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In data-scarce regions, prudent planning and precise decision-making for sustainable development, especially in agriculture, remain challenging due to the lack of correct information. Remotely sensed satellite images provide a powerful source for assessing land use and land cover (LULC) classes and crop identification. Applying remote sensing (RS) in conjunction with the Geographical Information System (GIS) and modern tools/algorithms of artificial intelligence (AI) and deep learning has been proven effective for strategic planning and integrated resource management. The study was conducted in the canal command area of the Lower Chenab Canal system in Punjab, Pakistan. Crop features/classes were assessed using the Normalized Difference Vegetation Index (NDVI) algorithm. The Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m and Landsat 5 TM (thematic mapper) images were deployed for NDVI time-series analysis with an unsupervised classification technique to obtain LULC classes that helped to discern cropping pattern, crop rotation, and the area of specific crops, which were then used as key inputs for agricultural mechanization planning and resource management. The accuracy of the LULC map was 78%, as assessed by the error matrix approach. Limitations of high-resolution RS data availability and the accuracy of the results are the concerns observed in this study that could be managed by the availability of good quality local sources and advanced processing techniques, that would make it more useful and applicable for regional agriculture and environmental management. [ABSTRACT FROM AUTHOR]

In Pakistan, the pea is one of the leading and popular vegetables grown. Peas are usually sown manually by placing seeds one by one or by broadcasting method. Manual placing is very arduous and time-consuming practice, which limits the pea planting acreage. The broadcasting requires more seed rate causing high input cost. The local farmers were in a dire need of a planter for pea cultivation. The aim of this study was to evaluate a pneumatic pea planter in the field developed by a local manufacturer under the supervision of Agricultural Engineering Institute, National Agricultural Research Centre, Islamabad, Pakistan. It was a four-row pneumatic planter. It makes two beds of 0.76 m wide and plants four rows of peas on both ends of the raised beds 5 cm deep. Initially, row to row distance was kept fixed to 0.76 m, however, plant to plant distance was adjustable from 6 to 20 cm by using different combinations of sprockets or by changing the seed metering plate having different number of holes. The working width of the planter was 3 m and can be operated with a 65 hp or higher tractor. The seed sticks well against the holes of seed metering plate due to air suction pressure, when the aspirator blower is operated at 400 to 540 rpm of tractor PTO. The planter was field evaluated at Gujranwala and compared with manual sowing and broadcasting methods. The actual field capacity of the planter was 0.45 ha/h with a field efficiency of 58.6%, when operated in low-I tractor gear and at 1800 engine rpm. The pea planting cost by manual seed placement and broadcasting methods was Rs. 14,940 and Rs. 5,240 per hectare, respectively. The operating cost of the planter was Rs. 3,015, which was 395.52 and 73.8% lower than the manual seed placement and broadcasting methods, respectively. By using pea planter, a farmer can achieve Rs. 64,855 and 21,325 more benefit from one hectare than manual seed placement and broadcasting methods, respectively. Timely sowing, quality of work and less drudgery were additional vital benefits to the farmer using this machine. Similarly, the seed cost was reduced by 291.9% as compared with broadcasting method. In the first version of planter, the row spacing was more than the farmers' used spacing, therefore, plant population was less than recommended. In future manufacturing, the row to row distance would be decreased to get optimum plant density and a higher yield. The pea planter is a good initiative for improving vegetable mechanisation of the country. [ABSTRACT FROM AUTHOR]

Fine-scale spatial information on soil properties is needed to successfully implement precision agriculture. Proximal gamma-ray spectroscopy has recently emerged as a promising tool to collect fine-scale soil information. The objective of this study was to evaluate a proximal gamma-ray spectrometer to predict several soil properties using energy-windows and full-spectrum analysis methods in two differently managed sandy loam fields: conventional and organic. In the conventional field, both methods predicted clay, pH and total nitrogen with a good accuracy (R² ≥ 0.56) in the top 0-15 cm soil depth, whereas in the organic field, only clay content was predicted with such accuracy. The highest prediction accuracy was found for total nitrogen (R² = 0.75) in the conventional field in the energy-windows method. Predictions were better in the top 0-15 cm soil depths than in the 15-30 cm soil depths for individual and combined fields. This implies that gamma-ray spectroscopy can generally benefit soil characterisation for annual crops where the condition of the seedbed is important. Small differences in soil structure (conventional vs. organic) cannot be determined. As for the methodology, we conclude that the energy-windows method can establish relations between radionuclide data and soil properties as accurate as the full-spectrum analysis method. [ABSTRACT FROM AUTHOR]

Olive, chiefly used for high quality edible oil and pickling, is a traditionally an important fruit crop of Mediterranean basin. Its cultivation has expanded to different parts of the world including Pakistan. The ability of olive to thrive on marginal and rocky lands has made it a popular fruit crop in Potohar. The present study was planned to investigate the impact of organic media and NPK levels on growth of olive seedlings. The research material consisted of 150 olive seedlings. The experiment was laid out following Complete Randomized Block Design with three replications at field area of Department of Horticulture, PMAS Arid Agriculture University Rawalpindi, Pakistan during 2018-19. There were 10 treatments of organic media and NPK doses with different combinations. Data were recorded for various traits viz., plant height, number of leaves and primary branches, leaf fresh weight, root length, root fresh weight, chlorophyll and NPK content. Data recorded were analyzed statistically using Statistix 8.1 software to ascertain significance and means were compared using LSD at 5% probability level. Results revealed that treatment T6 (FYM + Silt + Dose 3 i.e. 7.5 g NPK) was found most ideal growing media for most of traits like number of primary branches, root length, fresh root weight and chlorophyll content in olive seedlings. Hence it is recommended that growth media comprising of silt, FYM and 7.5 g NPK should be used for growing olive seedlings successfully. [ABSTRACT FROM AUTHOR]