The effect of duration of conservation agriculture adoption on soil carbon dynamics and system sustainability was evaluated on farms of 30 villages in the Nilokheri block of Karnal district, Haryana, India. Sustainability was evaluated, in which a number of soil physical, chemical, and biological parameters were measured and a Sustainability Index (SI) was applied. Soil samples were collected from existing conservation agriculture (CA) and conventional tillage (CT) farms. Villages under CA practices were subdivided as CA3, CA6, and CA9 based on the number of years of CA practice adoption. Results showed that bulk density (BD) of 0-15 cm soil depth was 7% greater in CA3 plots, whereas in CA6 and CA9 plots BD values were only 2% and 3% higher than CT. Soil organic carbon (SOC) in 0-15 cm soil depth was found to be greater by 16.32% in CA3 than CT plots, whereas SOC was higher by 38.77% and 61.22% in CA6 and CA9. In CA, for the 0-15 and 15-30 cm soil depths, labile pools were 36% and 22% greater than CT, respectively. For both the soil depths in CA, the recalcitrant pool was 12% and 9% more than CT, respectively. Microbial biomass carbon (MBC) values of the 0-15 cm soil depth were increased over CT by 18.57%, 47.08%, and 71.5% for CA3, CA6, and CA9 respectively. In CA plots, the SI of 0-15 cm soil depth ranged between cumulative ratings (CR) of 18-21, which indicates that CA practice is "sustainable" for both soil depths. For CT, CR ranged from 25 to 30 for both soil depths resulting in a SI of "sustainability with high input". Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) scores showed that SOC had the maximum weight (0.96) towards sustainability, giving it a rank of 1. Effective rooting depth (ERD), BD, texture, and wilting point (WP) ranked 2, 3, 4 and 5, respectively, indicating their corresponding weight of contribution towards the SI. Farmers in the Karnal district should be encouraged to adopt CA practices as they can increase SOC and move the systems from "sustainable with high input" to "sustainable".
For sustainable crop production and maintenance of soil health, conservation agriculture (CA) practices provides an opportunity for improving soil structure and physical health, nutrient and water use efficiency, soil organic carbon and mitigation of greenhouse gases emission from agriculture. CA is primarily based on four crop management practices such as minimum soil disturbance or no-tillage; permanent or semi-permanent retention of crop residue; crop rotation and control traffic. Different CA management practices affect crop yield as well as soil properties. CA makes necessary modifications in different soil hydro-physical properties, viz. increase in soil water infiltration, reduction in water runoff and soil loss, and reduction in evaporation loss. No tillage (NT), residue retention and crop rotation combined effect the soil organic carbon concentration. Different crop rotations and residue retentions and crops with different rooting depths used in CA practices have proved to reduce the compaction constraints.CA can help to mitigate GHG emissions, viz methane (CH4) and nitrous oxide (N2O) from agriculture by improving soil C sequestration, enhancing soil quality, nitrogen and water use efficiencies, and decreasing fuel consumption. But effect of CA and conventional agricultural practices of porosity and pore size distribution is very much limited. When CA is practiced for six to ten years there is improvement in soil structure, porosity and pore size distribution, macro-micro faunal activity, and organic matter content..The soil under ZT has the lowest porosity as compared to conventional management practices. The highest porosity and the maximum connected pores are frequently seen in conventionally tilled soil. In this paper, an attempt has been made to review the variation of porosity and pore size distribution and other soil physical properties under conservation agricultural practices.
Even though research shows that aggregate stability and mean weight diameter (MWD) are critical components of soil health, it is not routinely measured. An alternative approach to the physical measurement is to calculate these values based on routinely measured soil parameters. Therefore, the objective was to compare two artificial intelligence (AI)-based machine learning approaches, that is, support vector machine (SVM) and artificial neural network (ANN) models in prediction of soil wet aggregate stability (quantified by MWD). Soil samples (120) from the Indo-Gangetic Alluvium major soil group, that are characterized as Ustifluvents were used in the study. These samples were analyzed for sand, silt, clay, bulk density (BD), organic carbon (OC), and MWD. The correlation coefficient (r) was highest in case of SVM model with a percentage increase of 16.92 and 2.70 when compared with MLR and ANN respectively. The SVM and ANN models showed 6.36 and 2.12% decrease in RMSE in training dataset while a 14.28% decrease was found for SVM in testing dataset when compared to the multi-linear regression (MLR) model. Results showed that ANN with two neurons (building blocks of ANN) in hidden layer had better performance in predicting MWD than MLR, whereas the radial basis Kernel function based SVM was found to be best for training and testing data of MWD. Soil texture, OC, and BD can be used to predict soil structural stability effectively using SVM. However, additional work is needed to confirm these findings with other soils.
Climate change is real and inevitable, incessantly threatening the terrestrial ecosystem and global food security. Although the impacts of climate change on crop yield and the environment have received much attention in recent years, there are few studies on its implications for the production of high-quality seeds that provide the basic input for food production. Seeds are the primary planting material for crop cultivation and carry most new agricultural technologies to the field. Climatic abnormalities occurring at harvest and during the post-harvest stages may not always severely impact seed yield but can reduce the morphological, physiological and biochemical quality, ultimately reducing the field performance and planting value of the seed lot. In our preliminary data mining that considered the first 30 species appearing in the search results, seed setting, seed yield and seed quality parameters under temperature, CO2 and drought stresses showed differential response patterns depending on the cotyledon number (monocots vs. dicots), breeding system (self- vs. cross-pollinated), life cycle (annual vs. perennial) and maturity time (seed setting in cooler vs. hotter months). The relative proportions of the 30 species showed that germination and seedling vigour are adversely affected more in dicots and self-pollinated annual species that set seeds in hotter months. Together, these impacts can potentially reduce the quantity and quality of seeds produced. Immediate attention and action are required to understand and mitigate the detrimental impacts of climate change on the production and supply of high-quality seeds. This review summarises the current knowledge on this aspect, predicts the future implications and suggests some potential mitigation strategies in the context of projected population growth, climate change and seed requirement at the global level.
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