Search

The crack detection of concrete bridges is an important link in the safety evaluation of bridge structures, and the rapid and accurate identification and detection of bridge cracks is a prerequisite for ensuring the safety and long-term stable use of bridges. To solve the incomplete crack detection and segmentation caused by the complex background and small proportion in the actual bridge crack images, this paper proposes a coarse–fine combined bridge crack detection method of "double detection + single segmentation" based on deep learning. To validate the effect and practicality of fine crack detection, images of old civil bridges and viaduct bridges against a complex background and images of a bridge crack against a simple background are used as datasets. You Only Look Once V5(x) (YOLOV5(x)) was preferred as the object detection network model (ODNM) to perform initial and fine detection of bridge cracks, respectively. Using U-Net as the optimal semantic segmentation network model (SSNM), the crack detection results are accurately segmented for fine crack detection. The test results showed that the initial crack detection using YOLOV5(x) was more comprehensive and preserved the original shape of bridge cracks. Second, based on the initial detection, YOLOV5(x) was adopted for fine crack detection, which can determine the location and shape of cracks more carefully and accurately. Finally, the U-Net model was used to segment the accurately detected cracks and achieved a maximum accuracy (AC) value of 98.37%. The experiment verifies the effectiveness and accuracy of this method, which not only provides a faster and more accurate method for fine detection of bridge cracks but also provides technical support for future automated detection and preventive maintenance of bridge structures and has practical value for bridge crack detection engineering. [ABSTRACT FROM AUTHOR]

This article presents the findings of a study conducted in West Bengal, India, on the correlation between different tillage practices, nutrient management, and the yield of maize. The study found that there was no significant difference in grain and stover yield between different tillage practices. However, significantly higher grain and stover yield was observed with site-specific nutrient management. The study also revealed positive correlations between various growth parameters and grain yield. These findings provide valuable insights into sustainable farming practices for maize cultivation, contributing to food security and environmental stewardship. [Extracted from the article]

Site-specific nutrient management (SSNM) for a rice (Oryza sativa L.)-growing domain is typically developed by first using the nutrient omission plot technique (NOPT) to determine yield responses with added nitrogen (N), with added phosphorus (P), and with added potassium (K) and then conducting an evaluation experiment to verify an SSNM practice developed from the NOPT results. We hypothesized that an effective SSNM practice could be more rapidly developed through concurrent NOPT experiments and evaluation of a preliminary best-bet SSNM practice derived from experiences in other rice-growing domains. Our study domain was rainfed lowland rice in an Indonesian tidal swampland without past NOPT experiments and without a verified SSNM practice. Concurrent NOPT and evaluation experiments were conducted in the wet season (WS) and dry season across 20–25 farmers' fields either directly or not directly influenced by tides. Rice yields and gross return above fertilizer cost for a preliminary SSNM practice were higher than for farmers' fertilizer practice in both seasons and for two fertilizer practices based on soil tests in WS. The inherent ability of SSNM to adjust fertilizer rates for an attainable target yield and time fertilizer applications to match crop demand contributed to the superiority of SSNM. In NOPT experiments, relative yields without added N, without added P, and without added K respectively averaged 0.70, 0.89, and 0.88 across seasons and tidal locations. These relative yields enabled preliminary SSNM fertilizer rates to be further adjusted for the rice-growing domain. Rice growers in domains without a verified SSNM practice can benefit, before NOPT experiments are completed, by using generic SSNM guidelines for timing fertilizer applications and adjusting fertilizer rates for a target yield. After completion of NOPT experiments, the fertilizer rates can be further adjusted based on measured yield responses with added N, P, or K. [ABSTRACT FROM AUTHOR]

A high-quality dataset is a basic requirement to ensure the training quality and prediction accuracy of a deep learning network model (DLNM). To explore the influence of label image accuracy on the performance of a concrete crack segmentation network model in a semantic segmentation dataset, this study uses three labelling strategies, namely pixel-level fine labelling, outer contour widening labelling and topological structure widening labelling, respectively, to generate crack label images and construct three sets of crack semantic segmentation datasets with different accuracy. Four semantic segmentation network models (SSNMs), U-Net, High-Resolution Net (HRNet)V2, Pyramid Scene Parsing Network (PSPNet) and DeepLabV3+, were used for learning and training. The results show that the datasets constructed from the crack label images with pix-el-level fine labelling are more conducive to improving the accuracy of the network model for crack image segmentation. The U-Net had the best performance among the four SSNMs. The Mean Intersection over Union (MIoU), Mean Pixel Accuracy (MPA) and Accuracy reached 85.47%, 90.86% and 98.66%, respectively. The average difference between the quantized width of the crack image segmentation obtained by U-Net and the real crack width was 0.734 pixels, the maximum difference was 1.997 pixels, and the minimum difference was 0.141 pixels. Therefore, to improve the segmentation accuracy of crack images, the pixel-level fine labelling strategy and U-Net are the best choices. [ABSTRACT FROM AUTHOR]

