Has global deforestation accelerated due to the COVID-19 pandemic?

As the COVID-19 pandemic unfolded,questions arose as to whether the pandemic would amplify or pacify tropical deforestation.Early reports warned of increased deforestation rates;however,these studies were limited to a few months in 2020 or to selected regions.To better understand how the pandemic infl uenced tropical deforestation globally,this study used historical deforestation data(2004–2019)from the Terra-i pantropical land cover change monitoring system to project expected deforestation trends for 2020,which were used to determine whether observed deforestation deviated from expected trajectories after the fi rst COVID-19 cases were reported.Time series analyses were conducted at the regional level for the Americas,Africa and Asia and at the country level for Brazil,Colombia,Peru,the Democratic Republic of Congo and Indonesia.Our results suggest that the pandemic did not alter the course of deforestation trends in some countries(e.g.,Brazil,Indonesia),while it did in others(e.g.,Peru).We posit the importance of monitoring the long-term eff ects of the pandemic on deforestation trends as countries prioritize economic recovery in the aftermath of the pandemic.

The Digital Agricultural Knowledge and Information System(DAKIS):Employing digitalisation to encourage diversified and multifunctional agricultural systems

Multifunctional and diversified agriculture can address diverging pressures and demands by simultaneously enhancing productivity,biodiversity,and the provision of ecosystem services.The use of digital technologies can support this by designing and managing resource-efficient and context-specific agricultural systems.We present the Digital Agricultural Knowledge and Information System(DAKIS)to demonstrate an approach that employs digital technologies to enable decision-making towards diversified and sustainable agriculture.To develop the DAKIS,we specified,together with stakeholders,requirements for a knowledge-based decision-support tool and reviewed the literature to identify limitations in the current generation of tools.The results of the review point towards recurring challenges regarding the consideration of ecosystem services and biodiversity,the capacity to foster communication and cooperation between farmers and other actors,and the ability to link multiple spatiotemporal scales and sustainability levels.To overcome these challenges,the DAKIS provides a digital platform to support farmers'decision-making on land use and management via an integrative spatiotemporally explicit approach that analyses a wide range of data from various sources.The approach integrates remote and in situ sensors,artificial intelligence,modelling,stakeholder-stated demand for biodiversity and ecosystem services,and participatory sustainability impact assessment to address the diverse drivers affecting agricultural land use and management design,including natural and agronomic factors,economic and policy considerations,and socio-cultural preferences and settings.Ultimately,the DAKIS embeds the consideration of ecosystem services,biodiversity,and sustainability into farmers'decision-making and enables learning and progress towards site-adapted small-scale multifunctional and diversified agriculture while simultaneously supporting farmers'objectives and societal demands.

Explainable artificial intelligence and interpretable machine learning for agricultural data analysis

Artificial intelligence and machine learning have been increasingly applied for prediction in agricultural science.However,many models are typically black boxes,meaning we cannot explain what the models learned from the data and the reasons behind predictions.To address this issue,I introduce an emerging subdomain of artificial intelligence,explainable artificial intelligence(XAI),and associated toolkits,interpretable machine learning.This study demonstrates the usefulness of several methods by applying them to an openly available dataset.The dataset includes the no-tillage effect on crop yield relative to conventional tillage and soil,climate,and management variables.Data analysis discovered that no-tillage management can increase maize crop yield where yield in conventional tillage is<5000 kg/ha and the maximum temperature is higher than 32°.These methods are useful to answer(i)which variables are important for prediction in regression/classification,(ii)which variable interactions are important for prediction,(iii)how important variables and their interactions are associated with the response variable,(iv)what are the reasons underlying a predicted value for a certain instance,and(v)whether different machine learning algorithms offer the same answer to these questions.I argue that the goodness of model fit is overly evaluated with model performance measures in the current practice,while these questions are unanswered.XAI and interpretable machine learning can enhance trust and explainability in AI.

