Adaptation to saline intrusion for agriculture farming transformation in the coastal Ben Tre province, Vietnam

This study aimed to evaluate the adaptive capacity (AC) of agriculture farming transformation to improve for the adaptability by saline intrusion of those culture systems in the coastal Ben Tre province. The primary data was collected by questionnaires in three coastal districts of Ba Tri, Thanh Phu and Binh Dai with 178 households to distribute on agriculture land use transformations. An Object-Based Image Analysis (OBIA) and multi-temporal image analysis approach was developed to detect how LULC changes during 2010-2020 in the coastal Ben Tre province using Landsat TM and OLI data. The MODIS (MOD09 8-day reflectance) data was processed into monthly NDVI maps with the Time Series Product Tool software package and then used to classify regionally common rice crops LULC types using unsupervised classification by ISODATA algorithm. Based on primary data collection, evaluating adaptation measures was assessed the compilation of modification options based on a detailed description and criteria including human, society, infrastructure and natural conditions by standardized a weight ranges of adaptive indicators in the agriculture land use changes. The results detected five agriculture land use transformation including rice-shrimp crop rotation to aquaculture, single rice crop to aquaculture, double rice crop to perennial plant/orchards, double rice crop to rice-shrimp crop rotation, and triple rice crop to double rice crop. The adaptability of these transformations conducted the shrimp-rice crop rotation to aquaculture in Binh Dai of strongly high adaptability. Next is followed by double rice crops to orchards and double rice crops to rice-shrimp crop rotation with high adaptability while the rice-shrimp crop rotation to aquaculture is only moderate in Thanh Phu. The low adaptability levels were identified on single rice crop to aquaculture and triple rice crop to double rice crop transformations due to tolerant condition of saltwater for rice crop cultivation. The studies results can further contribute to support adaptation planning in these sectors by using, developing and streamlining this framework to additional and different socio-ecological contexts.

Identification of climate induced optimal rice yield and vulnerable districts rankings of the Punjab, Pakistan

The study attracted to insinuate the inhabitant anomalies of the crop yield in the districts of the Punjab where climate variation, inputs utilization, and district exponents are indispensable factors. Impact evaluation of sowing and harvesting dates for rice yield has been analyzed. Suitable sowing and harvesting dates and potential districts for the crop are proposed. Data consisting of 13,617 observations of more than 90 factors encompassing valuable dimensions of the growth of the crops collected through comprehensive surveys conducted by the Agriculture Department of Punjab are formulated to incorporate in this study. The results establish the significant negative repercussions of climate variability while the impacts vary in the districts. The crop yield deteriorates considerably by delaying the sowing and harvesting times. Districts climate-induced vulnerability ranking revealed Layyah, Jhelum, Mianwali, Khanewal and Chinniot, the most vulnerable while Kasur, Gujrat, Mandi Bhauddin, Nankana Sahib and Hafizabad, the least vulnerable districts. Spatial mapping explains the geographical pattern of vulnerabilities and yield/monetary losses. The study ranks districts using climate-induced yield and monetary loss (222.30 thousand metric tons of rice which are equal to 27.79 billion PKR climatic losses in single rice season) and recommends: the formation of district policy to abate the adverse climate impact, utilization of suitable climate variation by adhering proper sowing and harvesting times, setting the prioritized districts facing climate-induced losses for urgent attention and preferable districts for rice crop.

Mapping Crop Rotation by Using Deeply Synergistic Optical and SAR Time Series

Crop rotations, the farming practice of growing crops in sequential seasons, occupy a core position in agriculture management, showing a key influence on food security and agro-ecosystem sustainability. Despite the improvement in accuracy of identifying mono-agricultural crop distribution, crop rotation patterns remain poorly mapped. In this study, a hybrid convolutional neural network (CNN) and long short-term memory (LSTM) architecture, namely crop rotation mapping (CRM), were proposed to synergize the synthetic aperture radar (SAR) and optical time series in a rotational mapping task. The proposed end-to-end architecture had reasonable accuracies (i.e., accuracy > 0.85) in mapping crop rotation, which outperformed other state-of-the-art non-deep or deep-learning solutions. For some confusing rotation types, such as fallow-single rice and crayfish-single rice, CRM showed substantial improvements from traditional methods. Furthermore, the deeply synergistic SAR-optical, time-series data, with a corresponding attention mechanism, were effective in extracting crop rotation features, with an overall gain of accuracy of four points compared with ablation models. Therefore, our proposed method added wisdom to dynamic crop rotation mapping and yields important information for the agro-ecosystem management of the study area.

Plasma membrane H+-ATPase overexpression increases rice yield via simultaneous enhancement of nutrient uptake and photosynthesis

Nitrogen (N) and carbon (C) are essential elements for plant growth and crop yield. Thus, improved N and C utilisation contributes to agricultural productivity and reduces the need for fertilisation. In the present study, we find that overexpression of a single rice gene, Oryza sativa plasma membrane (PM) H+-ATPase 1 (OSA1), facilitates ammonium absorption and assimilation in roots and enhanced light-induced stomatal opening with higher photosynthesis rate in leaves. As a result, OSA1 overexpression in rice plants causes a 33% increase in grain yield and a 46% increase in N use efficiency overall. As PM H+-ATPase is highly conserved in plants, these findings indicate that the manipulation of PM H+-ATPase could cooperatively improve N and C utilisation, potentially providing a vital tool for food security and sustainable agriculture.