Recycling potassium from cow manure compost can replace potassium fertilizers in paddy rice production systems

The prevalence of K deficiency and negative K balance in rice production increases the demand for K fertilizer. However, the primary source of K fertilizer, potash rock, is limited. Recycling K from cow manure compost (CMC) is a sustainable solution. Nevertheless, the effects of substituting K fertilizer with CMC on rice yield, soil K fertility, and partial K balance (PKB) are not well understood. Therefore, a field experiment with four treatments (control - unfertilized, MNP K - CMC plus NPK fertilizer, MNP ½ K - CMC plus NP and 50 % K fertilizer, and MNP - CMC plus NP fertilizer) was conducted from 2020 to 2022 to study the effects of replacing K fertilizer with K from CMC on rice growth, yield, plant K uptake, soil K fertility, and PKB. The results indicated that K input from CMC exceeded the recommended K fertilizer level, sufficient for optimal rice growth and yield over three growing seasons and plant K uptake in the last two seasons. Plant K uptake increased with total K input and reached a plateau when total K input approached the maximum plant K uptake. In the MNP treatment, PKB was negative in the first two seasons but became positive in the last season, owing to the equivalence between K input from CMC and plant K uptake. Key factors influencing PKB in this treatment were K input from CMC and plant K uptake. Increasing the CMC application rate during the first two seasons could lead to a positive PKB. In this treatment, soil exchangeable K changed correspondingly with PKB, decreasing in the first two seasons but increasing in the last season. Overall, determining the appropriate amount of CMC application for a positive PKB is vital for the sustainability of substituting K fertilizer with K from CMC in paddy rice systems.

Evaluating the effect of remote sensing image spatial resolution on soil exchangeable potassium prediction models in smallholder farm settings

Major end users of Digital Soil Mapping (DSM) such as policy makers and agricultural extension workers are faced with choosing the appropriate remote sensing data. The objective of this research is to analyze the spatial resolution effects of different remote sensing images on soil prediction models in two smallholder farms in Southern India called Kothapally (Telangana State), and Masuti (Karnataka State), and provide empirical guidelines to choose the appropriate remote sensing images in DSM. Bayesian kriging (BK) was utilized to characterize the spatial pattern of exchangeable potassium (K_ex) in the topsoil (0-15 cm) at different spatial resolutions by incorporating spectral indices from Landsat 8 (30 m), RapidEye (5 m), and WorldView-2/GeoEye-1/Pleiades-1A images (2 m). Some spectral indices such as band reflectances, band ratios, Crust Index and Atmospherically Resistant Vegetation Index from multiple images showed relatively strong correlations with soil K_ex in two study areas. The research also suggested that fine spatial resolution WorldView-2/GeoEye-1/Pleiades-1A-based and RapidEye-based soil prediction models would not necessarily have higher prediction performance than coarse spatial resolution Landsat 8-based soil prediction models. The end users of DSM in smallholder farm settings need select the appropriate spectral indices and consider different factors such as the spatial resolution, band width, spectral resolution, temporal frequency, cost, and processing time of different remote sensing images. Overall, remote sensing-based Digital Soil Mapping has potential to be promoted to smallholder farm settings all over the world and help smallholder farmers implement sustainable and field-specific soil nutrient management scheme.