Adaptive Land Management Research Articles

Factors influencing private woodland owners’ land management decisions on Lyme disease mitigation in Maine

Tick-borne diseases (TBDs), including Lyme disease (LD), have become of great concern in the United States due to their impacts on individuals, communities, local businesses, and the national economy. Methods to mitigate the impact of TBDs often target tick habitat and wildlife hosts, yet the effectiveness of these methods is contingent on large-scale adoption by private woodland owners (PWOs) and agencies. As research has shown, adaptive land management methods may have a significative impact on LD mitigation in forest landscapes. Therefore, understanding how PWOs’ current and future decisions on land management methods may contribute to reducing human exposure to TBDs is critical. In this paper we characterize the factors influencing PWOs’ land management decisions and develop an improved understanding of their perception of LD as it relates to land management. We synthesize this information by identifying opportunities for and barriers against the adoption of adaptive land management methods among PWOs. We applied a quantitative survey to 355 PWOs from the state of Maine in fall of 2019 and spring of 2020. Our analysis sought to explore the relationships between PWOs’ individual characteristics and histories, and their knowledge, experience, perceptions, attitudes and sources of information regarding ticks and LD. Our findings show that the diversity of PWOs’ attitudes, perceptions of, and land management objectives requires to be approached in tandem. PWOs’ orientations toward LD management methods are significantly associated with several socio-demographic and land-ownership characteristics, such as gender, education background, size of woodlot and frequency of engagement in land management, supporting the inclusion of demographic data in future PWO behavior research. In this paper we discuss how sociodemographic characteristics alongside PWOs’ attitudes, perceptions of and land management objectives can be considered by agencies interested in promoting land management methods through targeted outreach and educational campaigns.

Holocene alluvial dynamics, soil erosion and settlement in the uplands of Macedonia (Greece): New geoarchaeological insights from Xerolakkos in Grevena

This paper addresses the interplay between Holocene landscape evolution and human settlement dynamics, drawing new evidence from the alluvial history of Xerolakkos, a continental stream in Grevena (Western Macedonia, Greece). We developed an integrated geoarchaeological survey combining remote sensing geomorphological mapping, litho-stratigraphic analysis and radiocarbon dating with the site evidence of a new archaeological survey. Results revealed four major alluviation phases, corresponding to 1) the beginning of the Holocene until the Early Neolithic (∼6300/6200 BCE), 2) the end of the Early and the Middle Neolithic (∼6000–5400 BCE), 3) from the Middle Bronze Age to the Late Roman period (∼1800 BCE – 500 CE), and 4) during the Byzantine and Ottoman eras (∼500–1800 CE), all separated by phases of floodplain incision. Furthermore, the effects of several Holocene Rapid Climatic Changes (RCC) are traced and discussed together with potential human responses; we also provide the first alluvial sequence recording the ∼6200 BCE (8.2 kyr BP) event in the Balkans. While the climate and the local geomorphological setting are considered the primary drivers behind instability and erosion during the Early and Middle Holocene, a landscape change starting in the Middle Bronze Age (after ∼1800 BCE) followed by a re-organisation of the rural economy in the Roman period suggests the increasing involvement of anthropogenic forcing which, by the Ottoman period, evolved into a dynamic situation between climatic variability and adaptive land management. Finally, we demonstrate how soil erosion in the upper catchment constitutes a serious taphonomic bias when studying the regional archaeological record.

A global long-term daily reanalysis of reference evapotranspiration for drought and food-security monitoring

NOAA has developed a global reference evapotranspiration (ET0) reanalysis using the UN Food and Agriculture Organization formulation (FAO-56) of the Penman-Monteith equation forced by MERRA phase 2 (MERRA2) meteorological and radiative drivers. The NOAA ET0 reanalysis is provided daily from January 1, 1980 to the near-present at a resolution of 0.5° latitude × 0.625° longitude. The reanalysis is verified against station data across southern Africa, a region presenting both significant challenges regarding hydroclimatic variability and observational quantity and quality and significant potential benefits to food-insecure populations. These data are generated from observations from the Southern African Science Service Centre for Climate Change and Adaptive Land Management (SASSCAL) network. We further verified globally against spatially distributed ET0 derived from two reanalyses–the Global Data Assimilation System (GDAS) and Princeton Global Forcing (PGF)–and these verifications produced similar results, yet demonstrated wide regional and seasonal differences. We also present cases that verify the operational applicability of the reanalysis in long-established drought, famine, crop- and pastoral-stress metrics, and in predictability assessments of drought forecasts.

