Publications
Peer-reviewed Journal Papers
Bold: Rajan Lab Member
[68] Siegfried, J., Adams, C. B., Rajan, N., Hague, S., Schnell, R., & Hardin, R. (2023). Combining a cotton ‘Boll Area Index’ with in-season unmanned aerial multispectral and thermal imagery for yield estimation. Field Crops Research, 291, 108765. https://doi.org/10.1016/j.fcr.2022.108765
[67] Lee, C. L., Strong, R., Briers, G., Murphrey, T., Rajan, N., & Rampold, S. (2023). A Correlational Study of Two US State Extension Professionals’ Behavioral Intentions to Improve Sustainable Food Chains through Precision Farming Practices. Foods, 12(11), 2208. https://doi.org/10.3390/foods12112208
[66]Sapkota, B. R., Adams, C. B., Kelly, B., Rajan, N., & Ale, S. (2023). Plant population density in cotton: Addressing knowledge gaps in stand uniformity and lint quality under dryland and irrigated conditions. Field Crops Research, 290, 108762. https://doi.org/10.1016/j.fcr.2022.108762
[65] Raub, H. D., Rajan, N., Mcinnes, K. J., & West, J. B. (2023). Excess energy and photosynthesis: responses to seasonal water limitations in co-occurring woody encroachers of the semi-arid Southern Great Plains. Photosynthetica, 61(3):285-296. http://DOI:10.32615/ps.2023.018
[64] Shrestha, R., Adams, C. B., Abello, F., DeLaune, P. B., Trostle, C., Rajan, N., ... & Ravelombola, W. (2023). Intensifying dryland wheat systems by integrating guar increased production and profitability. Industrial Crops and Products, 197, 116608. https://doi.org/10.1016/j.indcrop.2023.116608
[63] Hinson, P. O., Adams, C. B, Pinchak, B., Jones, D., Rajan, N., Somenahally, A., & Kimura, E. (2022). Organic transition in dual-purpose wheat systems: Agronomic performance and soil nitrogen dynamics, Agronomy Journal, 114(4), 2484-2500. https://doi.org/10.1002/agj2.21093
[62] Pokhrel, P., Rajan, N., Jifon, J., Rooney, W., Jessup, R., da Silva, J., Enciso, J., & Attia, A. (2022). Evaluation of the DSSAT‐CANEGRO model for simulating the growth of energy cane (Saccharum spp.), a biofuel feedstock crop. Crop Science, 62(1), 466-478. https://doi.org/10.1002/csc2.20648
[61] Menefee, D., Rajan, N., Shafian, S., & Cui, S. (2022). Modeling carbon uptake of dryland maize using high resolution satellite imagery. Frontiers in Remote Sensing, 11. https://doi.org/10.3389/frsen.2022.810030
[60] Li, Z., Xu, S., Rajan, N., Nair, S., Jagadamma, S., Nave, R.,Kubesch, J., Bates, G., McIntosh, D., Chen, C., & Cui, S. (2022). Productivity and nutritive value of no-input minimum tillage organic forage systems. Nutrient Cycling in Agroecosystems, 124(3), 335-357. https://doi.org/10.1007/s10705-022-10235-z
[59] Li, Z., Menefee, D., Yang, X., Cui, S., & Rajan, N. (2022). Simulating productivity of dryland cotton using APSIM, climate scenario analysis, and remote sensing. Agricultural and Forest Meteorology, 325, 109148. https://doi.org/10.1016/j.agrformet.2022.109148
[58] Chavez, J. C., Ganjegunte, G. K., Jeong, J., Rajan, N., Zapata, S. D., Ruiz-Alvarez, O., & Enciso, J. (2022). Radiation use efficiency and agronomic performance of biomass sorghum under different sowing dates. Agronomy, 12(6), 1252. https://doi.org/10.3390/agronomy12061252
[57] Bagnall, G. C., Altobelli, S. A., Conradi, M. S., Fabich, H. T., Fukushima, E., Koonjoo, N., Kuethe, D. O., Rooney, W. L., Stupic, K. F., Sveinsson, B., Weers, B., Rajan, N., Rosen M. S. & Morgan, C. L. (2022). Design and demonstration of a low‐field magnetic resonance imaging rhizotron for in‐field imaging of energy sorghum roots. The Plant Phenome Journal, 5(1), e20038. https://doi.org/10.1002/ppj2.20038
[56] Simoneaux, B., Neely, C., Ibrahim, A. M., Rajan, N., & Popescu, S. (2022). Comparing mechanical harvest with alternative ground‐based methods for estimating forage yields in cool‐season annual grasses. Agrosystems, Geosciences & Environment, 5(1), e20250. https://doi.org/10.1002/agg2.20250
