary among species in ways unlikely to explain overall rates of decline. Thirty years after the initial identification of a major catastrophe for global biodiversity, the scientific community has empirically demonstrated the reality of the problem, identified putative causes, provided evidence of their impacts, invested in broader-scale actions, and attempted meta-analyses to search out global drivers. We suggest an approach that focuses on key demographic rates that may improve amphibian population trends at multiple sites across the landscape.

Butterfly phenology in Mediterranean mountains using space-for-time substitution.

Inferring species' responses to climate change in the absence of long‐term time series data is a challenge, but can be achieved by substituting space for time. For example, thermal elevational gradients represent suitable proxies to study phenological responses to warming. We used butterfly data from two Mediterranean mountain areas to test whether mean dates of appearance of communities and individual species show a delay with increasing altitude, and an accompanying shortening in the duration of flight periods. We found a 14‐day delay in the mean date of appearance per kilometer increase in altitude for butterfly communities overall, and an average 23‐day shift for 26 selected species, alongside average summer temperature lapse rates of 3°C per km. At higher elevations, there was a shortening of the flight period for the community of 3 days/km, with an 8.8‐day average decline per km for individual species. Rates of phenological delay differed significantly between the two mountain ranges, although this did not seem to result from the respective temperature lapse rates. These results suggest that climate warming could lead to advanced and lengthened flight periods for Mediterranean mountain butterfly communities. However, although multivoltine species showed the expected response of delayed and shortened flight periods at higher elevations, univoltine species showed more pronounced delays in terms of species appearance. Hence, while projections of overall community responses to climate change may benefit from space‐for‐time substitutions, understanding species‐specific responses to local features of habitat and climate may be needed to accurately predict the effects of climate change on phenology.

Groundwater level prediction using genetic programming: the importance of precipitation data and weather station location on model accuracy

Groundwater (GW) level prediction is important for effective GW resource management. It is hypothesized that using precipitation data in GW level modelling will increase the overall accuracy of the results and that the distance of the observation well to the weather station (where precipitation data are obtained) will affect the model outcome. Here, genetic programming (GP) was used to predict GW level fluctuation in multiple observation wells under three scenarios to test these hypotheses. In Scenario 1, GW level and precipitation data were used as input data. Scenarios 2 only had GW level data as inputs to the model, and in Scenarios 3, only precipitation data were used as inputs. Long-term GW level time series data covering a period of 8 years were used to train and test the GP model. Further, to examine the effect of data from previous time periods on the accuracy of GW level prediction, 12 models with input data up to 12 months prior to the current period were investigated. Model performance was evaluated using two criteria, coefficient of determination (R2) and root mean square error (RMSE). Results show that when predicting GW levels through GP, using GW level and precipitation data together (Scenario 1) produces results with higher accuracy compared to only using GW level (Scenario 2) or precipitation data (Scenario 3). Additionally, it was found that model accuracy was highest for the well located closest to the weather station (where precipitation data were collected), demonstrating the importance of weather station location in GW level prediction. It was also found that using data from up to six previous time periods (months) can be the most efficient combination of input data for accurate predictions. The findings from this study are useful for increasing the prediction accuracy of GW level variations in unconfined aquifers for sustainable GW resource management.

Interannual variability of wind stress curl in the Black Sea and its response to changes in prevailing wind frequency

The purpose of this study is to identify trends in the long-term variability of the wind stress curl in the Black Sea and to analyze its response to changes in the direction of the prevailing wind flow over the sea. NCEP/NCAR reanalysis (1948–2016) and ERA Interim reanalysis (1979–2016) wind data at 10-m height are used. The temporal and spatial characteristics of the wind stress curl are compared by using these reanalyses with various spatial resolutions. Interannual changes in the wind stress curl are examined with long-term time series data of NCEP/NCAR reanalysis. The wind stress curl seasonal cycle, the dependence of its magnitude on wind direction, and the frequency of wind direction are in good agreement for the two reanalyses. There are multi-year periods of predominant positive and negative anomalies of the curl according to annual mean and winter data. The cyclonic wind stress curl increases in the late 1960s and early 1970s and weakens at the end of the 1990s. There is a weak positive trend in the curl time series in summer. The long-term variations of the wind stress curl are related to changes in the wind frequency of certain directions. High values of positive correlation coefficients have been obtained between the time series of the basin-averaged wind stress curl and the frequency of northeastern, eastern winds, and negative coefficients for the frequency of southwestern, western winds.