Public participation for a greener Europe: The potential of farmers in biodiversity monitoring

This article considers the potential of including farmers in the governance, organization and execution of biodiversity monitoring. More specifically, we consider the opportunities for farmer involvement in biodiversity data collection through an explorative empirical analysis of the governance of biodiversity monitoring activities in agricultural landscapes within the Netherlands. We identify practical obstacles to farmer participation, assess the role of environmental NGOs in this process, and consider the extent to which this data leads to increasingly adaptive land management decisions to promote biodiversity. The governance of agri-environmental subsidy contracts with farmer collectives in the Netherlands, we conclude, could be used as an important vehicle to incentivize both biodiversity data collection and science-based land management decisions.

Spatially differentiated effects of local moisture deficit and increased global temperature on hot extreme occurrences

This study compares the relative importance of global temperature change and local moisture deficit in influencing the frequency of hot extremes on a global scale. For the first time, the wavelet decomposed GRACE terrestrial water storage is applied in examining the relationship between soil moisture (θ) and the number of hot days in the hottest month (NHD). It reveals stronger θ-NHD relationships over larger areas than the selected commonly used soil moisture proxies (i.e., standardised precipitation index and a GLDAS model-derived product). During 1985–2015, local moisture deficit played a more important role in influencing hot extreme occurrences in the regions with relatively flat topography, thick soil and where inter-annual rainfall variability is large. They cover an area 1.6 times larger than the areas (e.g., mountain ranges, deserts) where the global temperature change has posed a stronger influence. Under a continuing increase of greenhouse gas forcing, global actions in reducing emissions will support combating the expansion of hot extremes. However, this study shows that necessary attention should also be directed to mitigating the moisture deficit exacerbating hot extremes, e. g., through regional adaptive land management.

Contributions to the Improvement of Climate Data Availability and Quality for Sub-Saharan Africa

Reliable weather observations are the basis to assess climate change and variability. Compared to other regions of the world, long time series of weather observations are sparse in many countries of Sub-Saharan Africa. Various activities at national or international level are ongoing to improve the availability and quality of climate databases. Here, we present ongoing international contributions with a focus on representative examples hosted at Germany's national meteorological service DWD (Deutscher Wetterdienst). The international exchange of monthly climate reports (CLIMAT) is monitored within the Monitoring Centre of the GCOS Surface Network (Global Climate Observing System). In that context also quality control is performed and data are made publicly available. Recent climate observations can be complemented by digitization of historical hand-written weather observations which are available in distributed archives. International data centers, such as the Global Precipitation Climatology Centre (GPCC), collect international data. They perform quality-control of these observations and provide derived products in support of global and regional climate assessments. These activities can also contribute to the improvement of national climate databases, as e.g., demonstrated in a cooperation among selected countries with the SASSCAL initiative (Southern African Science Service Centre for Climate Change and Adaptive Land Management). Satellite-based observations are an additional source that can provide climatological information for selected parameters. In particular, the METEOSAT satellite series provides valuable data for the African continent. The Satellite Application Facility on Climate Monitoring (CM SAF) provides high resolution climate data covering the last decades derived from observations of such meteorological satellites. Based on these examples the paper illustrates the variety of ongoing international efforts in support of regional observation-based climate information, but also identifies the needs for further activities.

Signs of resilience in resprouting

Early and ongoing monitoring of post-fire recovery dynamics is critical for understanding the ecological resilience of forests. The unprecedented scale of Australia’s Black Summer of 2019–2020 poses a significant challenge for post-fire recovery monitoring. Remote sensing is the only practical way to undertake broad-scale analysis of post-fire recovery dynamics. We used Sentinel 2 satellite imagery to compare pre-fire and 1-year post-fire Normalised Burn Ratio (NBR) values. A strong positive trend in the return of vegetation was indicated where post-fire values met or exceeded 80% of the pre-fire NBR value. Our assessment highlighted locations where early recovery responses were divergent from trends in the surrounding areas. Overall, there was a strong spectral recovery across the 2019–2020 south-east Australia fire extent, with more than 50% of the total burned area showing greater than 80% spectral recovery after 1 year. However, several areas where ecosystem resilience may be threatened were identified, including fire-sensitive ecosystems and severely drought-impacted regions. Our results demonstrated biogeographic variation in the effect of fire severity on post-fire spectral recovery, with little effect of severity on spectral recovery observed in the north-east, but much stronger effects in the south. Our study highlights further research for prioritisation as part of adaptive land management programs.