The crack detection of concrete bridges is an important link in the safety evaluation of bridge structures, and the rapid and accurate identification and detection of bridge cracks is a prerequisite for ensuring the safety and long-term stable use of bridges. To solve the incomplete crack detection and segmentation caused by the complex background and small proportion in the actual bridge crack images, this paper proposes a coarse–fine combined bridge crack detection method of “double detection + single segmentation” based on deep learning. To validate the effect and practicality of fine crack detection, images of old civil bridges and viaduct bridges against a complex background and images of a bridge crack against a simple background are used as datasets. You Only Look Once V5(x) (YOLOV5(x)) was preferred as the object detection network model (ODNM) to perform initial and fine detection of bridge cracks, respectively. Using U-Net as the optimal semantic segmentation network model (SSNM), the crack detection results are accurately segmented for fine crack detection. The test results showed that the initial crack detection using YOLOV5(x) was more comprehensive and preserved the original shape of bridge cracks. Second, based on the initial detection, YOLOV5(x) was adopted for fine crack detection, which can determine the location and shape of cracks more carefully and accurately. Finally, the U-Net model was used to segment the accurately detected cracks and achieved a maximum accuracy (AC) value of 98.37%. The experiment verifies the effectiveness and accuracy of this method, which not only provides a faster and more accurate method for fine detection of bridge cracks but also provides technical support for future automated detection and preventive maintenance of bridge structures and has practical value for bridge crack detection engineering.

Indiscriminate use of inorganic fertilizers has bought threat to soil health in respect to physical, chemical and biological properties of soil. Therefore, it is necessary to minimize the usage of inorganic fertilizers by substituting with organics. It is well established fact that the improvement in quality and productivity of the crops could be made possible with combined application of organic sources and balanced chemical fertilizers. Therefore, a field experiment was conducted during rabi 2021-22 at College of Agriculture, UAS, Raichur to study the effect of organic sources and nutrient levels on growth, yield and economics of safflower (Carthamus tinctorius L.). Eighteen treatment combinations were evaluated which consisted of three organic sources and six nutrient levels. The experiment was laid out in Factorial RBD design with three replications. The results indicated that among organic sources, application of FYM @ 5 t /ha recorded significantly higher growth parameters viz., plant height (85.95 cm), total dry matter accumulation (121.59 g/plant), seed yield (1467 kg/ha) and stalk yield (2216 kg /ha). Further this treatment recorded higher gross returns (Rs. 80,699/ha) and net returns (Rs. 51247/ha) compared to other treatment. Among the different nutrient levels, SSNM (STCR equation) + micronutrients Zn @ 25 kg/ha + S @ 10 kg/ha showed significant effect on growth parameters and seed yield (1508 kg/ha) as compared to other treatment. Moreover, the combination registered higher gross returns (Rs. 82,964/ha) and net returns (Rs. 54,540/ha) compared to other nutrient levels. [ABSTRACT FROM AUTHOR]

A high-quality dataset is a basic requirement to ensure the training quality and prediction accuracy of a deep learning network model (DLNM). To explore the influence of label image accuracy on the performance of a concrete crack segmentation network model in a semantic segmentation dataset, this study uses three labelling strategies, namely pixel-level fine labelling, outer contour widening labelling and topological structure widening labelling, respectively, to generate crack label images and construct three sets of crack semantic segmentation datasets with different accuracy. Four semantic segmentation network models (SSNMs), U-Net, High-Resolution Net (HRNet)V2, Pyramid Scene Parsing Network (PSPNet) and DeepLabV3+, were used for learning and training. The results show that the datasets constructed from the crack label images with pix-el-level fine labelling are more conducive to improving the accuracy of the network model for crack image segmentation. The U-Net had the best performance among the four SSNMs. The Mean Intersection over Union (MIoU), Mean Pixel Accuracy (MPA) and Accuracy reached 85.47%, 90.86% and 98.66%, respectively. The average difference between the quantized width of the crack image segmentation obtained by U-Net and the real crack width was 0.734 pixels, the maximum difference was 1.997 pixels, and the minimum difference was 0.141 pixels. Therefore, to improve the segmentation accuracy of crack images, the pixel-level fine labelling strategy and U-Net are the best choices.

