Search Results (2)

Efficient use of available resources in agricultural production is important to minimize carbon footprint considering the state of climate change. In this context, the current research was conducted to identify carbon and energy-efficient fodder cropping systems for sustainable livestock production. Annual monocropping, perennial monocropping, annual cereal + legume intercropping and perennial cereal + legume intercropping systems were evaluated by employing a randomized complete block design with three replications under field conditions. The lucerne (Medicago sativa L.) monocropping system recorded significantly lower carbon input (274 kg-CE ha⁻¹ year⁻¹) and showed higher carbon indices viz., carbon sustainability index (165.8), the carbon efficiency ratio (166.8) and carbon efficiency (347.5 kg kg-CE⁻¹) over other systems. However, higher green fodder biomass led to statistically higher carbon output (78,542 kg-CE ha⁻¹ year⁻¹) in the Bajra–Napier hybrid (Pennisetum glaucum × Pennisetum purpureum) + lucerne perennial system. Similar to carbon input, lower input energy requirement (16,106 MJ ha⁻¹ year⁻¹) and nutrient energy ratio (25.7) were estimated with the lucerne perennial system. However, significantly higher energy output (376,345 and 357,011 MJ ha⁻¹ year⁻¹) and energy indices viz., energy use efficiency (13.3 and 12.2), energy productivity (5.8 and 5.3 kg MJ⁻¹), net energy (327,811 and 347,961 MJ ha⁻¹ year⁻¹) and energy use efficiency (12.3 and 11.2) were recorded with Bajra–Napier hybrid + legume [lucerne and cowpea (Vigna unguiculata (L.) Walp.)] cropping systems, respectively. However, these systems were on par with the lucerne monocropping system. Additionally, Bajra–Napier hybrid + legume [cowpea, sesbania (Sesbania grandiflora (L.) Pers.) and lucerne] cropping systems also showed higher human energy profitability. Concerning various inputs’ contribution to total carbon and energy input, chemical fertilizers were identified as the major contributors (73 and 47%), followed by farmyard manure (20 and 22%) used to cultivate crops, respectively, across the cropping systems. Extensive use of indirect (82%) and non-renewable energy sources (69%) was noticed compared to direct (18%) and renewable energy sources (31%). Overall, perennial monocropping and cereal + legume cropping systems performed well in terms of carbon and energy efficiency. However, in green biomass production and carbon and energy efficiency, Bajra–Napier hybrid + legume (lucerne and cowpea) cropping systems were identified as the best systems for climate-smart livestock feed production. Full article

Hyperspectral remote sensing is considered to be an effective tool in crop monitoring and estimation of biomass. Many of the previous approaches are from single year or single date measurements, even though the complete crop growth with multiple years would be required for an appropriate estimation of biomass. The aim of this study was to estimate the fresh matter biomass (FMB) by terrestrial hyperspectral imaging of the three crops (lablab, maize and finger millet) under different levels of nitrogen fertiliser and water supply. Further, the importance of the different spectral regions for the estimation of FMB was assessed. The study was conducted in two experimental layouts (rainfed (R) and irrigated (I)) at the University of Agricultural Sciences, Bengaluru, India. Spectral images and the FMB were collected over three years (2016–2018) during the growing season of the crops. Random forest regression method was applied to build FMB models. R² validation (R²_val) and relative root mean square error prediction (rRMSEP) was used to evaluate the FMB models. The Generalised model (combination of R and I data) performed better for lablab (R²_val = 0.53, rRMSEP = 13.9%), maize (R²_val = 0.53, rRMSEP = 18.7%) and finger millet (R²_val = 0.46, rRMSEP = 18%) than the separate FMB models for R and I. In the best derived model, the most important variables contributing to the estimation of biomass were in the wavelength ranges of 546–910 nm (lablab), 750–794 nm (maize) and 686–814 nm (finger millet). The deviation of predicted and measured FMB did not differ much among the different levels of N and water supply. However, there was a trend of overestimation at the initial stage and underestimation at the later stages of crop growth. Full article