Precipitation temporal repackaging into fewer, larger storms delayed seasonal timing of peak photosynthesis in a semi‐arid grassland

Abstract Against a backdrop of rising temperature, large portions of the western United States are experiencing fewer, larger and less frequent precipitation events. How such temporal ‘repackaging’ of precipitation alters the magnitude and timing of seasonal maximum gross primary productivity (GPPmax) remains unknown. Addressing this knowledge gap is critical, since changes to GPPmax magnitude and timing can impact a range of ecosystem services and management decisions. Here we used a field‐based precipitation manipulation experiment in a semi‐arid mixed annual/perennial bunchgrass ecosystem with mean annual precipitation ~384 mm to investigate how temporal repackaging of a fixed total seasonal precipitation amount impacts seasonal GPPmax and its timing. We found that temporal repackaging of precipitation profoundly influenced the seasonal timing of GPPmax. Many/small precipitation events advanced the seasonal timing of GPPmax by ~13 days in comparison with climatic normal precipitation. Conversely, few/large events led to deeper soil water infiltration, which delayed the timing of GPPmax by up to 16 days in comparison with climatic normal precipitation, and altered end‐of‐season community composition by increasing the diversity of shallow‐rooted annual plants. While GPPmax magnitude did not differ across precipitation treatments, it was positively correlated with the abundance and biomass of deeper‐rooted perennial bunchgrasses. The sensitivity of plant growth, biomass accumulation and plant life histories to the timing and magnitude of precipitation events and the resulting temporal patterns of soil moisture regulated ecosystem responses to altered precipitation patterns. Our results highlight the sensitivity of semi‐arid grassland ecosystem to the temporal repackaging of precipitation. We find that already‐observed and model‐forecasted shifts toward few/large precipitation events could drive significant delays in the timing of peak productivity for this ecosystem. Adaptive land management frameworks should consider these findings since shifts in peak ecosystem productivity would have major implications for multiple land user communities. Additional research is needed to better understand the role of climate, community composition and soil properties in mediating variability in the seasonal timing of maximum ecosystem productivity. A free Plain Language Summary can be found within the Supporting Information of this article.

Analysis of Spatio-temporal rainfall trends and rainfall variability in Botswana between 1958 and 2019

Environments with a climatic characteristic of semi-aridity exhibit high rainfall variability, increasing significantly as the climate changes. In this study, rainfall concentration and Spatio-temporal trends in annual and seasonal (November to March) rainfall in Botswana were analyzed. The satellite data (1958-2019) is obtained from the Southern African Science Service Centre for Climate Change and Adaptive Land Management and the Climate Toolbox. The Mann–Kendall trend test (MK), autocorrelation function (ACF), relative percentage change (RPC), precipitation concentration index (PCI), and Theil–Sen's slope estimator (β) methods were adopted for data analysis. The regions include Gaborone, Maun, Francistown, Serowe, Kasane, Tsabong, Ghanzi, and Pandamatenga. The results indicated that the magnitude of change of change of the significant downward trends in the annual rainfall was found to be -1.11 mm/year at Maun, -1.62 mm/year at Ghanzi, -0.33 mm/year at Pandamatenga, and -0.25 mm/year at Tsabong. The magnitude of change of change of the significant downward trends in the seasonal rainfall was between -0.60 mm/year at Pandamatenga and -0.19 mm/year at Tsabong. All these regions, except Ghanzi, experienced a downward trend in the rainfall distribution. This is owing to the synoptic characteristic of the below-average geopotential heights over Ghanzi. Which might exacerbate the formation of convective systems, leading to a positive rainfall trend at Ghanzi. The annual calculated PCI values are divided into three classes; PCI>10≤15, PCI>16≤20, and PCI>20. The Mann-Kendall analysis of the PCI values indicates that all the regions experienced a downward rainfall trend, implying that it is heading toward a uniform to a moderate distribution. There were distinct patterns that emerged from the Pandamatenga region, indicating a strongly irregular distribution. The regions of Kasane, Maun and Francistown, demonstrated moderate to an irregular distribution. For Gaborone and Francistown, results showed occurrences of a moderate to strongly irregular distribution.