Differential impact of remotely sensed dry dekads on maize yield in Zimbabwe

Moisture requirements for crops vary with the crop phenological stage. In this study, we test whether and in what way the sequence of the dry dekads derived from Vegetation Condition Index (VCI) recorded during the growing season affect maize yield. VCI was calculated from eModis NDVI long-term time series data ranging from 2001 to 2016. The Kruskal–Wallis test was used to test whether there were statistical differences in yield among administrative units with different sequences. Pairwise comparisons indicated that sequences with higher number of dry dekads have significantly lower maize yield than those with few dry dekads. Furthermore, sequences with dry dekads in the beginning of the vegetative stage reduce yield more than those that have the dry dekads at the end. Based on these results, we conclude that the phenological stage at which the dry dekads occur has a significant effect on maize yield.

German solar power generation data mining and prediction with transparent open box learning network integrating weather, environmental and market variables

A compiled dataset of hourly-averaged solar power generation (MW) for Germany in 2016 integrates eight influencing weather, environmental and market price variables (8784 data records including 5012 non-zero generation periods). It provides valuable insight to solar power variations over the course of one year. The dataset is evaluated with the transparent open box (TOB) learning network for data mining and prediction purposes. This algorithm provides accurate and repeatable MW predictions and enables detailed evaluation of the key influencing variables on each hourly data record. TOB Stage 1 applies a data matching routine driven by the squared errors between the independent variables. TOB Stage 2 applies a customized memetic firefly optimizer to minimize the root mean squared error (RMSE) for MW predictions over subsets and/or the full dataset. TOB achieves high prediction accuracy (average of five cases RMSE = 1044.4 MW; R2 = 0.975) using tuning subsets of only ∼303 data records (∼6% of the full dataset). The dataset displays some significant MW prediction outliers that are readily identified and explained individually by the TOB algorithm’s data mining capabilities. A slightly filtered dataset (4918 data records excluding 94 outlier data records) improves MW prediction accuracy (average of five cases RMSE = 936.1 MW; R2 = 0.980). Whereas the prediction outliers are readily segregated as a separate subset for more detailed evaluation. The TOB algorithm’s combined machine-learning and data mining capabilities provide valuable insight to the dataset and the influences of its independent variables. The algorithm couples high-prediction accuracy and detailed evaluation of long-term and short-term time series data and for spatial scales varying from country level to individual solar farms.

Prospects of the Nigeria Brewery Industry

This study analyses the economics and determinants of a brewery industry (Guinness Nigeria Limited) with a view of establishing the future of breweries in Nigeria. The specific objectives were to estimate the factors influencing products production and determine the viability of products manufactured by the company. Multiple regression and the ordinary least square techniques were used to analyse the long-term (2006 - 2015) annual time series data observed for the study. The results showed that the major determinant of Stout, Harp and Malt production is demanded. As a significant input, it positively influences product production. The observed values of regression coefficients (R2) are 0.99349 (for Stout), 0.90981 (for Harp), and 0.99498 (for Malt), which indicates that they are reliable for determining the future of Guinness Nigeria Ltd. The results also showed that logarithm of demand for Stout production (LstoutD) of 13.5277, LharpD of 21.8439 and LmaltD of 12.2653 were projected respectively up to the year 2025, an indication that brewery production in the state is viable and that future is bright.