PENGARUH PUPUK HAYATI DENGAN KOMPOSISI MIKROORGANISME YANG BERBEDA TERHADAP PERTUMBUHAN TANAMAN

Biofertilizer can be used to replace chemical fertilizer so that soil quality is maintained and soil pollution can be avoided. The study aimed to determine the effect of biofertilizers with different compositions of microorganisms on the growth of maize plants. The research was carried out in a greenhouse. Maize plants were fertilized by two kinds of biofertilizers with different compositions of microorganisms. The first biofertilizer contained Azotobacter sp, Azospirillum sp, Rhizobium sp, Trichoderma sp, and Lactobacillus sp, and the second biofertilizer contained Strenotrophomonas sp. and Paenibacillus polymyxa. As controls, maize plants were fertilized by sterilized those biofertilizers. The results showed that the second biofertilizer increased the dry weight of the maize plant. Meanwhile, the first biofertilizer did not increase it. Several things must be considered in the application of biofertilizers, such as the composition of microorganisms, type of plant, level of fertilization, and the method of applying fertilizer to the plant.

Are there dynamic productivity gains from agricultural trade?

PurposeThe primary purpose of this paper is to take an in-depth look at the question of whether liberalizing trade in agriculture can generate dynamic productivity gains comparable to those in the manufacturing sector.Design/methodology/approachIn contrast to the manufacturing sector that has generated firm/plant-level trade data, there is a lack of farm-level trade data that are needed for empirical measurement of dynamic productivity gains. Therefore, the authors use thought experiments to analyze the sequence of events that would occur when trade is liberalized for agriculture; delineate the expected behaviors of the actors involved in the trade and draw inferences about whether there would be dynamic productivity gains from agricultural trade.FindingsThe central finding is that there would be little dynamic gain from agricultural trade at the farm level due to the limited role of producers in shaping their international competitiveness. Yet, agricultural trade may generate dynamic gains if states or input supply corporations respond to the freer trade environment by making more investments for research and development (R&D). Further, when intraindustry prevails, there can be productivity gains at the industry level due to the transfer of resources from less to more efficient farm producers.Originality/valueThe findings of the paper are expected to present insights into value for researchers working in the area of agricultural trade; for agricultural trade policymakers in developing countries and for trade negotiators engaged in reforming or designing World Trade Organization (WTO)’s trade rules for agriculture.

When SWEETs Turn Tweens: Updates and Perspectives

Sugar translocation between cells and between subcellular compartments in plants requires either plasmodesmata or a diverse array of sugar transporters. Interactions between plants and associated microorganisms also depend on sugar transporters. The sugars will eventually be exported transporter (SWEET) family is made up of conserved and essential transporters involved in many critical biological processes. The functional significance and small size of these proteins have motivated crystallographers to successfully capture several structures of SWEETs and their bacterial homologs in different conformations. These studies together with molecular dynamics simulations have provided unprecedented insights into sugar transport mechanisms in general and into substrate recognition of glucose and sucrose in particular. This review summarizes our current understanding of the SWEET family, from the atomic to the whole-plant level. We cover methods used for their characterization, theories about their evolutionary origins, biochemical properties, physiological functions, and regulation. We also include perspectives on the future work needed to translate basic research into higher crop yields. Expected final online publication date for the Annual Review of Plant Biology, Volume 73 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Cuckoo male bumblebees perform slower and longer flower visits than free-living male and worker bumblebees

