Climate change (CC) is undoubtedly induced and accelerated by human activity and can pose a serious threat to mankind by reducing food production. Significant weather aberrations in form of the uneven precipitation pattern, more frequent and intense occurrence of temperature fluctuations accompanied by changes in wind intensity and frequency, amount of clouds, intensity and quality of sunlight can be expected. Maybe the most vulnerable sector affected by CC is agriculture. So, it is important to mitigate and adapt to a new situation through different and most adaptable agricultural strategies. Accordingly, scientists, experts, politicians, decisionmakers, and others increasingly emphasize the need for further development of sustainable agricultural production, whose management will be compatible with different ecosystems (agroecosystem compliance with global ecosystems), while simultaneously restoring degraded agricultural land. One of the best solutions for sustainable agricultural production, under CC conditions, can be Conservation agriculture. Climate change is not only an abstraction, which is why one of the most important roles of conservation agriculture today is its ability to adapt and mitigate these changes. The basis of conservation agriculture production is in management set on three fundamental postulates, which contextually unify climate-soil-plant, while respecting agroecological and socioeconomic differences.

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Knowledge of the relationship between soil sampling density and spatial autocorrelation with interpolation accuracy allows more time- and cost-efficient spatial analysis. Previous studies produced contradictory observations regarding this relationship, and this study aims to determine and explore under which conditions the interpolation accuracy of chemical soil properties is affected. The study area covered 823.4 ha of agricultural land with 160 soil samples containing phosphorus pentoxide (P2O5) and potassium oxide (K2O) values. The original set was split into eight subsets using a geographically stratified random split method, interpolated using the ordinary kriging (OK) and inverse distance weighted (IDW) methods. OK and IDW achieved similar interpolation accuracy regardless of the soil chemical property and sampling density, contrary to the majority of previous studies which observed the superiority of kriging as a deterministic interpolation method. The primary dependence of interpolation accuracy to soil sampling density was observed, having R2 in the range of 56.5–83.4% for the interpolation accuracy assessment. While this study enables farmers to perform efficient soil sampling according to the desired level of detail, it could also prove useful to professions dependent on field sampling, such as biology, geology, and mining.

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