[55] MacMillan, J., Adams, C. B., Hinson, P. O., DeLaune, P. B., Rajan, N., & Trostle, C. (2022). Biological nitrogen fixation of cool‐season legumes in agronomic systems of the Southern Great Plains. Agrosystems, Geosciences & Environment, 5(1), e20244. https://doi.org/10.1002/agg2.20244
[54] Sapkota, B. B., Popescu, S., Rajan, N., Leon, R. G., Reberg-Horton, C., Mirsky, S., & Bagavathiannan, M. V. (2022). Use of synthetic images for training a deep learning model for weed detection and biomass estimation in cotton. Scientific Reports, 12(1), 1-18. https://doi.org/10.1038/s41598-022-23399-z
[53] Menefee, D., Rajan, N., Cui, S., Bagavathiannan, M., Schnell, R., & West, J. (2021). Simulation of dryland maize growth and evapotranspiration using DSSAT-CERES-Maize model. Agronomy Journal, 113, 1317-1332. https://doi.org/10.1002/agj2.20524
[52] Zapata, D., Rajan, N., Mowrer, J., Casey, K., Schnell, R., & Hons, F. (2021). Long-term tillage effect on with-in season variations in soil conditions and respiration from dryland winter wheat and soybean cropping systems. Scientific Reports, 11, 2344. https://doi.org/10.1038/s41598-021-80979-1
[51] Adams, C. B., Ritchie, G. L., & Rajan, N. (2021). Cotton phenotyping and physiology monitoring with a proximal remote sensing system. Crop Science, 61, 1317-1327. https://doi.org/10.1002/csc2.20434
[50] Shrestha, R., Adams, C. B., & Rajan, N. (2021). Does the drought tolerance of Guar [Cyamopsis tetragonoloba (L.) Taub.] extend belowground to root nodules? Journal of Agronomy and Crop Science, 00, 1-10. https://doi.org/10.1111/jac.12494
[49] Cui, X., Goff, T., Cui, S., Menefee, D., Wu, Q., Rajan, N., Nair, S., Phillips, N., & Walker, F. (2021). Predicting carbon and water vapor fluxes using machine learning and novel feature ranking algorithms. Science of the Total Environment, 145130. https://doi.org/10.1016/j.scitotenv.2021.145130
[48] Yang, X., Menefee, D., Cui, S., Rajan, N., & Fletcher, A. (2021). Assessing the impacts of projected climate changes on Maize (Zea mays) productivity using crop models and climate scenario simulation. Crop and Pasture Science, 72(12), 969-984. https://doi.org/10.1071/CP21279
[47] MacMillan, J., Adams, C. B., Trostle, C., & Rajan, N. (2021). Testing the efficacy of existing USDA Rhizobium germplasm collection accessions as inoculants for guar. Industrial Crops and Products, 161:113205. https://doi.org/10.1016/j.indcrop.2020.113205
[46] Govindasamy, P., Mowrer, J., Provin, T., Hons, F., Rajan, N., & Bagavathiannan, M. (2021). Soil carbon improvement under long-term (36 years) no-till sorghum production in a sub-tropical environment. Soil Use and Management, 37, 37-48. https://doi.org/10.1111/sum.12636
[45] Govindasamy, P., Mowrer, J., Rajan, N., Provin, T., Hons, F., & Bagavathiannan, M. (2021). Influence of long-term (36 years) tillage practices on soil physical properties in a grain sorghum experiment in Southeast Texas. Archives of Agronomy and Soil Science, 67, 234-244. https://doi.org/10.1080/03650340.2020.1720914
[44] Menefee, D., Rajan, N., Cui, S., Bagavathiannan, B., Schnell, R., & West, J. (2020). Carbon exchange of a dryland cotton field and its relationship with PlanetScope remote sensing data. Agricultural and Forest Meteorology, 294, 108130. https://doi.org/10.1016/j.agrformet.2020.108130
[43] Pokhrel, P., Rajan, N., Jifon, J., Rooney, W. L., Jessup, R., da Silva, J., Enciso, J., & Attia, A. (2020). Agronomic performance of the lignocellulosic feedstock crop energy cane in the Texas Rolling Plains. Agronomy Journal, 112, 3816-3831. https://doi.org/10.1002/agj2.20370