Design and Simulation of an Off-Grid Photovoltaic System with Duty Cycle Prediction Using Neural Network Controller

Global concerns over the inappropriate utilization of abundant renewable energy sources, the damages due to instability of fuel prices, and fossil fuels' effect on the environment have led to an increased interest in green energy (natural power generation) from renewable sources. In renewable energy, photovoltaic is relatively the dominant technique and exhibits non-linearities, leading to inefficiencies. Maximum Power Point is required to be tracked rapidly and improve the power output levels. The target is to use a Neural network controller by training historical data of ambient irradiance and temperature levels as inputs and voltage levels as output for the photovoltaic module to predict duty cycles across the DC-DC converter. The DC-DC converter is the electrical power conditioner at the Botswana International University of Science and Technology, Palapye Off-Grid photovoltaic system. Perturb and Observe algorithm on PSIM environment is only implemented to acquire the historical data for the training and Matlab for the modeling of the network. Relatively long period ambient irradiance and temperature data of Palapye were acquired from the Southern African Science Service Centre for Climate Change and Adaptive Land Management (SASSCAL) WeatherNet in Botswana. Matlab environment was used for the simulation of the backpropagation algorithm for training. The Neural network's feedforward to optimize the non-linear nature of the PV module input and output relationship with relatively fewer processes is required. The results show promising, and the Mean Errors appear to be typically about 0.1 V, and the best performance is 193.5812 at Epoch 13, while the regression delivered a relatively low measured error. The maximum power delivered by the duty cycles from the model with 90 % prediction accuracy. The article demonstrates Neural Network controller is more efficient than the conventional Perturb and Observe Maximum Power Point algorithm.

Geoinformation technologies in land management projects on the agro-landscape basis

At present, the issue of fundamental change in the formation of agronomic solutions through the introduction of ecologically balanced farming systems with the large-scale application of modern technologies is quite relevant. Modern GIS technologies serve an important element in the development of land management projects on an adaptive landscape basis. A geographic information model was developed for the creation of land management projects for agricultural enterprises, and an adaptive land management project for specific land use was improved.

Sampling design workflows and tools to support adaptive monitoring and management

• Adaptive land management requires monitoring of resource conditions, which requires choices about where and when to monitor a landscape. • Designing a sampling design for a monitoring program can be broken down in to eight steps: identifying questions, defining objectives, selecting reporting units, deciding data collection methods, defining the sample frame, selecting an appropriate design type, deciding stratification and allocation, and identifying the required sampling effort. • Here, we provide descriptions of each step in the process and identify tools and resources to complete each step.

Organic soil carbon in Austria – Status quo and foreseeable trends

We compiled information from different sources in order to establish a comprehensive map of the stock of soil organic carbon (SOC) in the upper 30 cm under different forms of land use for Austria. The information serves as a baseline for the evaluation of the potential of climate-change mitigation measures. SOC sequestration plays an important role in the discussion of terrestrial carbon (C) sinks and the size of the SOC pool is one of several quality measures for crop production and the national and regional food security. The baseline serves also for the evaluation of the effectiveness of adaptive land management in order to cope with climate change. Austrian croplands, grasslands, forests, and settlements contain 300 Mt SOC. Peatlands have the highest SOC density (220 t C/ha), yet cover only about 2% of the country. Forest soils store 106 t C/ha and comprise the largest pool due to the coverage of more than 4 Mha (48% of the country). Intensively and extensively managed grasslands cover 0.8 Mha (10%) and contain between 91 and 113 t C/ha, and cropland on 1.28 Mha (15%) hold on average 62 t C/ha. Due to the geographic heterogeneity of Austria with respect to climatic conditions, geology and soils, and topography the regional differences in SOC stocks are large. Measures to increase the SOC stock in cropland have been applied for 25 years within agri-environmental and climate-smart strategies. An increase of the total SOC pool is expected due to the afforestation and reforestation of marginal agricultural land and to a smaller extent due to the restoration of peatlands. A decline of the SOC stock is a consequence of land development for settlements and infrastructure.

Rhythm of change of trend-cycles of vegetation dynamics as an early warning indicator for land management

The use of time series of vegetation indices obtained from satellites has become a highly relevant source of data in studies of land degradation assessment and monitoring. However, information about future vegetation dynamics, which is key for early warnings oriented at land management decisions, is still lacking. Trend-cycle was recently proposed as an indicator that represents a smoothed version of a seasonally adjusted data series, which provides information on long-term movements (trend) while including changes in direction underlying the series (cycles). The aim was to estimate the direction and speed of change of the Normalized Difference Vegetation Index (NDVI) trend-cycles as a complementary information of the rhythm of change between cyclic phases of vegetation productivity. In particular, we estimate the first and second derivative of the end-point of the trend-cycle function, as a measure of the direction the function is going and the speed of change, respectively. The potential advantage of our proposal is the fast processing for large areas and its sensitivity to capturing shifts in temporal dynamics for short time series data. This information can be used as a proxy to build scenarios of the future behaviour of vegetation dynamics, which is a relevant issue to move forward in the development of early warning tools for adaptive land management.