The QUEFTS (QUantitative Evaluation of the Fertility of Tropical Soils) model is a valuable tool for developing location-specific management of macronutrients such as N, P, and K to attain higher yields. A review analysis is conducted to understand the applicability of the QUEFTS model in developing site-specific nutrient management (SSNM) for different crops. A total of 33 published articles are available for the QUEFTS model for different crops across the world. The SSNM is available for rice, wheat, maize, potato, tropical tuber crops (cassava, sweet potato, elephant foot yam, taro, and yam), peanut, watermelon, soybean, radish, tea, sweet potato, elephant foot yam, yam, and taro in different countries using the QUEFTS model, and it is discussed in this review. The maximum values of accumulation and dilution of N, P, and K; the balanced nutrient requirements to produce 1000 kg of crop yield; and their internal efficiencies are discussed in this review paper. The review shows that the QUEFTS model parameters derived for rice, wheat, and maize can be directly adapted to different environmental conditions. The data available for potato demand further refinement of the model for its general application irrespective of the agro-climatic conditions. The two sets of data available for cassava are similar and can use the same model parameters for further studies for nutrient management. Single set of data for other crops shows the necessity for more studies to increase their production. The modeled values for different crops presented in this review can further assist the decision-makers in scheduling fertilizer applications for the respective crops, reducing fertilizer and economic losses, and maintaining soil quality. Thus, QUEFTS can be widely used as a simple model to recommend nutrient management practices. However, the QUEFTS model considers only the mean meteorological parameters irrespective of their daily variations. It also considers the soil as a homogeneous medium, which influences the crop yield estimation, which needs to be addressed among agricultural researchers. Therefore, further modifications are also needed for QUEFTS to incorporate different agro-climatic and management conditions and different interactive processes, including other micronutrients, irrigation practices, and sub-soil properties as this model assumes that nutrients are the only limiting factors in crop yield. This review also demands the necessity to focus on the nutrient management of other food crops to ensure food security. [ABSTRACT FROM AUTHOR]

Nepal is a major rice producer of South Asia, feeding more than 20 million people of the country and abroad. However, productivity and profitability of rice are low in Nepal compared to other regional countries. Large yield gaps exist between attainable and farmers' yields and nutrient management is identified as one of the major reasons. Nutrient Expert® (NE), a decision-support tool based on site-specific nutrient management (SSNM) principles has been validated and applied in many countries of Asia and Africa as new recommendation protocol for nutrient management in selected crops, including rice. Present study used NE® Rice as nutrient management protocol in farmers' fields across Terai and mid-hills regions of Nepal during 2014–2018. The study revealed that NE-based fertilizer recommendations resulted in increase in yield over 2.0 t.ha−1 (p ≤ 0.05) and double the profits compared to existing blanket fertilizer recommendation, and farmers' fertilizer practice. Due to large variations in slope, altitude, soil, and management, performance of NE® varied across locations. Its performance was better in Terai due to flat lands and less variations in soil types, compared to sloping lands and varied soils of mid-hills. NE tool provided adequate guidance to apply the required rate of fertilizer that matched the crop requirement and soil nutrient supply of each farmer fields. The novelty of the study is introduction of a dynamic fertilizer recommendation approach that can supplement the existing static blanket recommendation system. We conclude that Nutrient Expert® - Rice can be considered as an effective tool for nutrient recommendation for rice in Nepal. [ABSTRACT FROM AUTHOR]

Wheat is the third most important cereal crop in Nepal after rice and maize both in area and production, but its productivity of 2.3 tonne ha−1 is very less compared to other developed countries (6 tonne ha−1 for Switzerland and China) in the world. The main cause of low wheat yield in Nepal is the improper and inadequate use of fertilizer devoid of site specific nutrient management practices. Therefore, a farmers' field experiment was conducted during November 2015 to April 2016 to rectify the best fertilizer management options at two sites of Damak and Gauradaha in Jhapa district in eastern-Terai of Nepal using Nutrient Expert®-Wheat model. The research was accomplished in Randomized Complete Block Design with 2 treatments and 20 replications, considering farmers' field as replication. Two treatments included in the experimentation were NE® (Nutrient Expert Recommendation) and FFP (Farmer's Fertilizer Practices). The statistical result revealed the highly significant difference in terms of number of effective tiller m−2, plant height, filled grain per spike, spike length, grain, straw and biological yields and harvest index. The highest yield (4.71 tonne ha−1) was obtained from NE field than FFP (2.99 tonne ha−1). On an average, NE based practices produced 58 % higher yield in comparison to FFP. NE based treatment produced significantly higher biomass yield, yield attributes and cost-benefit ratio than FFP treatments. Field experiment validation confirmed that the Nutrient Expert® Wheat model could be used as the most adoptable and practical precision decision support system tool to make a more authentic fertilizer recommendation in eastern-Terai of Nepal.