[42] Bhandari, M., Ibrahim, A. M. H., Xue, Q., Jinha, J., Chang, A., Rudd, J. C., Maeda, M., Rajan, N., Neely, H., & Landivar, J. (2020). Assessing winter wheat foliage disease severity using aerial imagery acquired from small Unmanned Aerial Vehicle (UAV). Computers and Electronics in Agriculture, 176,105665. https://doi.org/10.1016/j.compag.2020.105665
[41] Singh, V., Rana, A., Bishop, M., Filippi, A., Cope, D., Rajan, N., & Bagavathiannan, M. (2020). Unmanned aircraft systems for precision weed detection and management: Prospects and challenges. Advances in Agronomy, 159, 93-134. https://doi.org/10.1016/bs.agron.2019.08.004
[40] Han, X., Thomasson, J. A., Swaminathan, V., Wang, T., Siegfried, J., Raman, R., Rajan, N., & Neely, H. (2020). Field-based calibration of unmanned aerial vehicle thermal infrared imagery with temperature-controlled references. Sensors, 20, 7098. https://doi.org/10.3390/s20247098
[39] Sapkota, B., Singh, V., Clark, N., Rajan, N., and Bagavathiannan, M., (2020). Detection of Italian ryegrass in wheat and prediction of competitive interactions using remote-sensing and machine-learning techniques. Remote Sensing, 12, 2977. https://doi.org/10.3390/rs12182977
[38] Anapalli S.S., Fisher, D. K., Reddy, K. N., Rajan, N., & Pinnamaneni, S. R. (2019). Modeling evapotranspiration for irrigation water management in a humid climate. Agricultural Water Management, 225,105731. https://doi.org/10.1016/j.agwat.2019.105731
[37] Kothatri, K., Ale, S., Attia, A., Rajan, N., Xue, Q., & Munster, C. L. (2019). Potential climate change adaptation strategies for winter wheat production in the Texas High Plains. Agricultural Water Management, 225,105764. https://doi.org/10.1016/j.agwat.2019.105764
[36] Chavez J.C., Enciso, J., Ganjegunte, G., Rajan, N., Jifon, J., & Singh, V. P. (2019). Growth response and productivity of sorghum for bioenergy production in South Texas. Transactions of ASABE, 62, 1207–1218. https://doi.org/10.13031/trans.13317
[35] Olanrewaju, S., Rajan, N., Ibrahim, A. M. H., Rudd, J. C., Liu, S., Sui, R., Jessup, K.E., & Xue, Q. (2019). Using aerial imagery and digital photography to monitor growth and yield in winter wheat. International Journal of Remote Sensing, 40, 6905-6929. https://doi.org/10.1080/01431161.2019.1597303
[34] Sharma, S., Rajan, N., Cui, S., Mass, S., Casey, K., Ale, S., & Jessup, R. (2019). Carbon and evapotranspiration dynamics of a non-native perennial grass with biofuel potential in the southern U.S. Great Plains. Agricultural and Forest Meteorology, 269-270, 285-293. https://doi.org/10.1016/j.agrformet.2019.01.037
[33] Shafian, S., Rajan, N., Schnell, R., Bagavathiannan, M., Valasek, J., Shi, Y., & Olsenholler, J. (2018). Unmanned aerial systems-based remote sensing for monitoring sorghum growth and development. PloS ONE, 13(5), e0196605. https://doi.org/10.1371/journal.pone.0196605
[32] Chen, Y., Ale, S., & Rajan, N. (2018). Implications of biofuel-induced changes in land use and crop management on sustainability of agriculture in the Texas High Plains. Biomass and Bioenergy, 111, 13-21. https://doi.org/10.1016/j.biombioe.2018.01.012
[31] Sharma, S., Rajan, N., Cui, S., Casey, K., Ale, S., Jessup, R., & Maas, S. (2017). Seasonal variability of evapotranspiration and carbon exchanges over a biomass sorghum field in the Southern U.S. Great Plains. Biomass and Bioenergy, 105, 392-401. https://doi.org/10.1016/j.biombioe.2017.07.021
[30] Glimanov, T.G., Morgan, J. A., Hanan, N. P., Wylie, B. K., Rajan, N., Smith, D. P., & Howard, D. M. (2017). Productivity and CO2 exchange of Great Plains ecoregions. I. Shortgrass steppe: Flux tower estimates. Rangeland Ecology & Management, 70, 700-717. https://doi.org/10.1016/j.rama.2017.06.007
