Mineral reactions in soils demonstrably take place on a human timescale. The weathering of silicate ‘rock-forming’ minerals releases nutrients that are essential for plant growth, including silica. This process consumes CO2, which is ultimately derived from the atmosphere, through enhanced rock weathering. From a human perspective, the weathering process has two beneficial functions – crop nutrition and climate mitigation – through the removal of atmospheric CO2. By considering these as a coupled process, the release of silica during weathering can be matched to what is taken from the soil by a crop (e.g. wheat). A simple analysis shows that the amount of silica that accumulates in wheat during a 4 month growing period is readily released by the weathering of pyroxene and plagioclase, minerals that commonly occur in basaltic igneous rocks. In contrast, the dissolution rate for quartz is so low that it cannot supply the silica taken up by the crop and is inert. Similarly, dissolution of clay minerals releases sufficient silica for plant uptake. Rapid weathering of silicate minerals within soils is evident from images of surfaces of grains exposed in soils for periods of 10–100 years. The evidence for silicate rock weathering as part of the soil system that sustains humanity is provided by the vegetation that we see around us.

Blessed milkthistle is considered to be a noxious weed in irrigated and rainfed areas of Pakistan due to its strong allelopathic effects on food crops. For sustainable wheat production, it is necessary to know the critical time for weed removal (CTWR) for blessed milkthistle to allow wheat growers to get maximum benefit from control of this weed. A field study was conducted in 2014 and 2015 at the College of Agriculture, University of Sargodha, Pakistan, to investigate the CTWR of blessed milkthistle in wheat. The field experiments were designed with seven treatments; weed free (control); 2, 3, 4, 5, and 6 wk after emergence (WAE); and weedy check. At 6 WAE, a significant reduction was noted in plant height (8% and 17%), number of productive tillers per square meter (16% and 16%), spike length (23% and 54%), grains per spike (13% and 34%), 1,000-grain weight (14% and 37%), grain yield (20% and 21%), and biological yield (24% and 50%) compared with control (weed-free plots) during 2014 and 2015, respectively. The logistic model supports the field study results and suggests that blessed milkthistle’s CTWR for wheat is 1 to 5 WAE based on acceptable yield losses of 5% to 15% during both years. The experimental results and logistic model indicate that blessed milkthistle should be controlled within 1 to 5 WAE to get better wheat crop harvests without compromising farmers’ profits. To our knowledge, this is the first study ever in Pakistan regarding the CTWR in terms of WAE of blessed milkthistle and could help other scientists create weed control strategies for other areas of the country.

Weed-detecting reflectance sensors were modified to allow selective interrogation of the near infrared–red ratio to estimate differences in plant biomass. Sampling was programmed to correspond to the forward movement of the field of view of the sensors. There was a linear relationship (r 2 > 0.80) between actual biomass and crop canopy analyzer (CCA) values up to 2,000 kg/ha for winter wheat sequentially thinned to create different amounts of biomass and up to 1,000 kg/ha for spring wheat sampled at different stages of development. At higher amounts of biomass the sensors underestimated the actual biomass. A linear relationship (r 2 = 0.73) was obtained with the CCA for the biomass of 76 chickpea cultivars at 500 growing degree days (GDD500). The reflectance sensors were used to determine differences in the herbicide response of soybean cultivars sprayed with increasing rates of herbicides. The CCA data resulted in better dose–response relationships than did biomass data for bromoxynil at 0.8 kg ai/ha and glyphosate at 1.35 kg ai/ha. There was no phytotoxicity to soybean with imazethapyr at 1.44 kg ai/ha. The method offers a quick and nondestructive means to measure differences in early-season crop growth. It also has potential in selecting crop cultivars with greater seedling vigor, as an indicator of crop nutrient status, in plant disease assessment, in determining crop cultivar responses to increasing herbicide dose rates, in weed mapping, and in studying temporal changes in crop or weed biomass.

The long-term productivity, sustainability, and environmental impact of cropping systems cannot be assessed adequately using conventional agronomic experiments. This pap erdiscusses the use of computer-based technologies as support tools for this type of assessment, including crop growth simulation models, weather generators, geographical information systems, and risk assessment and economic models. Comprehensive systems that integrate these technologies are just emerging, offering great potential for the analysis of agricultural systems in the future.