An experiment was conducted to study the effect of nutrient management and varieties of maize on productivity and economics and their residual effect on succeeding gobhi sarson crop. Four nutrient management treatments (viz. site-specific nutrient management, SSNM; recommended fertilizer dose, RFD; 50% RFD and farmer's practice) were applied to five varieties of maize (viz. PAC 740, PMZ-4, PSCL 4640, HQPM 1, and BISCO 855) during the kharif seasons of 2012 and 2013 at Palampur. The residual effects of fertilizers were studied in succeeding gobhi sarson. The SSNM-based nutrient application in maize significantly increased crop dry matter, cobs/ha, grain yield, nitrogen, phosphorus and potassium (NPK) uptake, succeeding gobhi sarson seed yield, maize grain equivalent yield, net return, profitability, and B:C. The SSNM-based nutrient application in maize resulted in 4.2–4.6% and 32.6–46.2% higher system productivity and Indian Net Rupee (INR) 4,507–4,926 and INR 28,837–30,078 higher net returns of over recommended fertilizer application and farmer practice, respectively. SSNM had lowest productivity cost of INR 3.1/kg grain. Apparent NPK productivity (62.4 kg grain/kg NPK applied) was highest under 50% RFD. BISCO 855 was earliest in silking with bolder grain. PMZ-4 and PSCL 4640 gave significantly higher dry matter, cob number, grain and stover yield, apparent NPK and physiological productivity, maize equivalent yield, net return and B:C, and lower productivity cost. Gobhi sarson seed yield was not significantly affected by preceding maize varieties. For higher system productivity and income from the maize-gobhi sarson, SSNM with PMZ 4 or PSCL 4640 varieties of maize must be adopted. [ABSTRACT FROM AUTHOR]

Yam (Dioscorea spp.) is an important tropical tuber crop contributing to food and nutritional security of economically backward and ethnic people of many countries in Africa, South America and Asia. Yam yield in the major growing environments of India has been stagnating despite the development of high yielding varieties and increasing use of chemical fertilizers. On-station experiments were conducted to evaluate the performance of site-specific nutrient management (SSNM). Field and crop specific NPK rates were calculated using quantitative evaluation of fertility of tropical soils (QUEFTS) model. The yield advantage of SSNM over current recommendation was 8.16 t ha-1 (mean of 3 yrs). The increase in agronomic efficiency and recovery efficiency of N in SSNM over current recommendation was 24.1 kg kg-1 and 0.09 kg kg-1, respectively. Similar increases were observed for P and K. The results showed the potential of SSNM in increasing yield and nutrient use efficiency of white yam. [ABSTRACT FROM AUTHOR]

The field experiments were conducted to develop fertilizer best management practice (FBMP) by site specific nutrient management (SSNM) and SSNM zonation maps and customised fertilizer ratios at ICAR-CTCRI, Thiruvananthapuram, Kerala, India, during 2012-2015. The SSNM plot resulted in an average actual tuberous root yield of 36.80, 37.40 and 38.70 t/ha during 2012, 2013 and 2014 respectively against a yield target of 40 t/ha. The average yield difference between SSNM and PR for the three crops grown was 5.50 t/ha (17%, P=0.001) and was similar during all the three years. On an average, plant N accumulation increased by 48 kg/ha (25%, P=0.003), P accumulation by 9 kg/ha (50%, P=0.003), and K accumulation by 39 kg/ha (20%, P=0.005). Among the three crops grown, the AEN increased by 34 kg/kg (72%, P=0.002), REN by 0.04 kg/kg (8%, P=0.003) and PEN by 55 kg/kg (58%, P=0.005). The guidelines in estimating the fertilizer N, P2O5 and K2O required by cassava based on tuberous root yield response to fertilizer N, P and K and efficiency of fertilizer N, P and K use also were developed. Soil fertility maps and agro-ecological units, SSNM zonation maps and customised fertilizer ratios were also developed for all the major cassava-growing regions in India. [ABSTRACT FROM AUTHOR]

This study was conducted in order to determine the status of different pools of inorganic phosphorus in surface and sub-surface soils, changed over 3 years of intensive cropping with Rice-Maize-Green gram (residual) under site-specific nutrient management and work out the correlation among different pools of P. Postharvest soil properties indicated that soil pH was increasing gradually due to lime addition as one input, in spite of the removal of basic cations. There was small but a positive build up of organic carbon, available N and P. There was a drastic reduction in available K status in postharvest soil with available S were found. Phosphorus was fractionated broadly in two groups (a) Inorganic-P and (b) Organic-P. The inorganic P fractions constituted 71.9–86.0% of total P, whereas organic-P constituted 14.0–28.1% in surface soil and 75.9–81% and 18.1–24.1% in subsoil, respectively. The sequence of different pools in order of Fe-P > RS-P > Al-P > Ca-P > occluded-P > saloid-P (surface soil 0–15 cm) and RS-P > Fe-P > Ca-P > Occluded-P > Al-P > saloid-P (Subsurface soil 15–30 cm). Bray's extracted-P is positively correlated with Fe (r = 0.71*) and Al-P (r = 0.67*) indicating the major contribution of both fractions toward available P pool in surface soil, whereas, Al-P majorly contribute in sub-surface soil. Among the inorganic fractions of Phosphorus, Fe-P (r = 0.72*) are showing positive and significant relationship with total uptake of P, in surface soil and Al-P (0.70*) in sub-surface soil. Thus, understanding the use of both inorganic and organic fertilizer under intensive cropping for 3 years must be planned carefully because the interaction of different forms of P, in acidic soil and various other factors influencing P availability to plants. [ABSTRACT FROM AUTHOR]