[29] Chen, Y., Ale, S., Rajan, N., & Srinivasan, R. (2017). Modeling the effects of land use change from cotton (Gossypium hirsutum L.) to perennial bioenergy grasses on watershed hydrology and water quality under changing climate. Agricultural Water Management, 192,198-208. https://doi.org/10.1016/j.agwat.2017.07.011
[28] Modala, N. R., Ale, S., Goldberg, D. W., Olivares, M., Munster, C. L., Rajan, N., & Feagin, R. A. (2017). Climate change projections for the Texas High Plains and Rolling Plains. Theoretical and Applied Climatology, 129, 263-280. https://doi.org/10.1007/s00704-016-1773-2
[27] Chen, Y., Ale, S., Rajan, N., & Munster, C. (2017). Assessing the hydrologic and water quality impacts of biofuel-induced changes in land use and management. Global Change Biology-Bioenergy, 9, 1461-1475. https://doi.org/10.1111/gcbb.12434
[26] Shi, Y., Thomasson, J. A., Murray, S. C., Pugh, N. A., Rooney, W. L., Shafian, S., Rajan, N., Rouze, G., Morgan, C. L., Neely, H. L., Rana, A., Bagavathiannan, M. V., Henrickson, J., Bowden, E., Valasek, J., Olsenholler, J., Bishop, M. P., Sheridan, R., Putman, E. B., Popescu, S., Burks, T., Cope, D., Ibrahim, A., Mccutchen, B. F., Baltensperger, D. D., Avant, Jr. R. V., Vidrine, M., & Yang, C. (2016). Unmanned aerial vehicles for high-throughput phenotyping and agronomic research. PloS ONE, 11(7), e0159781. https://doi.org/10.1371/journal.pone.0159781
[25] Attia, A., & Rajan, N. (2016). Within-season growth and spectral reflectance of cotton and their relation to lint yield. Crop Science, 56, 2688-2701. https://doi.org/10.2135/cropsci2015.05.0296
[24] Attia, A., Rajan, N., Nair, S. S., DeLaune, P. B., Xue, Q., Ibrahim, A. M. H., & Hays, D. B. (2016). Modeling cotton lint yield and water use efficiency responses to irrigation scheduling using Cotton2K. Agronomy Journal, 108:1614-1623. https://doi.org/10.2134/agronj2015.0437
[23] Chen, Y., Ale, S., & Rajan, N. (2016). Spatial variability of biofuel production potentials and hydrologic fluxes of land use change from cotton (Gossypium hirsutum L.) to Alamo switchgrass (Panicum virgatum L.) in the Texas High Plains. Bioenergy Research, 9: 1126-1141. https://doi.org/10.1007/s12155-016-9758-7
[22] Chen, Y., Ale, S., Rajan, N., Morgan, C. L. S., & Park, J. (2016). Hydrological responses of land use change from upland cotton to cellulosic bioenergy crops in the Southern High Plains of Texas. Global Change Biology-Bioenergy, 8(5): 981-999. https://doi.org/10.1111/gcbb.12304
[21] Attia, A., Rajan, N., Xue, Q., Nair, S., Ibrahim, A., & Hays, D. (2016). Application of DSSAT-CERES-Wheat model to simulate winter wheat response to irrigation scheduling in the Texas High Plains. Agricultural Water Management, 165: 50-60. https://doi.org/10.1016/j.agwat.2015.11.002
[20] Adhikari, P., Ale, S., Bordovsky, J. P., Thorp, K. R., Modala, N. R., Rajan, N., & Barnes, E. M. (2016). Simulating future climate change impacts on seed cotton yields in the Texas High Plains using the CSM-CROPGRO-Cotton model. Agricultural Water Management, 164, 317-330. https://doi.org/10.1016/j.agwat.2015.10.011
[19] Attia, A., Rajan, N., Ritchie, G., Cui, S., Ibrahim, A., Hays, D., Xue, Q., & Wilborn, J. (2015). Yield, quality, and spectral reflectance responses of cotton under sub-surface irrigation. Agronomy Journal, 107, 1355-1364. https://doi.org/10.2134/agronj14.0502
[18] Rajan, N., Maas, S., Kellison, R., Dollar, M., Cui, S., Sharma, S., & Attia, A. (2015). Emitter uniformity and application efficiency for center-pivot irrigation systems. Irrigation and Drainage, 64, 353-361. https://doi.org/10.1002/ird.1878
[17] Rajan, N., Maas, S. J., & Cui, S. (2015). Extreme drought effects on evapotranspiration and energy balance of a pasture in the Southern Great High Plains. Ecohydrology, 8, 1194-1204. https://doi.org/10.1002/eco.1574