Endophytic bacteria associated with halophyte Seidlitzia rosmarinus Ehrenb.ex Boiss.from saline soil of Uzbekistan and their plant beneficial traits

Endophytic bacteria of halophytic plants play essential roles in salt stress tolerance.Therefore,an understanding of the true nature of plant-microbe interactions under extreme conditions is essential.The current study aimed to identify cultivable endophytic bacteria associated with the roots and shoots of Seidlitzia rosmarinus Ehrenb.ex Boiss.grown in the salt-affected soil in Uzbekistan and to evaluate their plant beneficial traits related to plant growth stimulation and stress tolerance.Bacteria were isolated from the roots and the shoots of S.rosmarinus using culture-dependent techniques and identified by the 16S rRNA gene.RFLP(Restriction Fragment Length Polymorphism)analysis was conducted to eliminate similar isolates.Results showed that the isolates from the roots of S.rosmarinus belonged to the genera Rothia,Kocuria,Pseudomonas,Staphylococcus,Paenibacillus and Brevibacterium.The bacterial isolates from the shoots of S.rosmarinus belonged to the genera Staphylococcus,Rothia,Stenotrophomonas,Brevibacterium,Halomonas,Planococcus,Planomicrobium and Pseudomonas,which differed from those of the roots.Notably,Staphylococcus,Rothia and Brevibacterium were detected in both roots and shoots,indicating possible migration of some species from roots to shoots.The root-associated bacteria showed higher levels of IAA(indole-3-acetic acid)synthesis compared with those isolated from the shoots,as well as the higher production of ACC(1-aminocyclopropane-1-carboxylate)deaminase.Our findings suggest that halophytic plants are valuable sources for the selection of microbes with a potential to improve plant fitness under saline soils.

Determinants for adoption of physical soil and water conservation measures by smallholder farmers in Ethiopia

Adoption rates of soil and water conservation measures remain below the expected levels in Ethiopia despite the considerable investments in reducing land degradation and improving soil fertility.This constitutes one of the key research agendas in the country.This paper underscores the need for investigating the factors hindering or facilitating the adoption of soil and water conservation measures.The study results presented in this paper are based on cross-section data collected from 408 households in eastern Ethiopia,including field observations of 790 plots selected using a multi-stage sampling procedure.A multivariate probit model was employed to analyse the determinants of adoption of three soil and water conservation measures (stone bund,soil bund,and bench terracing) at the plot level.The study findings reveal that household,socioeconomic,and institution characteristics were the key factors that influenced the adoption of soil bund,stone bund,and bench terracing conservation measures.Furthermore,there was a significant correlation among the three soil and water conservation measures,indicating that the adoption of these measures is interrelated.In particular,the results show that there was a positive correlation between stone bunds and soil bunds.However,the correlations between bench terracing and stone bunds as well as bench terracing and soil bunds were negative (implying substitutability).These results imply that the Government and other relevant organizations that are responsible for reducing land degradation in order to increase agricultural production should support the establishment and strengthening of local institutions to facilitate the adoption of soil and water conservation measures.

Land use/land cover classification and its change detection using multi-temporal MODIS NDVI data

Detailed analysis of Land Use/Land Cover （LULC） using remote sensing data in complex irrigated basins provides complete profile for better water resource management and planning. Using remote sensing data, this study provides detailed land use maps of the Lower Chenab Canal irrigated region of Pakistan from 2005 to 2012 for LULC change detection. Major crop types are demarcated by identifying temporal profiles of NDVI using MODIS 250 m × 250 m spatial resolution data. Wheat and rice are found to be major crops in rabi and kharif seasons, respectively. Accuracy assessment of prepared maps is performed using three dif- ferent techniques： error matrix approach, comparison with ancillary data and with previous study. Producer and user accuracies for each class are calculated along with kappa coeffi- cients （K）. The average overall accuracies for rabi and kharif are 82.83% and 78.21%, re- spectively. Producer and user accuracies for individual class range respectively between 72.5% to 77% and 70.1% to 84.3% for rabi and 76.6% to 90.2% and 72% to 84.7% for kharif. The K values range between 0.66 to 0.77 for rabi with average of 0.73, and from 0.69 to 0.74 with average of 0.71 for kharif. LULC change detection indicates that wheat and rice have less volatility of change in comparison with both rabi and kharif fodders. Transformation be- tween cotton and rice is less common due to their completely different cropping conditions. Results of spatial and temporal LULC distributions and their seasonal variations provide useful insights for establishing realistic LULC scenarios for hydrological studies.