Cuckoo bumblebees are a monophyletic group within the genus Bombus and social parasites of free-living bumblebees, upon which they rely to rear their offspring. Cuckoo bumblebees lack the worker caste and visit flowers primarily for their own sustenance and do not collect pollen. Although different flower-visiting behaviours can be expected between cuckoo and free-living bumblebees due to different biological constraints, no study has yet quantified such differences. Here, we provide the first empirical evidence of different flower-visiting behaviours between cuckoo and free-living bumblebees. We recorded the flower-visiting behaviour of 350 individual bumblebees over two years in a wild population of the entomophilous plant Gentiana lutea, of which they are among the main pollinators. In cuckoo bumblebees (28.9% of the total), we only found males, while we found both workers and males in free-living bumblebees. Cuckoo bumblebees visited significantly more flowers for longer time periods than both free-living bumblebee workers and males within whorls, while differences at the whole-plant level were less marked. Free-living bumblebee males visited more flowers and performed slightly longer flower visits than workers. Behavioural differences between cuckoo male bumblebees and free-living bumblebee workers are likely related to different foraging needs, while differences between cuckoo and free-living bumblebee males may be caused by differences in colony development and a delayed mating period of free-living bumblebees. The longer visits made by cuckoo male bumblebees will likely negatively affect plant reproductive success through increased within-plant pollen flow.

Non-Destructive Monitoring of Maize Nitrogen Concentration Using a Hyperspectral LiDAR: An Evaluation from Leaf-Level to Plant-Level

Advanced remote sensing techniques for estimating crop nitrogen (N) are crucial for optimizing N fertilizer management. Hyperspectral LiDAR (HSL) data, with both spectral and spatial information of the targets, can extract more plant properties than traditional LiDAR and hyperspectral imaging systems. In this study, we tested the ability of HSL in terms of estimating maize N concentration at the leaf-level by using spectral indices and partial least squares regression (PLSR) methods. Subsequently, the N estimation was scaled up to the plant-level based on HSL point clouds. Biomass, extracted with structural proxies, was utilized to exhibit its supplemental effect on N concentration. The results show that HSL has the ability to extract N concentrations at both the leaf-level and the canopy-level, and PLSR showed better performance (R2 > 0.6) than the single spectral index (R2 > 0.4). In comparison to the stem height and maximum canopy width, the plant height had the strongest ability (R2 = 0.88) to estimate biomass. Future research should utilize larger datasets to test the viability of using HSL to monitor the N concentration of crops, which is beneficial for precision agriculture.

Multihazard Risk Aggregation Approach for Quantitative Risk Assessment of Upstream Oil and Gas Facilities

For the design and operation of Oil and Gas (O&G) facilities, a Quantitative Risk Assessment (QRA) should be performed to quantify the risk of major accidents due to multiple hazards and sources at the plant level, thus allowing the effective identification and allocation of safety barriers. In this work, a novel approach for the multi-hazard and multi-source aggregation of risks is proposed, accounting for the uncertainties typically unexpressed in a conventional QRA (both on the frequency and severity of the accidental scenarios). The multi-hazard risk assessment framework proposed is applied to assess the Location-Specific Individual Risk (LSIR) for a representative Upstream O&G plant (case study), using a model based on multistate Bayesian Networks (BNs) for different functional units, each one undergoing an initiating event of Loss Of Primary Containment (LOPC). Estimates of frequency and severity for each possible accident scenario are aggregated to eventually calculate the overall LSIR. Moreover, LSIR's confidence intervals are provided to describe the uncertainty associated to the estimates, and the frequency and severity contributions to risk are derived for targeted prioritization of the safety barriers in view of the risk reduction.

Wastewater network infrastructure in public health: Applications and learnings from the COVID-19 pandemic

Accurate estimates of COVID-19 burden of infections in communities can inform public health strategy for the current pandemic. Wastewater based epidemiology (WBE) leverages sewer infrastructure to provide insights on rates of infection by measuring viral concentrations in wastewater. By accessing the sewer network at various junctures, important insights regarding COVID-19 disease activity can be gained. The analysis of sewage at the wastewater treatment plant level enables population-level surveillance of disease trends and virus mutations. At the neighborhood level, WBE can be used to describe trends in infection rates in the community thereby facilitating local efforts at targeted disease mitigation. Finally, at the building level, WBE can suggest the presence of infections and prompt individual testing. In this critical review, we describe the types of data that can be obtained through varying levels of WBE analysis, concrete plans for implementation, and public health actions that can be taken based on WBE surveillance data of infectious diseases, using recent and successful applications of WBE during the COVID-19 pandemic for illustration.