# Presentation: 197825

## Management Zones to Improve NUE and Protect Water Quality on Irrigated Systems

**Figure 3**. Correlation between soil nitrate-N ( a ); potassium ( b );  and fine soil particles (clay + silt).

- r2 = 0.38 ***
- y = 5.5x + 91

- r2 = 0.22 ***
- y = 1.1x + 6.7

**(b)**

- r2 = 0.41***
- y = 0.06x +  0.5

**(c)**

- r2 = 0.46***
- y = 1.3x + 7

**(a)**

**Figure 1.** Spatial distribution of percentage of soil fine particles (silt and clay) ( a ); Ec mSm-1 ( b ); phosphorous ug g-1 ( c ); soil nitrate-N ug g-1 ( d );  soil organic matter % ( e ); potassium ug g-1 ( f ); SPAD readings ( g); and potato yield (h).

**(c)**

**(f)**

**Management Zones to Improve NUE and Protect Water Quality on Irrigated Systems**

- Introduction

- Materials and Methods

-      During 1999-2000 we collected, using differential global position systems (DGPS), one sample at random within an acre, for two whole center pivot irrigation systems (120+ samples/center pivot). SPAD©† chlorophyll readings and other plant measurements were collected during the growing season. Yield monitoring was done for potato and small grains. The Veris model 3100©† sensor was used to measure soil electrical conductivity.  Other additional soil, plant, and climatological data  was also collected and soil texture was used to divide the field by zones.

-    Residual soil NO3--N and concentrations of other macro and micro nutrients were spatially correlated with soil electrical conductivity (P<0.05) (Figs. 1 & 2). Soil texture was spatially correlated with soil organic matter, EC, and macro and micro nutrients and soil NO3--N (P<0.05) (Figs 1& 3).   The finer textured areas had the greatest concentrations of nutrients, higher yield for potato and higher SPAD readings for barley. In general, variability was significantly lower for nutrients when the field was divided by zones, than the variability observed for the center pivot. This suggests that there is potential to divide this fields by zones and improve the accuracy of the recommended nutrients and potentially reduce the losses of nutrients from these fields.

- Results and Discussion

**(a)**

**(b)**

**(e)**

**(d)**

**(g)**

**(h)**

**Figure 2**. Correlation between fine soil particles ( a ); soil nitrate-N ( b ); soil organic matter % ( c ) and potassium ( d ) vs soil electrical conductivity (EC).

- Summary and Conclusions

-       Texture should be considered as a driving variable when establishing management zones, however, the interaction with other variables such as soil chemical and physical characteristics, yield and others need to be considered. These initial results agree with Fleming et al. which worked with corn (_*Zea mayz L.*_; 1999) and show that there is potential to use soil chemical and physical characteristics to manage nutrients by zones to improve nutrient management on a potato-malting barley rotation and protect water quality by better accounting for nutrients levels and residual soil NO3--N.

- † Names are necessary to report factually on available data; however, the USDA neither guarantees nor warrants the standard of the product, and the use of the name by the USDA implies no approval of the product to the exclusion of others that may be suitable.

- * Corresponding Author Email jdelgado@lamar.colostate.edu

-   Fine particles % (clay + silt)

-      Nitrogen, phosphorous and potassium use efficiencies are in general reported to be about 50, 10 and 40%. Losses of nutrients from the fields can potentially contribute to environmental impacts. Although farmers are implementing best management practices (BMPs) there is still field variability in center pivot irrigated circles that can contribute to lower nutrient use efficiency. Our objectives were to evaluate the potential to use precision farming for improvement of nutrient management under irrigated potato (_*Solanum tuberosum*__ L._) followed by malting barley (_*Hordeum vulgare*__ L._) for cropping systems of South Central Colorado.

- J.A. Delgado1*, G. Buchleiter2, A. Stuebe3, M. Dillon4, M. Shaffer5, A. Thompson-Johns4,  K. Thompson6 and R. Follett1
- 1USDA-ARS-Soil Plant Nutrient Research Unit,  Fort Collins, CO, 80522; 2 USDA-ARS-WMRU,  Fort Collins, CO, 80522; 3USDA-NRCS, San Luis, CO 81152; 4CSU-SLVRC,
- Center, CO 81125; 5USDA-ARS-GPSR, Fort Collins, CO 80522; and 6AGRO Eng., Alamosa, CO 81101

**(d)**

**(b)**

**(a)**

- r2=0.24***
- y=0.7x + 3.6

- r2=0.35***
- y=3.1x + 82.4