Integrating optimization and damage estimation to increase economic benefit and ensure food security under seasonal land variability.

Rapidly increasing population and rising food demand necessitates expansion of agricultural mainland. However, with accelerated urbanization, agricultural land resources are difficult to expand. The riverine sandbars offer a vast fertile terrain that is conducive to agricultural food production and could support the global food demands despite urbanization. Nevertheless, hydroclimatic factors like streamflow and precipitation have an impact on the availability of agricultural land and potential crop damage in riverine ecosystems. The present study determines the agricultural economic benefits from cultivation in riverine sandbars through optimization framework under stochastic streamflow. A damage estimation model was developed to evaluate the economic losses an optimally planned riverine agricultural area would suffer, if the flow variation exceeds a certain threshold. The findings showed that economic benefit of ∼130 million rupees could be achieved with ∼22 million rupees of additional benefit from the proposed optimization approach. This additional benefit was in reference to the selective cropping approaches, which the farmers did not account. Furthermore, the damage estimation model could comprehend the losses under fluctuating streamflow in subsequent years that was found to vary between 5 and 34 percent. Therefore, this framework of integrating optimization and damage estimation approaches contribute to a better understanding of optimally utilizing the riverine sandbars, thereby improving the socio-economic status of marginalized communities and providing potential additional land resources to sustain food security and production. The study also highlighted the need of crop insurance facilities to assess and manage risks that could provide financial support to farmers, cover crop loss and damage arising from hydroclimatic variabilities.

Assessment of land use change in riverine ecosystem and utilizing it for socioeconomic benefit.

Increasing urbanization has led to diminished agricultural mainland for food production worldwide. Riverine sandbars could be viewed as prospective landmasses which could act as additional agricultural lands. The present study emphasizes sandbar area availability and the scope of utilizing these landmasses through geospatial techniques, modelling studies and questionnaire surveys. Satellite images were derived at decadal scales to determine the change in the land-use land cover (LULC) within the Brahmaputra River in Assam, India. The fluctuations in the landmasses under different streamflow conditions were derived using a hydrodynamic model. The results showed that the percentage of vegetated sandbars has increased from ~27.36% in 1976-1980 to ~44.67% in 2016-2017, and consecutively, the percentage of non-vegetated sandbars has decreased from ~51.18 to ~30.93%. Furthermore, it was found that the area under cropland and vegetated sandbar varied seasonally. To further understand the utilization of these resources, a socioeconomic survey with a structured set of questions was conducted at various sandbars. The survey revealed various aspects of economic benefits, practices of sandbar cultivation, beneficial produce, difficulties faced, and willingness among farmers to expand sandbar cultivation. Coupling the prevailing traditional knowledge of cultivation among the farmers with scientifically derived information obtained from satellite data and mathematical model simulations can lead to a more systematic utilization of the ecosystem. Thus, the study shows the immense potential of utilizing the sandbars of large rivers to sustain food demands, alongside developing links between land-use change, ecosystem behavior, and socio-economic development.

Optimizing crop planning in the winter fallow season using residual soil nutrients and irrigation water allocation in India.

Effective management of water resources is essential for crop diversification and food security. This study proposes an Irrigation-Food-Environment-Chance-constrained Programming (IFEC) model for simultaneously optimizing crop planting area, irrigation water, and residual fertilizer considering inflow uncertainty along with farmer preference crop. Eight irrigation water allocation optimal models were constructed, fixing the preference crop cultivation area, while deviations in downstream release, and vegetable crop area cultivation were executed for sensitivity analysis. Model is then applied in a command area fed by a sub-tributary of Brahmaputra, India. On averaging, plant available N and P for the area were 62.14 kg ha-1 and 1.13 kg ha-1 respectively. With variation in available water, changes would occur in vegetable and cereal crops having higher yield and relatively less crop water requirement as compared to maize. Results showed that complying with preference crop area up to 60% would decrease the profit by 49% as compared to 20% at even 10% risk probability for 70% release. At existing conditions, water would be insufficient at 60% preference crop. Further, R2 value between benefit and water availability for vegetable cultivation varies from 0.99 to 0.78 for all scenarios. The tool featured that, setting specific preference crop areas provides equitable situation rather than mono-cropping. From the study findings, we suggest two salient recommendations: (1) promoting policies with appropriate financial subsidies for vegetable cultivation that focus on intensification with less water-requiring crops and (2) optimization results could be achieved by expanding the water utilization in the present condition while increasing efficiency.

The potential global distribution of the papaya mealybug, Paracoccus marginatus, a polyphagous pest.

BACKGROUND: The papaya mealybug, Paracoccus marginatus, is a highly polyphagous invasive pest that affects more than 200 plants, many of which are of economic importance. We modelled the potential distribution of P. marginatus using CLIMEX, a process-oriented, climate-based niche model. We combined this model with spatial data on irrigation and cropping patterns to increase the real-world applicability of the model. RESULTS: The resulting model agreed with known distribution points for this pest and with broad areas where P. marginatus has been reported, but for which no GPS data were available. Our model highlights the potential expansion of P. marginatus into novel areas in Central and East Africa, as well as further expansion in Central America and Asia, as these areas are highly climatically suitable, and have large expanses of suitable crop hosts. It also highlights areas, such as the central and eastern states of the USA as well as the western provinces of China, that are suitable for seasonal invasions of P. marginatus. CONCLUSION: Our results offer refined resolution on areas with high potential for invasion by P. marginatus. © 2020 Society of Chemical Industry.

Task-specific versus generalized mnemonic representations in parietal and prefrontal cortices.

Our ability to learn a wide range of behavioral tasks is essential for responding appropriately to sensory stimuli according to behavioral demands, but the underlying neural mechanism has been rarely examined by neurophysiological recordings in the same subjects across learning. To understand how learning new behavioral tasks affects neuronal representations, we recorded from posterior parietal cortex (PPC) before and after training on a visual motion categorization task. We found that categorization training influenced cognitive encoding in PPC, with a marked enhancement of memory-related delay-period encoding during the categorization task that was absent during a motion discrimination task before categorization training. In contrast, the prefrontal cortex (PFC) exhibited strong delay-period encoding during both discrimination and categorization tasks. This reveals a dissociation between PFC's and PPC's roles in working memory, with general engagement of PFC across multiple tasks, in contrast with more task-specific mnemonic encoding in PPC.

Proliferating cells express mRNAs with shortened 3' untranslated regions and fewer microRNA target sites.

Messenger RNA (mRNA) stability, localization, and translation are largely determined by sequences in the 3' untranslated region (3'UTR). We found a conserved increase in expression of mRNAs terminating at upstream polyadenylation sites after activation of primary murine CD4+ T lymphocytes. This program, resulting in shorter 3'UTRs, is a characteristic of gene expression during immune cell activation and correlates with proliferation across diverse cell types and tissues. Forced expression of full-length 3'UTRs conferred reduced protein expression. In some cases the reduction in protein expression could be reversed by deletion of predicted microRNA target sites in the variably included region. Our data indicate that gene expression is coordinately regulated, such that states of increased proliferation are associated with widespread reductions in the 3'UTR-based regulatory capacity of mRNAs.