[16] Modala, N. R., Ale, S., Rajan, N., Munster, C. L., DeLaune, P. B., Thorp, K. R., Nair, S. S., & Barnes, E. M. (2015). Evaluation of the csm-cropgro-Cotton model for the Texas Rolling Plains region and simulation of deficit irrigation strategies for increasing water use efficiency. Transactions of ASABE, 58, 685-696. https://doi.org/10.13031/trans.58.10833
[15] Sharma, B., Ritchie, G. L., & Rajan, N. (2015). Near-remote green: Red PVI ground cover fraction estimation. Crop Science, 55, 2252-2261. https://doi.org/10.2135/cropsci2014.09.0625
[14] Govind, A., Cowling, S., Kumari, J., Rajan, N., & Al-Yaari, A. (2015). Distributed modeling of ecohydrological processes at high spatial resolution over a landscape having patches of managed forest stands and crop fields in SW Europe. Ecological Modelling, 297: 126-140. https://doi.org/10.1016/j.ecolmodel.2014.10.019
[13] Rajan, N., Puppala, N., Maas, S., Payton, P., & Nuti, R. (2014). Aerial remote sensing of peanut ground cover. Agronomy Journal, 106, 1358-1364. https://doi.org/10.2134/agronj13.0532
[12] Rajan, N., & Maas, S. (2014). Spectral crop coefficient approach for estimating daily crop water use. Advances in Remote Sensing, 3, 197-207. http://dx.doi.org/10.4236/ars.2014.33013
[11] Thorp, K. R., Ale, S., Bange, M. P., Barnes, E. M., Hoogenboom, G., Lascano, R. J., McCarthy, A. C., Nair, S., Paz, J. O., Rajan, N., Reddy, K. R., Wall, G., & White, J. (2014). Development and application of process-based simulation models for cotton production: A review of past, present, and future directions. Journal of Cotton Science, 18, 10-47. http://www.cotton.org/journal/2014-18/1/
[10] Cui, S., Rajan, N., Maas, S. J., & Youn, E. (2014). An automated soil line identification method using relevance vector machine. Remote Sensing Letters, 5, 175-184. https://doi.org/10.1080/2150704X.2014.890759
[9] Rajan, N., Maas, S. J., & Cui, S. (2013). Extreme drought effects on carbon dynamics of a pasture in the semi-arid Southern High Plains. Agronomy Journal, 105, 1749-1760. https://doi.org/10.2134/agronj2013.0112
[8] Snowden, C., Ritchie, G. L., Cave, J., Keeling, W., & Rajan, N. (2013). Boll distribution effects on yield and micronaire under multiple irrigation levels. Agronomy Journal, 105, 1536-1544. https://doi.org/10.2134/agronj2013.0084
[7] Chen, Y., Hu, W., Huang, B., Weindorf, D. C., Rajan, N., Liu, X., & Niedermann, S. (2013). Accumulation and health risk of heavy metals in vegetables from harmless and organic vegetable production systems of China. Ecotoxicology and Environmental Safety, 98, 324-330. https://doi.org/10.1016/j.ecoenv.2013.09.037
[6] Padilla, F.L.M., Maas, S. J., González-Dugo, M. P., Mansilla, F., Rajan, N., Gavilán, P., & Domínguez, J. (2012). Monitoring regional wheat yield in Southern Spain using the GRAMI model and satellite imagery. Field Crops Research, 130, 145-154. https://doi.org/10.1016/j.fcr.2012.02.025
[5] Chaudhuri, S., Ale, S., DeLaune, P., & Rajan, N. (2012). Spatio-temporal variability of groundwater nitrate concentration in Texas: 1960 to 2010. Journal of Environmental Quality, 41, 1806-1817. https://doi.org/10.2134/jeq2012.0022
[4] Rajan, N., Maas, S. J., & Kathilankal, J. C. (2010). Estimating crop water use of cotton in the Southern High Plains. Agronomy Journal, 102, 1641-1651. https://doi.org/10.2134/agronj2010.0076
[3] Maas, S. J., & Rajan, N. (2010). Normalizing and converting image dc data using scatter plot matching. Remote Sensing 2, 1644-1661. https://doi.org/10.3390/rs2071644
[2] Rajan, N., & Maas, S. J. (2009). Mapping crop ground cover using airborne multispectral digital imagery. Precision Agriculture, 10, 304-318. https://doi.org/10.1007/s11119-009-9116-2
[1] Maas, S. J., & Rajan, N. (2008). Estimating ground cover of field crops using medium-resolution multispectral satellite imagery. Agronomy Journal 100, 320-327. https://doi.org/10.2134/agronj2007.0140