Short-Term Response of Soil Respiration to Addition of Chars:Impact of Fermentation Post-Processing and Mineral Nitrogen

The biodegradability of chars derived from pyrolysis and hydrothermal carbonisation(HTC) was studied in short-term dynamic incubation experiments under controlled conditions. Carbon dioxide C(CO2) emissions from soil-char mixtures in combination with solid digestate or mineral nitrogen(N) fertiliser were measured in dynamic chambers for 10 d. Compared to the original material(maize straw), pyrolysis and HTC chars showed significantly lower CO2 emissions and slower decay dynamics; and compared to the soil control, HTC char increased soil respiration to a significant extent, while pyrolysis char did not. The addition of mineral N resulted in a delayed respiration dynamics for HTC char, while the addition of digestate resulted in an increase in the respired CO2 for pyrolysis char and a decrease for HTC char. For the first time, a peculiar two-stage decay kinetics was observed for HTC char,indicating a highly inhomogeneous substrate consisting at least of two C pools.

Microplastic fiber and drought effects on plants and soil are only slightly modified by arbuscular mycorrhizal fungi

Microplastics are increasingly recognized as a factor of global change. By altering soil inherentproperties and processes, ripple-on effects on plants and their symbionts can be expected.Additionally, interactions with other factors of global change, such as drought, can influence theeffect of microplastics. We designed a greenhouse study to examine effects of polyester microfibers,arbuscular mycorrhizal (AM) fungi and drought on plant, microbial and soil responses. We found thatpolyester microfibers increased the aboveground biomass of Allium cepa under well-watered anddrought conditions, but under drought conditions the AM fungal-only treatment reached the highestbiomass. Colonization with AM fungi increased under microfiber contamination, however, plantbiomass did not increase when both AM fungi and fibers were present. The mean weight diameter ofsoil aggregates increased with AM fungal inoculation overall but decreased when the system wascontaminated with microfibers or drought stressed. Our study adds additional support to themounting evidence that microplastic fibers in soil can affect the plant–soil system by promoting plantgrowth, and favoring key root symbionts, AM fungi. Although soil aggregation is usually positivelyinfluenced by plant roots and AM fungi, and microplastic promotes both, our results show that plasticstill had a negative effect on soil aggregates. Even though there are concerns that microplastic mightinteract with other factors of global change, our study revealed no such effect for drought.

Tire abrasion particles negatively affect plant growth even at low concentrations and alter soil biogeochemical cycling

Tire particles(TPs)are a major source of microplastic on land,and considering their chemical composition,they represent a potential hazard for the terrestrial environment.We studied the effectsof TPs at environmentally relevant concentrations alongawide concentrationgradient(0–160 mg g^(-1))and tested the effects on plant growth,soil pH and the key ecosystem process of litter decomposition and soil respiration.The addition of TPs negatively affected shoot and root growth already at low concentrations.Tea litter decomposition slightly increased with lower additions of TPs but decreased later on.Soil pH increased until a TP concentration of 80 mg g^(-1) and leveled off afterwards.Soil respiration clearly increased with increasing concentration of added TPs.Plant growth was likely reduced with starting contamination and stopped when contamination reached a certain level in the soil.The presence of TPs altered a number of biogeochemical soil parameters that can have further effects on plant performance.Considering the quantities of yearly produced TPs,their persistence,and toxic potential,we assume that these particles will eventually have a significant impact on terrestrial ecosystems.

Multi-level governance through adaptive co-management:conflict resolution in a Brazilian state park

Introduction:Communities situated in protected areas generate conflicts among park administrators,residents and scientists.Should they stay or should they go?This article presents a positive example of a community existing in a state park.The study describes the community’s governance process as well as how the park administration and the community solve the conflicts that arise and achieve a method of co-management in a multi-level governance process.Methods:The analysis is based on the Management and Transition Framework(MTF).We used a case study approach and collected data via document study,participatory observation and qualitative interviews.Results:We find that the agreed system of community-based co-management has improved the implementation and enforcement of the state park’s rules through negotiation and communication mechanisms in the park council.This relative success is due to the construction of social capital,equality and empowerment.Conclusions:For state parks in similar situations,the findings suggest that:1)a community should have at least a minimal level of self-organization;2)the empowerment of the community in the decision-making process is useful;3)the park administration should gain the trust of the residents;and 4)the effective management of ecosystem services can create a win-win situation for the community as well as the park.