Field experiments were conducted at the Annamalai University Experimental Farm, Annamalai nagar, Tamil Nadu to study the effect of Site Specific Nutrient Management for Transplanted Rice Cv. ADT 36 and ADT 43. There are fifteen treatments viz., T1 - 150: 15: 50 kg NPK ha-1, T2 - 150: 30: 50 kg NPK ha-1, T3 - 150: 45: 50 kg NPK ha-1, T4 - 150: 60: 50 kg NPK ha-1, T5 - 150: 15: 75 kg NPK ha-1, T6 - 150: 30: 75 kg NPK ha-1, T7 - 150:45:75 kg NPK ha-1, T8 - 150: 60: 75 kg NPK ha-1, T9 - 150: 15:100 kg NPK ha-1, T10 - 150:30:100 kg NPK ha-1, T11 - 150:45:100 kg NPK ha-1, T12 - 150:60:100 kg NPK ha-1, T13 - LCC:15:80 kg NPK ha-1, T14 - 150:50:50 kg NPK ha-1, T15 - control. The treatments were replicated thrice adopting randomized block design. The study revealed that in both the seasons, growth and yield components viz., plant height, number of tillers hill-1, leaf area index and dry matter production were increased by the application of T8 - 150: 60: 75 kg NPK ha-1. Application of treatment, T8 - 150: 60: 75 kg NPK ha-1 recorded the highest grain yield of 5280 and 5390 kg ha-1 with ADT 36 and ADT 43 respectively. The treatment T8, was followed by the treatment, T12 - 150:60:100 kg NPK ha-1. [ABSTRACT FROM AUTHOR]

System of Rice Intensification (SRI) was evaluated for the variety Hardinath-1 under nutrient omission treatment (-N, -P, -K, -Zn, -B and +All) through field experiment conducted in randomized complete block design (RCBD) with six treatments and four replications during spring season of 2018 at agriculture field of Sailaja Acharya Memorial Polytechnic, Siswani Jahada, Morang. The highest grain (4.62 t/ha) and straw yields (7.75 t/ha) were obtained from the fully fertilized plot (i. e.+All treatments) which might be due to application of all of the major nutrients in sufficient amount (i. e. 150 : 45 : 45 : 20 : 5 N : P : K : Zn : B kg/ha) along with following SRI principles. Omitting the major nutrients showed significantly lesser growth and yield parameters of rice. The P omitted treatment showed significantly higher sterility percentage (23.74) as P had major role on grain filling and development. The nutrients omission treatment also produced significant effect on plant uptake of N, P, K, Zn and B. Significantly higher uptake of N (76.73 kg/ha), P (38.28 kg/ha), K (52.41 kg/ha), Zn (0.086 kg/ha) and B (0.022 kg/ha) was obtained from fully fertilized plot. Based on plant uptake of nutrient on omission plot, native N, P, K, Zn and B supplying capacity of the soil at research plot was found to be 23.51, 25.94, 37.59, 0.075 and 0.019 kg/ha, respectively. The recovery efficiency of applied N, P, K, Zn and B was found to be 35.48, 5.7, 32.93, 0.06 and 0.06%, respectively. Owing to native nutrient supplying capacity of the soil at the research site, N, P, K, Zn and B required to obtain the target yield of 5 t/ha was found 139.5 kg N/ha, 45.22 kg P/ha, 50.8 kg K/ha, 12.1 kg Zn/ha and 2.6 kg B/ha, respectively. [ABSTRACT FROM AUTHOR]

Understanding, predicting, and managing the spatiotemporal complexity of stream thermal regimes requires monitoring strategies designed specifically to make inference about spatiotemporal variability on the whole stream network. Moreover, monitoring can be tailored to capture particular facets of this complex thermal landscape that may be important indicators for species and life stages of management concern. We applied spatial stream network models (SSNMs) to an empirical dataset of water temperature from the Snoqualmie River watershed, WA, and use results to provide guidance with respect to necessary sample size, location of new sites, and selection of a modeling approach. As expected, increasing the number of monitoring stations improved both predictive precision and the ability to estimate covariates of stream temperature; however, even relatively small numbers of monitoring stations, n = 20, did an adequate job when well-distributed and when used to build models with only a few covariates. In general, winter data were easier to model and, across seasons, mean temperatures were easier to model than summer maximums, winter minimums, or variance. Adding new sites was advantageous but we did not observe major differences in model performance for particular new site locations. Adding sites from parts of the river network with thermal regimes which differed from the rest of the network, and which were therefore highly influential, improved nearby predictions but reduced model-estimated precision of predictions in the rest of the network. Lastly, using models which accounted for the network-based spatial correlation between observations made it much more likely that estimated prediction confidence intervals covered the true parameter; the exact form of the spatial correlation made little difference. By incorporating spatial structure between observations, SSNMs are particularly valuable for accurate estimation of prediction uncertainty at unmeasured locations. Based on our results, we make the following suggestions for designing water temperature monitoring arrays: (1) make use of pilot data when possible; (2) maintain a distribution of monitors across the stream network (i.e., over space and across the full range of covariates); (3) maintain multiple spatial clusters for more accurately estimating correlation of nearby sites; (4) if sites are to be added, prioritize capturing a range of covariates over adding new tributaries; (5) maintain a sensor array in winter; and (6) expect reduced accuracy and precision when predicting metrics other than means. [ABSTRACT FROM AUTHOR]