Hydrologic impacts of drought-adaptive agricultural water management in a semi-arid river basin: Case of Rincon Valley, New Mexico

This paper examines the coupled effects of weather condition, crop coverage change, and regional water management (i.e., releases from Caballo Reservoir) on hydrologic characteristics of Rincon Valley (2466 km2), a semi-arid agricultural area in New Mexico, U.S.A., using Soil and Water Assessment Tool (SWAT). The model simulates the vertical water budget and horizontal water transfers during the period 1994–2013, incorporating irrigation of fourteen crops in normal (2008) and dry (2011) years to evaluate the hydrologic impacts of cropping change as a drought-adaptive water management strategy. It was calibrated (2000–2002) and validated (2003–2005) using daily-observed streamflow data. Furthermore, evapotranspiration, diversion and irrigation water volume were verified for the period of 2000–2005 using monthly crop irrigation requirement data and canal discharge data. Results demonstrate the significant role of surface water infiltration, providing approximately 18% of the average annual groundwater recharge during the irrigation season. Watershed scale evapotranspiration (ET) and return flows for the irrigation season were estimated to be 23% and 1% higher than those for the non-irrigation season, respectively. For irrigation units, the ratio of ET to combined precipitation and irrigation water for the dry year was 5% higher than the normal year whereas surface runoff, soil water storage, and groundwater recharge were 7%, 17%, and 39% lower than the normal year, respectively. High groundwater recharge occurs in the hydrologic response units (HRU) where corn and cotton are planted on silty clay loam soil. The Alfalfa acreage (i.e., the largest water user) was reduced by 15% while the cotton acreage was increased by 13% in order to adapt to lower water availability during the dry year. Quantitative understanding of the hydrologic fluxes in the Rincon Valley’s irrigated agricultural area illuminates adaptive land and water management to buffer the adverse impacts of prolonged droughts.

Wavelet analyses and comparative denoised signals of meteorological factors of the namibian atmosphere

With population growth and climate change increasing the exposure of communities and assets to extreme hydrological events, such as floods and droughts, it is crucial to make accurate and timely early warning, climate adaptation and water management information available that can help minimize the loss of the limited quantities of water in arid regions. Trend estimation using wavelets has continued to attract ubiquitous applications in various fields including atmospheric and water studies. While Fourier analysis employs big waves, wavelet analysis uses small waves. Since wavelets localise features in a signal data to different scales, we can therefore preserve vital signal features while removing noise in the signal. Hence the basic notion of wavelet denoising or wavelet thresholding is that the wavelet transform results in a sparse representation for many real-world signals, meaning that the wavelet transform concentrates signal features in a few large-magnitude wavelet coefficients. Since small wavelet coefficients are typically noise, we can “shrink” or remove them without affecting the signal quality.This paper employs wavelet analyses of some selected meteorological factors of the Namibian atmosphere to investigate the noise in the data collected from a number of Southern African Science Service Centre for Climate Change and Adaptive Land Management (SASSCAL) stations, for the years 2012 to 2015. The results show some significant levels of noise around the thresholds of various optimal time series peaks of the data. Hence the denoised data serve to represent fairly refined representations of data for the given period. Moreover, the results of comparing the optimal game model solutions for the original and denoised data specifically identified some weather stations that are “humidity noise stable” and “temperature noise stable”. Thus, these suggest that despite the noise in the respective meteorological data for these stations, the effects of the noise in the data appear to be manageable or optimally controllable, within the given period.

Forage quality and availability for large herbivores in southern African rangelands

Chapter published in Climate change and adaptive land management in southern Africa – assessments, changes, challenges, and solutions. Biodiversity & Ecology, volume 6.

Woodland resources and management in southern Africa

Chapter published in Climate change and adaptive land management in southern Africa – assessments, changes, challenges, and solutions, pp. 296-308

Seasonal prediction and regional climate projections for southern Africa

Chapter published in Climate change and adaptive land management in southern Africa – assessments, changes, challenges, and solutions (2018), pp. 14-21. This is an open access publication.

Rangeland monitoring and assessment: a review

Chapter published in Climate change and adaptive land management in southern Africa – assessments, changes, challenges, and solutions. Biodiversity & Ecology, volume 6