On farm trials were conducted for two year to evaluate the effect of soil test based site specific nutrient management (SSNM) vis-a-vis other farmers practice on rice (Oryza sativa L.) and wheat (Triticum aestivum L.) productivity in villages of Ghaziabad (Uttar Pradesh) and Faridabad (Haryana). Results revealed that the SSNM led to significant increase in grain yield of rice and wheat crops in both the years as compared to the state recommended NPK fertilizer treatment and farmers practices. On an average, SSNM increased the grain yield of rice crop by 19.9% and 14.9% at Partapur and Badarpur said, respectively, over farmers practice. The corresponding increases in grain yield of wheat at Kumbhawas and Badarpur said were 13.9% and 15.4 %. The maximum net profits of Rupees 9 401/ha and Rupees 12 100/ha were obtained from rice under SSNM at Badarpur Said and Partapur, respectively. In wheat crop, the highest net profits of Rupees 6 346/ ha at Badarpur said and Rupees 4 346/ha at Kumbhawas were recorded under SSNM. The lowest yield and net profits from both the crops were obtained with farmers practices. The physicochemical properties (pH, EC and organic carbon) do not get deteriorated due to various nutrient management practices. Available major (NPK), secondary (S) and micronutrients (Zn and B) exhibited significant differences due to treatment variations. SSNM proved significantly superior in respect of buildup of these nutrients in soil.

Elephant foot yam [Amorphophallus paeoniifolius (Dennst.) Nicolson] is an important tuber crop grown in many parts of India for its starchy corm and the average yield of this crop is far below the potential productivity of 100 tonnes/ha. One of the reasons attributed to the lower yield is the imbalanced application of essential plant nutrients. Site specific nutrient management (SSNM) is the application of plant nutrients based on the soil and crop need, yield target and developed with the aid of models such as QUEFTS. This paper gives the result of four year study conducted to calibrate and validate the QUEFTS model for elephant foot yam cultivation and the development of SSNM zonation maps and secondary and micronutrient fortified customised fertilizers for the crop in major growing environments of India. Data collected from different field experiments conducted in major elephant foot yam-production regions of India during 1968 to 2011 were used to calibrate the model. The derived parameters of minimum and maximum accumulation of N (130 and 460), P (900 and 2100) and K (100 and 170) are proposed as standard borderline values in the QUEFTS model for elephant foot yam. A linear increase in corm yield was suggested by the model with N, P and K uptakes of 3.97, 0.71, and 7.05 kg N, P and K/1 000 kg corm. The average NPK ratio in total plant dry matter was 5.56:1:9.88. Based on these results, the model was calibrated using historical data as well as by conducting field experiments. It can be observed that fertilizer best management practices by SSNM resulted in an average actual corm yield of 33.45 tonnes/ha, whereas, the model predicted a yield of 35 tonnes/ha. The results of the study showed good agreement between predicted and measured corm yields during the four years, which indicated that the calibrated model can be used to improve NPK fertilizer recommendations for elephant foot yam in India. Based on the results and using soil fertility maps and agro ecological unit maps, SSNM zonation maps and secondary and micronutrient fortified customised fertilizers were developed for major elephant foot yam growing environments of India.

The present experiment was carried out during 2013-14 and 2014-15 at Norman E. Borlaug Crop Research Centre, Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India representing the Tarai belt of Uttarakhand. The experiment was laid out in a split plot design with 18 treatments with 06 tillage options i.e. FIRBS, conventional tillage (CT), sub soiling (SS), zero tillage (ZT), minimum tillage with two pass of rotavator (MT) and permanent raised bed system (PRBS) in main plot and 03 nutrient levels i.e. 50% of recommended dose of fertilizers (50% RDF), site specific nutrient management (SSNM) and 100% recommended dose of fertilizer (RDF) in sub plot with three replications. The tillage options and nutrients levels influenced significantly most of the growth and yield attributes. Based on pooled values PRBS the highest grain yield that was statistically equal to FIRBS and SS. In general wheat grain yield was almost equal at PRBS, FIRBS and SS that was 9.5 and 16% and 32.5% greater than CT, MT and ZT respectively. The harvest index was also recorded significantly highest under FIRBS followed by PRBS and Nutrient use efficiency was recorded highest under PRBS followed by SS and FIRBS, though remained non significant on pooled basis. Similarly RDF produced highest grain yield on pooled basis that was 11.7 and 48.6% higher than SSNM and 50% RDF, respectively but the harvest index was found significantly highest under at 50% RDF. The Nutrient use efficiency was recorded significantly highest under 50% during growth stages. [ABSTRACT FROM AUTHOR]

Nitrogen (N) fertilizer has been excessively used in China's crop production, resulting in nonpoint pollution and significant greenhouse gas emissions. Previous studies show that farmers can reduce N-fertilizer upon receiving knowledge training. However, there is little evidence of the effectiveness of this effort in the long term. Based on an experimental study of site-specific nutrient management for rice production in China and a unique household dataset captured over seven years, this study shows that the traditional training approach has not been effective in reducing Chinese farmers' N-fertilizer use. Persistently reducing farmers' excessive use of N-fertilizer in the long term will require intensive in-field guidance – something that requires substantial investment and institutional innovation. [ABSTRACT FROM AUTHOR]

Behaviour of crops and soils due to non-redressal of spatial variability in soil properties is not expressed uniformly within bigger orchard/field which has been the major triggering factor for not able to break yield barrier through conventional nutrient management strategy. Better response of site specific nutrient management (SSNM) over recommended doses of fertilizers (RDF) including those of farmers’ usual fertilizer practices (FFP) signals a wake-up call to address the fertilizer requirements on the principles of SSNM, if the full potential productivity of perennial crops has to be realized and sustain on any given soil type. The SSNM also warrants to tailor the fertilizer application on the basis of spatial variation in soil fertility and tree canopy size within an orchard/field through variable rate fertilizer application to minimize the gap between actual and potential productivity of crops. Application of geospatial tools like GPS and GIS coupled with DRIS- based nutrient diagnostics has substantially aided in developing rationale of fertilizer use using SSNM concept in crops like citrus, avocado, coconut, olive etc. which could well be expanded to other perennial crops.

The experiment was conducted during kharif 2011 in sandy clay loam soil at Zonal Agricultural Research Station, UAS, GKVK campus, Bengaluru, Karnataka to study yield attributes, nutrient uptake and yield of maize (Zea mays L.) hybrid NAH 1137 (Hema) and economics of maize cultivation as influenced by nutrient management through various fertilization approaches. The investigation consisted SSNM through fertilizers for targeted yield of 8 and 10 t ha-1, SSNM through INM for targeted yield of 8 and 10 t ha-1, STCR through fertilizers for targeted yield of 8 and 10 t ha-1, STCR through INM for targeted yield of 8 and 10 t ha-1. These eight treatments were compared with 100 per cent RDF and100 per cent RDF + FYM @ 10 t ha-1, and were replicated thrice. Among the treatments, SSNM through fertilizers for targeted yield of 10 t ha-1 recorded higher dry matter production (501.4 g), cob length (20.3 cm), number of grain rows cob-1 (20.5), number of grains row-1 (41.3), number of grains plant-1 (891.2) test weight (32.9 g) and significantly higher grain yield (9.77 t ha-1) as compared to other treatments. Whereas, significantly lower cob length (16.5 cm) number of grain rows cob-1 (13.6), number of grains row-1 (31.4), number of grains plant-1 (426.7) test weight (21.4 g), Grain yield (4.04 t ha-1) were recorded in the treatment with the application of 100 % RDF. Significantly higher nutrient uptake (504.8, 103.1 and 212.3 N, P and K kg ha-1) was recorded in SSNM through fertilizers for targeted yield of 10 t ha-1 over 100 % RDF (219.4, 32.2 and 73; N, P and K kg ha-1) and economics. [ABSTRACT FROM AUTHOR]

The experiment was conducted during kharif 2011 in sandy clay loam soil at Zonal Agricultural Research Station, UAS, GKVK campus, Bengaluru, Karnataka to study yield attributes, nutrient uptake and yield of maize (Zea mays L.) hybrid NAH 1137 (Hema) and economics of maize cultivation as influenced by nutrient management through various fertilization approaches. The investigation consisted SSNM through fertilizers for targeted yield of 8 and 10 t ha-1, SSNM through INM for targeted yield of 8 and 10 t ha-1, STCR through fertilizers for targeted yield of 8 and 10 t ha-1, STCR through INM for targeted yield of 8 and 10 t ha-1. These eight treatments were compared with 100 per cent RDF and100 per cent RDF + FYM @ 10 t ha-1, and were replicated thrice. Among the treatments, SSNM through fertilizers for targeted yield of 10 t ha-1 recorded higher dry matter production (501.4 g), cob length (20.3 cm), number of grain rows cob-1 (20.5), number of grains row-1 (41.3), number of grains plant-1 (891.2) test weight (32.9 g) and significantly higher grain yield (9.77 t ha-1) as compared to other treatments. Whereas, significantly lower cob length (16.5 cm) number of grain rows cob-1 (13.6), number of grains row-1 (31.4), number of grains plant-1 (426.7) test weight (21.4 g), Grain yield (4.04 t ha-1) were recorded in the treatment with the application of 100% RDF. Significantly higher nutrient uptake (504.8, 103.1 and 212.3 N, P and K kg ha-1) was recorded in SSNM through fertilizers for targeted yield of 10 t ha-1 over 100% RDF (219.4, 32.2 and 73; N, P and K kg ha-1) and economics. [ABSTRACT FROM AUTHOR]

Fertilizer K and P requirements for rice ( Oryza sativa L.) can be determined with site-specific nutrient management (SSNM) using estimated target yield, nutrient balances, and yield gains from added nutrient. We used the QUEFTS (QUantitative Evaluation of the Fertility of Tropical Soils) model with >8000 plot-level observations to estimate the relationship between grain yield and nutrient accumulation in above-ground dry matter of irrigated rice with harvest index ≥ 0.4. Predicted reciprocal internal efficiencies (RIEs) at 60–70% of yield potential corresponded to plant accumulation of 14.6 kg N, 2.7 kg P, and 15.9 kg K per tonne of grain yield. These RIEs enable determination of plant requirements for K and P and net output of K and P in harvested grain and removed crop residues at a target yield. Yield gains for nutrient applied to irrigated rice averaged 12% for K and 9% for P for 525 to 531 observations. For fields without certain yield gain, fertilizer K and P requirements can be determined by a partial maintenance approach (i.e., fertilizer input < output in nutrient balance), which considers nutrient supply mediated through soil processes and balances trade-offs between financial loss with full maintenance rates and risk of excessive nutrient depletion without nutrient application. When yield gains to an added nutrient are certain, partial maintenance plus yield gain can be used to determine fertilizer requirements. The SSNM-based approach and algorithms enable rapid development of field-specific K and P management. [ABSTRACT FROM AUTHOR]

Crop modelling can provide us with a vast amount of real-time information about fertilizer dose to achieve the target yield, crop conditions, etc. Nutrient-use efficiency in wheat is low because of conventional blanket and imbalanced fertilizer use. Estimation of fertilizer requirements based on quantitative approaches can assist in improving yields and nutrient-use efficiency. Field experiments were conducted in 20 sites in eastern India to assess the soil supply, requirement and internal efficiency of nitrogen (N), phosphorous (P), potassium (K) and zinc (Zn) in wheat. The data were used to calibrate the QUEFTS (QUantitative Evaluation of the Fertility of Tropical Soils) model for site-specific, balanced fertilizer recommendations. The parameters of maximum accumulation (a) and maximum dilution (d) in wheat were calculated for N (35 and 100 kg), P (129 and 738 kg), K (17 and 56 kg), and Zn (21502 and 140244 mg). Grain yield of wheat showed good correlation with N (R² = 0.937**), P (R² = 0.901**) and K uptake (R² = 0.801**). The NPK ratio to produce 1 ton grain yield of wheat was derived to be 4.9:1.0:8.9. The relationships between chemical properties and nutrient supplying capacity of soils were also established. The model was validated using the data of four other experiments. Observed yields with different amounts of N, P, K and Zn were in good agreement with the predicted values suggesting that the validated QUEFTS model can be used for site-specific nutrient management in wheat. [ABSTRACT FROM AUTHOR]

Wheat is the third most important cereal crop in Nepal after rice and maize both in area and production, but its productivity of 2.3 tonne ha - 1 is very less compared to other developed countries (6 tonne ha -1 for Switzerland and China) in the world. The main cause of low wheat yield in Nepal is the improper and inadequate use of fertilizer devoid of site specific nutrient management practices. Therefore, a farmers' field experiment was conducted during November 2015 to April 2016 to rectify the best fertilizer management options at two sites of Damak and Gauradaha in Jhapa district in eastern-Terai of Nepal using Nutrient Expert®-Wheat model. The research was accomplished in Randomized Complete Block Design with 2 treatments and 20 replications, considering farmers' field as replication. Two treatments included in the experimentation were NE® (Nutrient Expert Recommendation) and FFP (Farmer's Fertilizer Practices). The statistical result revealed the highly significant difference in terms of number of effective tiller m -2 , plant height, filled grain per spike, spike length, grain, straw and biological yields and harvest index. The highest yield (4.71 tonne ha -1 ) was obtained from NE field than FFP (2.99 tonne ha -1 ). On an average, NE based practices produced 58 % higher yield in comparison to FFP. NE based treatment produced significantly higher biomass yield, yield attributes and cost-benefit ratio than FFP treatments. Field experiment validation confirmed that the Nutrient Expert® Wheat model could be used as the most adoptable and practical precision decision support system tool to make a more authentic fertilizer recommendation in eastern-Terai of Nepal., (© 2020 The Author(s).)